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Neuron Models

Artificial Neurons#

IntegratorNeuron #

Bases: Neuron

TEMPLATE

Integrator Neuron for stop_condition in spiking models.

The variable g_ampa increases for incoming spikes (target ampa) and decreases exponentially with time constant tau. If g_ampa reaches a threshold, the neuron's variable decision, which is by default -1, changes to the neuron_id. This can be used to cause the stop_condition of ANNarchy's simulate_until() function (stop_codnition="decision>=0 : any"). In case of multiple integrator neurons, the neuron_id can be used to identify the neuron that reached the threshold.

Warning

You have to define the variable neuron_id for each neuron in the Integrator population.

Parameters:

Name Type Description Default
tau float

Time constant in ms of the neuron. Default: 1.

 1
threshold float

Threshold for the decision g_ampa has to reach. Default: 1.

 1

Examples:

from ANNarchy import Population, simulate_until
from CompNeuroPy.neuron_models import Integrator

# Create a population of 10 integrator neurons
integrator_neurons = Population(
    geometry=10,
    neuron=IntegratorNeuron(tau=1, threshold=1),
    stop_condition="decision>=0 : any",
    name="integrator_neurons",)

# set the neuron_id for each neuron
integrator_neurons.neuron_id = range(10)

# simulate until one neuron reaches the threshold
simulate_until(max_duration=1000, population=integrator_neurons)

# check if simulation stop due to stop_codnition and which neuron reached the
# threshold
if (integrator_neurons.decision >= 0).any():
    neurons_reached_thresh = integrator_neurons.neuron_id[
        integrator_neurons.decision >= 0
    ]
    print(f"Neuron(s) {neurons_reached_thresh} reached threshold.")
else:
    print("No neuron reached threshold.")
Variables to record
  • g_ampa
  • decision
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/artificial_nm.py 
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class IntegratorNeuron(Neuron):
    """
    TEMPLATE

    Integrator Neuron for stop_condition in spiking models.

    The variable g_ampa increases for incoming spikes (target ampa) and decreases
    exponentially with time constant tau. If g_ampa reaches a threshold, the neuron's
    variable decision, which is by default -1, changes to the neuron_id. This can be
    used to cause the stop_condition of ANNarchy's simulate_until() function
    (stop_codnition="decision>=0 : any"). In case of multiple integrator neurons,
    the neuron_id can be used to identify the neuron that reached the threshold.

    !!! warning
        You have to define the variable neuron_id for each neuron in the Integrator
        population.

    Parameters:
        tau (float, optional):
            Time constant in ms of the neuron. Default: 1.
        threshold (float, optional):
            Threshold for the decision g_ampa has to reach. Default: 1.

    Examples:
        ```python
        from ANNarchy import Population, simulate_until
        from CompNeuroPy.neuron_models import Integrator

        # Create a population of 10 integrator neurons
        integrator_neurons = Population(
            geometry=10,
            neuron=IntegratorNeuron(tau=1, threshold=1),
            stop_condition="decision>=0 : any",
            name="integrator_neurons",)

        # set the neuron_id for each neuron
        integrator_neurons.neuron_id = range(10)

        # simulate until one neuron reaches the threshold
        simulate_until(max_duration=1000, population=integrator_neurons)

        # check if simulation stop due to stop_codnition and which neuron reached the
        # threshold
        if (integrator_neurons.decision >= 0).any():
            neurons_reached_thresh = integrator_neurons.neuron_id[
                integrator_neurons.decision >= 0
            ]
            print(f"Neuron(s) {neurons_reached_thresh} reached threshold.")
        else:
            print("No neuron reached threshold.")
        ```

    Variables to record:
        - g_ampa
        - decision
        - r
    """

    # For reporting
    _instantiated = []

    def __init__(self, tau: float = 1, threshold: float = 1):
        # Create the arguments
        parameters = f"""
            tau = {tau} : population
            threshold = {threshold} : population
            neuron_id = 0
        """

        super().__init__(
            parameters=parameters,
            equations="""
                dg_ampa/dt = - g_ampa / tau
                ddecision/dt = 0 : init = -1
            """,
            spike="""
                g_ampa >= threshold
            """,
            reset="""
                decision = neuron_id
            """,
            name="integrator_neuron",
            description="""
                Integrator Neuron, which integrates incoming spikes with value g_ampa
                and emits a spike when reaching a threshold. After spike decision
                changes, which can be used as for stop condition""",
        )

        # For reporting
        self._instantiated.append(True)

IntegratorNeuronSimple #

Bases: Neuron

TEMPLATE

Integrator Neuron for stop_condition in spiking models.

The variable g_ampa increases for incoming spikes (target ampa) and decreases exponentially with time constant tau. You can check g_ampa and use it for the stop_condition of ANNarchy's simulate_until() function (stop_codnition="g_ampa>=some_value : any"). In case of multiple integrator neurons, the neuron_id can be used to identify the neuron that reached the threshold.

Warning

You have to define the variable neuron_id for each neuron in the Integrator population.

Parameters:

Name Type Description Default
tau float

Time constant in ms of the neuron. Default: 1.

 1

Examples:

from ANNarchy import Population, simulate_until
from CompNeuroPy.neuron_models import Integrator

# Create a population of 10 integrator neurons
integrator_neurons = Population(
    geometry=10,
    neuron=IntegratorNeuronSimple(tau=1),
    stop_condition="g_ampa>=5 : any",
    name="integrator_neurons",)

# set the neuron_id for each neuron
integrator_neurons.neuron_id = range(10)

# simulate until one neuron reaches the threshold
simulate_until(max_duration=1000, population=integrator_neurons)

# check if simulation stop due to stop_codnition and which neuron reached the
# threshold
if (integrator_neurons.g_ampa >= 5).any():
    neurons_reached_thresh = integrator_neurons.neuron_id[
        integrator_neurons.g_ampa >= 5
    ]
    print(f"Neuron(s) {neurons_reached_thresh} reached threshold.")
else:
    print("No neuron reached threshold.")
Variables to record
  • g_ampa
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/artificial_nm.py 
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class IntegratorNeuronSimple(Neuron):
    """
    TEMPLATE

    Integrator Neuron for stop_condition in spiking models.

    The variable g_ampa increases for incoming spikes (target ampa) and decreases
    exponentially with time constant tau. You can check g_ampa and use it for the
    stop_condition of ANNarchy's simulate_until() function
    (stop_codnition="g_ampa>=some_value : any"). In case of multiple integrator neurons,
    the neuron_id can be used to identify the neuron that reached the threshold.

    !!! warning
        You have to define the variable neuron_id for each neuron in the Integrator
        population.

    Parameters:
        tau (float, optional):
            Time constant in ms of the neuron. Default: 1.

    Examples:
        ```python
        from ANNarchy import Population, simulate_until
        from CompNeuroPy.neuron_models import Integrator

        # Create a population of 10 integrator neurons
        integrator_neurons = Population(
            geometry=10,
            neuron=IntegratorNeuronSimple(tau=1),
            stop_condition="g_ampa>=5 : any",
            name="integrator_neurons",)

        # set the neuron_id for each neuron
        integrator_neurons.neuron_id = range(10)

        # simulate until one neuron reaches the threshold
        simulate_until(max_duration=1000, population=integrator_neurons)

        # check if simulation stop due to stop_codnition and which neuron reached the
        # threshold
        if (integrator_neurons.g_ampa >= 5).any():
            neurons_reached_thresh = integrator_neurons.neuron_id[
                integrator_neurons.g_ampa >= 5
            ]
            print(f"Neuron(s) {neurons_reached_thresh} reached threshold.")
        else:
            print("No neuron reached threshold.")
        ```

    Variables to record:
        - g_ampa
        - r
    """

    # For reporting
    _instantiated = []

    def __init__(self, tau: float = 1):
        # Create the arguments
        parameters = f"""
            tau = {tau} : population
            neuron_id = 0
        """

        super().__init__(
            parameters=parameters,
            equations="""
                dg_ampa/dt = - g_ampa / tau
                r = 0
            """,
            name="integrator_neuron_simple",
            description="""
                Integrator Neuron, which integrates incoming spikes with value g_ampa,
                which can be used as a stop condition
            """,
        )

        # For reporting
        self._instantiated.append(True)

PoissonNeuron #

Bases: Neuron

TEMPLATE

Poisson neuron whose rate can be specified and is reached instantaneous. The neuron emits spikes following a Poisson distribution, the average firing rate is given by the parameter rates.

Parameters:

Name Type Description Default
rates float

The average firing rate of the neuron in Hz. Default: 0.

 0
Variables to record
  • p
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/artificial_nm.py 
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class PoissonNeuron(Neuron):
    """
    TEMPLATE

    Poisson neuron whose rate can be specified and is reached instantaneous. The
    neuron emits spikes following a Poisson distribution, the average firing rate
    is given by the parameter rates.

    Parameters:
        rates (float, optional):
            The average firing rate of the neuron in Hz. Default: 0.

    Variables to record:
        - p
        - r
    """

    # For reporting
    _instantiated = []

    def __init__(self, rates: float = 0):
        # Create the arguments
        parameters = f"""
            rates = {rates}
        """

        super().__init__(
            parameters=parameters,
            equations="""
                p = Uniform(0.0, 1.0) * 1000.0 / dt
            """,
            spike="""
                p <= rates
            """,
            reset="""
                p = 0.0
            """,
            name="poisson_neuron",
            description="""
                Poisson neuron whose rate can be specified and is reached instantaneous.
            """,
        )

        # For reporting
        self._instantiated.append(True)

PoissonNeuronUpDown #

Bases: Neuron

TEMPLATE

The neuron emits spikes following a Poisson distribution, the average firing rate is given by the parameter rates and is reached with time constants tau_up and tau_down.

Attributes:

Name Type Description
rates float

The average firing rate of the neuron in Hz. Default: 0.
tau_up float

Time constant in ms for increasing the firing rate. Default: 1.
tau_down float

Time constant in ms for decreasing the firing rate. Default: 1.
Source code in src/CompNeuroPy/neuron_models/final_models/artificial_nm.py 
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class PoissonNeuronUpDown(Neuron):
    """
    TEMPLATE

    The neuron emits spikes following a Poisson distribution, the average firing rate is
    given by the parameter rates and is reached with time constants tau_up and tau_down.

    Attributes:
        rates (float, optional):
            The average firing rate of the neuron in Hz. Default: 0.
        tau_up (float, optional):
            Time constant in ms for increasing the firing rate. Default: 1.
        tau_down (float, optional):
            Time constant in ms for decreasing the firing rate. Default: 1.
    """

    # For reporting
    _instantiated = []

    def __init__(self, rates: float = 0, tau_up: float = 1, tau_down: float = 1):
        # Create the arguments
        parameters = f"""
            rates = {rates}
            tau_up = {tau_up}
            tau_down = {tau_down}
        """

        super().__init__(
            parameters=parameters,
            equations="""
                p = Uniform(0.0, 1.0) * 1000.0 / dt
                dact/dt = if (rates - act) > 0:
                              (rates - act) / tau_up
                          else:
                              (rates - act) / tau_down
            """,
            spike="""
                p <= act
            """,
            reset="""
                p = 0.0
            """,
            name="poisson_neuron_up_down",
            description="""Poisson neuron whose rate can be specified and is reached
                with time constants tau_up and tau_down.
            """,
        )

        # For reporting
        self._instantiated.append(True)

PoissonNeuronSin #

Bases: Neuron

TEMPLATE

Neuron emitting spikes following a Poisson distribution, the average firing rate is given by a sinus function.

Parameters:

Name Type Description Default
amplitude float

Amplitude of the sinus function. Default: 0.

 0
base float

Base (offset) of the sinus function. Default: 0.

 0
frequency float

Frequency of the sinus function. Default: 0.

 0
phase float

Phase of the sinus function. Default: 0.

 0
Variables to record
  • rates
  • p
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/artificial_nm.py 
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class PoissonNeuronSin(Neuron):
    """
    TEMPLATE

    Neuron emitting spikes following a Poisson distribution, the average firing rate
    is given by a sinus function.

    Parameters:
        amplitude (float, optional):
            Amplitude of the sinus function. Default: 0.
        base (float, optional):
            Base (offset) of the sinus function. Default: 0.
        frequency (float, optional):
            Frequency of the sinus function. Default: 0.
        phase (float, optional):
            Phase of the sinus function. Default: 0.

    Variables to record:
        - rates
        - p
        - r
    """

    # For reporting
    _instantiated = []

    def __init__(
        self,
        amplitude: float = 0,
        base: float = 0,
        frequency: float = 0,
        phase: float = 0,
    ):
        # Create the arguments
        parameters = f"""
            amplitude = {amplitude}
            base = {base}
            frequency = {frequency}
            phase = {phase}
        """

        super().__init__(
            parameters=parameters,
            equations="""
                rates = amplitude * sin((2*pi*frequency)*(t/1000-phase)) + base
                p     = Uniform(0.0, 1.0) * 1000.0 / dt
            """,
            spike="""
                p <= rates
            """,
            reset="""
                p = 0.0
            """,
            name="poisson_neuron_sin",
            description="Poisson neuron whose rate varies with a sinus function.",
        )

        # For reporting
        self._instantiated.append(True)

Hodgkin Huxley Neurons#

HHneuronBischop #

Bases: _HHneuron

PREDEFINED

Hodgkin Huxley neuron model for striatal FSI from Bischop et al. (2012).

Variables to record
  • prev_v
  • I_L
  • alpha_h
  • beta_h
  • h_inf
  • tau_h
  • h
  • alpha_m
  • beta_m
  • m_inf
  • m
  • I_Na
  • alpha_n1
  • beta_n1
  • n1_inf
  • tau_n1
  • n1
  • I_Kv1
  • alpha_n3
  • beta_n3
  • n3_inf
  • tau_n3
  • n3
  • I_Kv3
  • PV
  • PV_Mg
  • dPV_Ca_dt
  • PV_Ca
  • Ca
  • k_inf
  • tau_k
  • k
  • I_SK
  • a_inf
  • a
  • I_Ca
  • v
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/H_and_H_like_nm.py 
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class HHneuronBischop(_HHneuron):
    """
    PREDEFINED

    Hodgkin Huxley neuron model for striatal FSI from
    [Bischop et al. (2012)](https://doi.org/10.3389/fnmol.2012.00078).

    Variables to record:
        - prev_v
        - I_L
        - alpha_h
        - beta_h
        - h_inf
        - tau_h
        - h
        - alpha_m
        - beta_m
        - m_inf
        - m
        - I_Na
        - alpha_n1
        - beta_n1
        - n1_inf
        - tau_n1
        - n1
        - I_Kv1
        - alpha_n3
        - beta_n3
        - n3_inf
        - tau_n3
        - n3
        - I_Kv3
        - PV
        - PV_Mg
        - dPV_Ca_dt
        - PV_Ca
        - Ca
        - k_inf
        - tau_k
        - k
        - I_SK
        - a_inf
        - a
        - I_Ca
        - v
        - r
    """

    def __init__(self):
        self.bischop = _BischopStrings()

        super().__init__()

    def _get_parameters(self):
        return self.bischop.parameters_base

    def _get_equations(self):
        return self.bischop.equations_base + self.bischop.membrane_base

    def _get_name(self):
        return "H_and_H_Bischop"

    def _get_description(self):
        return (
            "Hodgkin Huxley neuron model for striatal FSI from Bischop et al. (2012)."
        )

HHneuronBischopSyn #

Bases: _HHneuron

PREDEFINED

Hodgkin Huxley neuron model for striatal FSI from Bischop et al. (2012) with conductance-based synapses/currents for AMPA and GABA.

Variables to record
  • g_ampa
  • g_gaba
  • prev_v
  • I_L
  • alpha_h
  • beta_h
  • h_inf
  • tau_h
  • h
  • alpha_m
  • beta_m
  • m_inf
  • m
  • I_Na
  • alpha_n1
  • beta_n1
  • n1_inf
  • tau_n1
  • n1
  • I_Kv1
  • alpha_n3
  • beta_n3
  • n3_inf
  • tau_n3
  • n3
  • I_Kv3
  • PV
  • PV_Mg
  • dPV_Ca_dt
  • PV_Ca
  • Ca
  • k_inf
  • tau_k
  • k
  • I_SK
  • a_inf
  • a
  • I_Ca
  • v
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/H_and_H_like_nm.py 
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class HHneuronBischopSyn(_HHneuron):
    """
    PREDEFINED

    Hodgkin Huxley neuron model for striatal FSI from
    [Bischop et al. (2012)](https://doi.org/10.3389/fnmol.2012.00078) with
    conductance-based synapses/currents for AMPA and GABA.

    Variables to record:
        - g_ampa
        - g_gaba
        - prev_v
        - I_L
        - alpha_h
        - beta_h
        - h_inf
        - tau_h
        - h
        - alpha_m
        - beta_m
        - m_inf
        - m
        - I_Na
        - alpha_n1
        - beta_n1
        - n1_inf
        - tau_n1
        - n1
        - I_Kv1
        - alpha_n3
        - beta_n3
        - n3_inf
        - tau_n3
        - n3
        - I_Kv3
        - PV
        - PV_Mg
        - dPV_Ca_dt
        - PV_Ca
        - Ca
        - k_inf
        - tau_k
        - k
        - I_SK
        - a_inf
        - a
        - I_Ca
        - v
        - r
    """

    def __init__(self):
        self.bischop = _BischopStrings()

        super().__init__()

    def _get_parameters(self):
        return self.bischop.parameters_conductance

    def _get_equations(self):
        return self.bischop.equations_conductance + self.bischop.membrane_conductance

    def _get_name(self):
        return "H_and_H_Bischop_syn"

    def _get_description(self):
        return """
                Hodgkin Huxley neuron model for striatal FSI from Bischop et al. (2012)
                with conductance-based synapses/currents for AMPA and GABA.
            """

HHneuronCorbit #

Bases: _HHneuron

PREDEFINED

Hodgkin Huxley neuron model for striatal FSI from Corbit et al. (2016).

Variables to record
  • prev_v
  • I_L
  • m_Na
  • h_Na
  • I_Na
  • n_Kv3_inf
  • tau_n_Kv3_inf
  • n_Kv3
  • I_Kv3
  • m_Kv1
  • h_Kv1
  • I_Kv1
  • v
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/H_and_H_like_nm.py 
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class HHneuronCorbit(_HHneuron):
    """
    PREDEFINED

    Hodgkin Huxley neuron model for striatal FSI from
    [Corbit et al. (2016)](https://doi.org/10.1523/JNEUROSCI.0339-16.2016).

    Variables to record:
        - prev_v
        - I_L
        - m_Na
        - h_Na
        - I_Na
        - n_Kv3_inf
        - tau_n_Kv3_inf
        - n_Kv3
        - I_Kv3
        - m_Kv1
        - h_Kv1
        - I_Kv1
        - v
        - r
    """

    def __init__(self):
        self.corbit = _CorbitStrings()

        super().__init__()

    def _get_parameters(self):
        return self.corbit.parameters_base

    def _get_equations(self):
        return self.corbit.equations_base + self.corbit.membrane_base

    def _get_name(self):
        return "H_and_H_Corbit"

    def _get_description(self):
        return "Hodgkin Huxley neuron model for striatal FSI from Corbit et al. (2016)."

HHneuronCorbitSyn #

Bases: _HHneuron

PREDEFINED

Hodgkin Huxley neuron model for striatal FSI from Corbit et al. (2016) with conductance-based synapses/currents for AMPA and GABA.

Variables to record
  • g_ampa
  • g_gaba
  • prev_v
  • I_L
  • m_Na
  • h_Na
  • I_Na
  • n_Kv3_inf
  • tau_n_Kv3_inf
  • n_Kv3
  • I_Kv3
  • m_Kv1
  • h_Kv1
  • I_Kv1
  • v
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/H_and_H_like_nm.py 
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class HHneuronCorbitSyn(_HHneuron):
    """
    PREDEFINED

    Hodgkin Huxley neuron model for striatal FSI from
    [Corbit et al. (2016)](https://doi.org/10.1523/JNEUROSCI.0339-16.2016) with
    conductance-based synapses/currents for AMPA and GABA.

    Variables to record:
        - g_ampa
        - g_gaba
        - prev_v
        - I_L
        - m_Na
        - h_Na
        - I_Na
        - n_Kv3_inf
        - tau_n_Kv3_inf
        - n_Kv3
        - I_Kv3
        - m_Kv1
        - h_Kv1
        - I_Kv1
        - v
        - r
    """

    def __init__(self):
        self.corbit = _CorbitStrings()

        super().__init__()

    def _get_parameters(self):
        return self.corbit.parameters_conductance

    def _get_equations(self):
        return self.corbit.equations_conductance + self.corbit.membrane_conductance

    def _get_name(self):
        return "H_and_H_Corbit_syn"

    def _get_description(self):
        return """
                Hodgkin Huxley neuron model for striatal FSI from Corbit et al. (2016)
                with conductance-based synapses/currents for AMPA and GABA.
            """

HHneuronCorbitVoltageClamp #

Bases: _HHneuron

PREDEFINED

Hodgkin Huxley neuron model for striatal FSI from Corbit et al. (2016) with voltage clamp. Membrane potential v is clamped and I_inf can be recorded.

Variables to record
  • prev_v
  • I_L
  • m_Na
  • h_Na
  • I_Na
  • n_Kv3_inf
  • tau_n_Kv3_inf
  • n_Kv3
  • I_Kv3
  • m_Kv1
  • h_Kv1
  • I_Kv1
  • v
  • I_inf
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/H_and_H_like_nm.py 
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class HHneuronCorbitVoltageClamp(_HHneuron):
    """
    PREDEFINED

    Hodgkin Huxley neuron model for striatal FSI from
    [Corbit et al. (2016)](https://doi.org/10.1523/JNEUROSCI.0339-16.2016) with
    voltage clamp. Membrane potential v is clamped and I_inf can be recorded.

    Variables to record:
        - prev_v
        - I_L
        - m_Na
        - h_Na
        - I_Na
        - n_Kv3_inf
        - tau_n_Kv3_inf
        - n_Kv3
        - I_Kv3
        - m_Kv1
        - h_Kv1
        - I_Kv1
        - v
        - I_inf
        - r
    """

    def __init__(self):
        self.corbit = _CorbitStrings()

        super().__init__()

    def _get_parameters(self):
        return self.corbit.parameters_base

    def _get_equations(self):
        return self.corbit.equations_base + self.corbit.membrane_voltage_clamp

    def _get_name(self):
        return "H_and_H_Corbit_voltage_clamp"

    def _get_description(self):
        return """
                Hodgkin Huxley neuron model for striatal FSI from Corbit et al. (2016)
                with voltage clamp.
            """

Izhikevich (2003)-like Neurons#

Izhikevich2003FixedNoisyAmpa #

Bases: Neuron

TEMPLATE

Izhikevich (2003)-like neuron model with additional conductance based synapses for AMPA and GABA currents with noise in AMPA conductance. Fixed means, the 3 factors of the quadratic equation cannot be changed.

Parameters:

Name Type Description Default
a float

Time constant of the recovery variable u.

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b float

Sensitivity of the recovery variable u to the membrane potential v.

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c float

After-spike reset value of the membrane potential v.

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d float

After-spike change of the recovery variable u.

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tau_ampa float

Time constant of the AMPA conductance.

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tau_gaba float

Time constant of the GABA conductance.

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E_ampa float

Reversal potential of the AMPA conductance.

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E_gaba float

Reversal potential of the GABA conductance.

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I_app float

External applied current.

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increase_noise float

Increase of the Poisson distributed (equivalent to a Poisson distributed spike train as input) noise in the AMPA conductance.

 0
rates_noise float

Rate of the Poisson distributed noise in the AMPA conductance.

 0
Variables to record
  • g_ampa
  • g_gaba
  • v
  • u
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/izhikevich_2003_like_nm.py 
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class Izhikevich2003FixedNoisyAmpa(Neuron):
    """
    TEMPLATE

    [Izhikevich (2003)](https://doi.org/10.1109/TNN.2003.820440)-like neuron model with
    additional conductance based synapses for AMPA and GABA currents with noise in AMPA
    conductance. Fixed means, the 3 factors of the quadratic equation cannot be changed.

    Parameters:
        a (float, optional):
            Time constant of the recovery variable u.
        b (float, optional):
            Sensitivity of the recovery variable u to the membrane potential v.
        c (float, optional):
            After-spike reset value of the membrane potential v.
        d (float, optional):
            After-spike change of the recovery variable u.
        tau_ampa (float, optional):
            Time constant of the AMPA conductance.
        tau_gaba (float, optional):
            Time constant of the GABA conductance.
        E_ampa (float, optional):
            Reversal potential of the AMPA conductance.
        E_gaba (float, optional):
            Reversal potential of the GABA conductance.
        I_app (float, optional):
            External applied current.
        increase_noise (float, optional):
            Increase of the Poisson distributed (equivalent to a Poisson distributed
            spike train as input) noise in the AMPA conductance.
        rates_noise (float, optional):
            Rate of the Poisson distributed noise in the AMPA conductance.

    Variables to record:
        - g_ampa
        - g_gaba
        - v
        - u
        - r
    """

    # For reporting
    _instantiated = []

    def __init__(
        self,
        a: float = 0,
        b: float = 0,
        c: float = 0,
        d: float = 0,
        tau_ampa: float = 1,
        tau_gaba: float = 1,
        E_ampa: float = 0,
        E_gaba: float = 0,
        I_app: float = 0,
        increase_noise: float = 0,
        rates_noise: float = 0,
    ):
        # Create the arguments
        parameters = f"""
            a              = {a} : population
            b              = {b} : population
            c              = {c} : population
            d              = {d} : population
            tau_ampa       = {tau_ampa} : population
            tau_gaba       = {tau_gaba} : population
            E_ampa         = {E_ampa} : population
            E_gaba         = {E_gaba} : population
            I_app          = {I_app}
            increase_noise = {increase_noise} : population
            rates_noise    = {rates_noise}
        """

        super().__init__(
            parameters=parameters,
            equations="""
                dg_ampa/dt = ite(Uniform(0.0, 1.0) * 1000.0 / dt > rates_noise, -g_ampa/tau_ampa, -g_ampa/tau_ampa + increase_noise/dt)
                dg_gaba/dt = -g_gaba / tau_gaba
                dv/dt      = 0.04 * v * v + 5 * v + 140 - u + I_app - neg(g_ampa*(v - E_ampa)) - pos(g_gaba*(v - E_gaba))
                du/dt      = a * (b * v - u)
            """,
            spike="""
                v >= 30
            """,
            reset="""
                v = c
                u = u + d
            """,
            name="Izhikevich2003_fixed_noisy_AMPA",
            description="""
                Standard neuron model from Izhikevich (2003) with additional
                conductance-based synapses for AMPA and GABA currents with noise in AMPA
                conductance.
            """,
        )

        # For reporting
        self._instantiated.append(True)

Izhikevich2003NoisyAmpa #

Bases: Neuron

TEMPLATE

Izhikevich (2003)-like neuron model with additional conductance based synapses for AMPA and GABA currents with noise in AMPA conductance.

Parameters:

Name Type Description Default
a float

Time constant of the recovery variable u.

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b float

Sensitivity of the recovery variable u to the membrane potential v.

 0
c float

After-spike reset value of the membrane potential v.

 0
d float

After-spike change of the recovery variable u.

 0
n2 float

Factor of the quadratic equation of the membrane potential v.

 0
n1 float

Factor of the quadratic equation of the membrane potential v.

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n0 float

Factor of the quadratic equation of the membrane potential v.

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tau_ampa float

Time constant of the AMPA conductance.

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tau_gaba float

Time constant of the GABA conductance.

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E_ampa float

Reversal potential of the AMPA conductance.

 0
E_gaba float

Reversal potential of the GABA conductance.

 0
I_app float

External applied current.

 0
increase_noise float

Increase of the Poisson distributed (equivalent to a Poisson distributed spike train as input) noise in the AMPA conductance.

 0
rates_noise float

Rate of the Poisson distributed noise in the AMPA conductance.

 0
Variables to record
  • g_ampa
  • g_gaba
  • v
  • u
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/izhikevich_2003_like_nm.py 
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class Izhikevich2003NoisyAmpa(Neuron):
    """
    TEMPLATE

    [Izhikevich (2003)](https://doi.org/10.1109/TNN.2003.820440)-like neuron model with
    additional conductance based synapses for AMPA and GABA currents with noise in AMPA
    conductance.

    Parameters:
        a (float, optional):
            Time constant of the recovery variable u.
        b (float, optional):
            Sensitivity of the recovery variable u to the membrane potential v.
        c (float, optional):
            After-spike reset value of the membrane potential v.
        d (float, optional):
            After-spike change of the recovery variable u.
        n2 (float, optional):
            Factor of the quadratic equation of the membrane potential v.
        n1 (float, optional):
            Factor of the quadratic equation of the membrane potential v.
        n0 (float, optional):
            Factor of the quadratic equation of the membrane potential v.
        tau_ampa (float, optional):
            Time constant of the AMPA conductance.
        tau_gaba (float, optional):
            Time constant of the GABA conductance.
        E_ampa (float, optional):
            Reversal potential of the AMPA conductance.
        E_gaba (float, optional):
            Reversal potential of the GABA conductance.
        I_app (float, optional):
            External applied current.
        increase_noise (float, optional):
            Increase of the Poisson distributed (equivalent to a Poisson distributed
            spike train as input) noise in the AMPA conductance.
        rates_noise (float, optional):
            Rate of the Poisson distributed noise in the AMPA conductance.

    Variables to record:
        - g_ampa
        - g_gaba
        - v
        - u
        - r
    """

    # For reporting
    _instantiated = []

    def __init__(
        self,
        a: float = 0,
        b: float = 0,
        c: float = 0,
        d: float = 0,
        n2: float = 0,
        n1: float = 0,
        n0: float = 0,
        tau_ampa: float = 1,
        tau_gaba: float = 1,
        E_ampa: float = 0,
        E_gaba: float = 0,
        I_app: float = 0,
        increase_noise: float = 0,
        rates_noise: float = 0,
    ):
        # Create the arguments
        parameters = f"""
            a              = {a} : population
            b              = {b} : population
            c              = {c} : population
            d              = {d} : population
            n2             = {n2} : population
            n1             = {n1} : population
            n0             = {n0} : population
            tau_ampa       = {tau_ampa} : population
            tau_gaba       = {tau_gaba} : population
            E_ampa         = {E_ampa} : population
            E_gaba         = {E_gaba} : population
            I_app          = {I_app}
            increase_noise = {increase_noise} : population
            rates_noise    = {rates_noise}
        """

        super().__init__(
            parameters=parameters,
            equations="""
                dg_ampa/dt = ite(Uniform(0.0, 1.0) * 1000.0 / dt > rates_noise, -g_ampa/tau_ampa, -g_ampa/tau_ampa + increase_noise/dt)
                dg_gaba/dt = -g_gaba / tau_gaba
                dv/dt      = n2 * v * v + n1 * v + n0 - u + I_app - neg(g_ampa*(v - E_ampa)) - pos(g_gaba*(v - E_gaba))
                du/dt      = a * (b * v - u)
            """,
            spike="""
                v >= 30
            """,
            reset="""
                v = c
                u = u + d
            """,
            name="Izhikevich2003_noisy_AMPA",
            description="""
                Neuron model from Izhikevich (2003). With additional conductance based
                synapses for AMPA and GABA currents with noise in AMPA conductance.
            """,
        )

        # For reporting
        self._instantiated.append(True)

Izhikevich2003NoisyAmpaNonlin #

Bases: Neuron

TEMPLATE

Izhikevich (2003)-like neuron model with additional conductance based synapses for AMPA and GABA currents with noise in AMPA conductance. With nonlinear function for external current.

Parameters:

Name Type Description Default
a float

Time constant of the recovery variable u.

 0
b float

Sensitivity of the recovery variable u to the membrane potential v.

 0
c float

After-spike reset value of the membrane potential v.

 0
d float

After-spike change of the recovery variable u.

 0
n2 float

Factor of the quadratic equation of the membrane potential v.

 0
n1 float

Factor of the quadratic equation of the membrane potential v.

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n0 float

Factor of the quadratic equation of the membrane potential v.

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tau_ampa float

Time constant of the AMPA conductance.

 1
tau_gaba float

Time constant of the GABA conductance.

 1
E_ampa float

Reversal potential of the AMPA conductance.

 0
E_gaba float

Reversal potential of the GABA conductance.

 0
I_app float

External applied current.

 0
increase_noise float

Increase of the Poisson distributed (equivalent to a Poisson distributed spike train as input) noise in the AMPA conductance.

 0
rates_noise float

Rate of the Poisson distributed noise in the AMPA conductance.

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nonlin float

Exponent of the nonlinear function for the external current.

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Variables to record
  • g_ampa
  • g_gaba
  • I
  • v
  • u
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/izhikevich_2003_like_nm.py 
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class Izhikevich2003NoisyAmpaNonlin(Neuron):
    """
    TEMPLATE

    [Izhikevich (2003)](https://doi.org/10.1109/TNN.2003.820440)-like neuron model with
    additional conductance based synapses for AMPA and GABA currents with noise in AMPA
    conductance. With nonlinear function for external current.

    Parameters:
        a (float, optional):
            Time constant of the recovery variable u.
        b (float, optional):
            Sensitivity of the recovery variable u to the membrane potential v.
        c (float, optional):
            After-spike reset value of the membrane potential v.
        d (float, optional):
            After-spike change of the recovery variable u.
        n2 (float, optional):
            Factor of the quadratic equation of the membrane potential v.
        n1 (float, optional):
            Factor of the quadratic equation of the membrane potential v.
        n0 (float, optional):
            Factor of the quadratic equation of the membrane potential v.
        tau_ampa (float, optional):
            Time constant of the AMPA conductance.
        tau_gaba (float, optional):
            Time constant of the GABA conductance.
        E_ampa (float, optional):
            Reversal potential of the AMPA conductance.
        E_gaba (float, optional):
            Reversal potential of the GABA conductance.
        I_app (float, optional):
            External applied current.
        increase_noise (float, optional):
            Increase of the Poisson distributed (equivalent to a Poisson distributed
            spike train as input) noise in the AMPA conductance.
        rates_noise (float, optional):
            Rate of the Poisson distributed noise in the AMPA conductance.
        nonlin (float, optional):
            Exponent of the nonlinear function for the external current.

    Variables to record:
        - g_ampa
        - g_gaba
        - I
        - v
        - u
        - r
    """

    # For reporting
    _instantiated = []

    def __init__(
        self,
        a: float = 0,
        b: float = 0,
        c: float = 0,
        d: float = 0,
        n2: float = 0,
        n1: float = 0,
        n0: float = 0,
        tau_ampa: float = 1,
        tau_gaba: float = 1,
        E_ampa: float = 0,
        E_gaba: float = 0,
        I_app: float = 0,
        increase_noise: float = 0,
        rates_noise: float = 0,
        nonlin: float = 1,
    ):
        # Create the arguments
        parameters = f"""
            a              = {a} : population
            b              = {b} : population
            c              = {c} : population
            d              = {d} : population
            n2             = {n2} : population
            n1             = {n1} : population
            n0             = {n0} : population
            tau_ampa       = {tau_ampa} : population
            tau_gaba       = {tau_gaba} : population
            E_ampa         = {E_ampa} : population
            E_gaba         = {E_gaba} : population
            I_app          = {I_app}
            increase_noise = {increase_noise} : population
            rates_noise    = {rates_noise}
            nonlin         = {nonlin} : population
        """

        super().__init__(
            parameters=parameters,
            equations="""
                dg_ampa/dt = ite(Uniform(0.0, 1.0) * 1000.0 / dt > rates_noise, -g_ampa/tau_ampa, -g_ampa/tau_ampa + increase_noise/dt)
                dg_gaba/dt = -g_gaba / tau_gaba
                I = I_app - neg(g_ampa*(v - E_ampa)) - pos(g_gaba*(v - E_gaba))
                dv/dt      = n2 * v * v + n1 * v + n0 - u + f(I,nonlin)
                du/dt      = a * (b * v - u)
            """,
            spike="""
                v >= 30
            """,
            reset="""
                v = c
                u = u + d
            """,
            functions="""
                f(x,y)=((abs(x))**(1/y))/((x+1e-20)/(abs(x)+ 1e-20))
            """,
            name="Izhikevich2003_noisy_AMPA_nonlin",
            description="""
                Neuron model from Izhikevich (2003). With additional conductance based
                synapses for AMPA and GABA currents with noise in AMPA conductance.
                With nonlinear function for external current.
            """,
        )

        # For reporting
        self._instantiated.append(True)

Izhikevich2003NoisyAmpaOscillating #

Bases: Neuron

TEMPLATE

Izhikevich (2003)-like neuron model with additional conductance based synapses for AMPA and GABA currents with noise in AMPA conductance. With additional oscillation term.

Parameters:

Name Type Description Default
a float

Time constant of the recovery variable u.

 0
b float

Sensitivity of the recovery variable u to the membrane potential v.

 0
c float

After-spike reset value of the membrane potential v.

 0
d float

After-spike change of the recovery variable u.

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n2 float

Factor of the quadratic equation of the membrane potential v.

 0
n1 float

Factor of the quadratic equation of the membrane potential v.

 0
n0 float

Factor of the quadratic equation of the membrane potential v.

 0
tau_ampa float

Time constant of the AMPA conductance.

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tau_gaba float

Time constant of the GABA conductance.

 1
E_ampa float

Reversal potential of the AMPA conductance.

 0
E_gaba float

Reversal potential of the GABA conductance.

 0
I_app float

External applied current.

 0
increase_noise float

Increase of the Poisson distributed (equivalent to a Poisson distributed spike train as input) noise in the AMPA conductance.

 0
rates_noise float

Rate of the Poisson distributed noise in the AMPA conductance.

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freq float

Frequency of the oscillation term.

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amp float

Amplitude of the oscillation term.

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Variables to record
  • osc
  • g_ampa
  • g_gaba
  • v
  • u
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/izhikevich_2003_like_nm.py 
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class Izhikevich2003NoisyAmpaOscillating(Neuron):
    """
    TEMPLATE

    [Izhikevich (2003)](https://doi.org/10.1109/TNN.2003.820440)-like neuron model with
    additional conductance based synapses for AMPA and GABA currents with noise in AMPA
    conductance. With additional oscillation term.

    Parameters:
        a (float, optional):
            Time constant of the recovery variable u.
        b (float, optional):
            Sensitivity of the recovery variable u to the membrane potential v.
        c (float, optional):
            After-spike reset value of the membrane potential v.
        d (float, optional):
            After-spike change of the recovery variable u.
        n2 (float, optional):
            Factor of the quadratic equation of the membrane potential v.
        n1 (float, optional):
            Factor of the quadratic equation of the membrane potential v.
        n0 (float, optional):
            Factor of the quadratic equation of the membrane potential v.
        tau_ampa (float, optional):
            Time constant of the AMPA conductance.
        tau_gaba (float, optional):
            Time constant of the GABA conductance.
        E_ampa (float, optional):
            Reversal potential of the AMPA conductance.
        E_gaba (float, optional):
            Reversal potential of the GABA conductance.
        I_app (float, optional):
            External applied current.
        increase_noise (float, optional):
            Increase of the Poisson distributed (equivalent to a Poisson distributed
            spike train as input) noise in the AMPA conductance.
        rates_noise (float, optional):
            Rate of the Poisson distributed noise in the AMPA conductance.
        freq (float, optional):
            Frequency of the oscillation term.
        amp (float, optional):
            Amplitude of the oscillation term.

    Variables to record:
        - osc
        - g_ampa
        - g_gaba
        - v
        - u
        - r
    """

    # For reporting
    _instantiated = []

    def __init__(
        self,
        a: float = 0,
        b: float = 0,
        c: float = 0,
        d: float = 0,
        n2: float = 0,
        n1: float = 0,
        n0: float = 0,
        tau_ampa: float = 1,
        tau_gaba: float = 1,
        E_ampa: float = 0,
        E_gaba: float = 0,
        I_app: float = 0,
        increase_noise: float = 0,
        rates_noise: float = 0,
        freq: float = 0,
        amp: float = 6,
    ):
        # Create the arguments
        parameters = f"""
            a              = {a} : population
            b              = {b} : population
            c              = {c} : population
            d              = {d} : population
            n2             = {n2} : population
            n1             = {n1} : population
            n0             = {n0} : population
            tau_ampa       = {tau_ampa} : population
            tau_gaba       = {tau_gaba} : population
            E_ampa         = {E_ampa} : population
            E_gaba         = {E_gaba} : population
            I_app          = {I_app}
            increase_noise = {increase_noise} : population
            rates_noise    = {rates_noise}
            freq           = {freq}
            amp            = {amp}
        """

        super().__init__(
            parameters=parameters,
            equations="""
                osc        = amp * sin(t * 2 * pi * (freq / 1000))
                dg_ampa/dt = ite(Uniform(0.0, 1.0) * 1000.0 / dt > rates_noise, -g_ampa/tau_ampa, -g_ampa/tau_ampa + increase_noise/dt)
                dg_gaba/dt = -g_gaba / tau_gaba
                dv/dt      = n2 * v * v + n1 * v + n0 - u + I_app - neg(g_ampa*(v - E_ampa)) - pos(g_gaba*(v - E_gaba)) + osc
                du/dt      = a * (b * v - u)
            """,
            spike="""
                v >= 30
            """,
            reset="""
                v = c
                u = u + d
            """,
            name="Izhikevich2003_noisy_AMPA_oscillating",
            description="""
                Neuron model from Izhikevich (2003). With additional conductance based
                synapses for AMPA and GABA currents with noise in AMPA conductance.
                With additional oscillation term.
            """,
        )

        # For reporting
        self._instantiated.append(True)

Izhikevich2003NoisyBase #

Bases: Neuron

TEMPLATE

Izhikevich (2003)-like neuron model with additional conductance based synapses for AMPA and GABA currents and a noisy baseline current.

Parameters:

Name Type Description Default
a float

Time constant of the recovery variable u.

 0
b float

Sensitivity of the recovery variable u to the membrane potential v.

 0
c float

After-spike reset value of the membrane potential v.

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d float

After-spike change of the recovery variable u.

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n2 float

Factor of the quadratic equation of the membrane potential v.

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n1 float

Factor of the quadratic equation of the membrane potential v.

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n0 float

Factor of the quadratic equation of the membrane potential v.

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tau_ampa float

Time constant of the AMPA conductance.

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tau_gaba float

Time constant of the GABA conductance.

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E_ampa float

Reversal potential of the AMPA conductance.

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E_gaba float

Reversal potential of the GABA conductance.

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I_app float

External applied current.

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base_mean float

Mean of the baseline current.

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base_noise float

Standard deviation of the baseline current.

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rate_base_noise float

Rate of the Poisson distributed noise in the baseline current, i.e. how often the baseline current is changed randomly.

 0
Variables to record
  • g_ampa
  • g_gaba
  • offset_base
  • I_base
  • I
  • v
  • u
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/izhikevich_2003_like_nm.py 
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class Izhikevich2003NoisyBase(Neuron):
    """
    TEMPLATE

    [Izhikevich (2003)](https://doi.org/10.1109/TNN.2003.820440)-like neuron model with
    additional conductance based synapses for AMPA and GABA currents and a noisy baseline
    current.

    Parameters:
        a (float, optional):
            Time constant of the recovery variable u.
        b (float, optional):
            Sensitivity of the recovery variable u to the membrane potential v.
        c (float, optional):
            After-spike reset value of the membrane potential v.
        d (float, optional):
            After-spike change of the recovery variable u.
        n2 (float, optional):
            Factor of the quadratic equation of the membrane potential v.
        n1 (float, optional):
            Factor of the quadratic equation of the membrane potential v.
        n0 (float, optional):
            Factor of the quadratic equation of the membrane potential v.
        tau_ampa (float, optional):
            Time constant of the AMPA conductance.
        tau_gaba (float, optional):
            Time constant of the GABA conductance.
        E_ampa (float, optional):
            Reversal potential of the AMPA conductance.
        E_gaba (float, optional):
            Reversal potential of the GABA conductance.
        I_app (float, optional):
            External applied current.
        base_mean (float, optional):
            Mean of the baseline current.
        base_noise (float, optional):
            Standard deviation of the baseline current.
        rate_base_noise (float, optional):
            Rate of the Poisson distributed noise in the baseline current, i.e. how
            often the baseline current is changed randomly.

    Variables to record:
        - g_ampa
        - g_gaba
        - offset_base
        - I_base
        - I
        - v
        - u
        - r
    """

    # For reporting
    _instantiated = []

    def __init__(
        self,
        a: float = 0,
        b: float = 0,
        c: float = 0,
        d: float = 0,
        n2: float = 0,
        n1: float = 0,
        n0: float = 0,
        tau_ampa: float = 1,
        tau_gaba: float = 1,
        E_ampa: float = 0,
        E_gaba: float = 0,
        I_app: float = 0,
        base_mean: float = 0,
        base_noise: float = 0,
        rate_base_noise: float = 0,
    ):
        # Create the arguments
        parameters = f"""
            a               = {a} : population
            b               = {b} : population
            c               = {c} : population
            d               = {d} : population
            n2              = {n2} : population
            n1              = {n1} : population
            n0              = {n0} : population
            tau_ampa        = {tau_ampa} : population
            tau_gaba        = {tau_gaba} : population
            E_ampa          = {E_ampa} : population
            E_gaba          = {E_gaba} : population
            I_app           = {I_app}
            base_mean       = {base_mean}
            base_noise      = {base_noise}
            rate_base_noise = {rate_base_noise}
        """

        super().__init__(
            parameters=parameters,
            equations="""
                dg_ampa/dt  = -g_ampa/tau_ampa
                dg_gaba/dt  = -g_gaba / tau_gaba
                offset_base = ite(Uniform(0.0, 1.0) * 1000.0 / dt > rate_base_noise, offset_base, Normal(0, 1) * base_noise)
                I_base      = base_mean + offset_base
                I           = I_app - neg(g_ampa*(v - E_ampa)) - pos(g_gaba*(v - E_gaba)) + I_base
                dv/dt       = n2 * v * v + n1 * v + n0 - u + I
                du/dt       = a * (b * v - u)
            """,
            spike="""
                v >= 30
            """,
            reset="""
                v = c
                u = u + d
            """,
            name="Izhikevich2003_noisy_I",
            description="""
                Neuron model from Izhikevich (2003). With additional conductance based
                synapses for AMPA and GABA currents and a noisy baseline current.
            """,
        )

        # For reporting
        self._instantiated.append(True)

Izhikevich2003NoisyBaseNonlin #

Bases: Neuron

TEMPLATE

Izhikevich (2003)-like neuron model with additional conductance based synapses for AMPA and GABA currents and a noisy baseline current. With nonlinear function for external current.

Parameters:

Name Type Description Default
a float

Time constant of the recovery variable u.

 0
b float

Sensitivity of the recovery variable u to the membrane potential v.

 0
c float

After-spike reset value of the membrane potential v.

 0
d float

After-spike change of the recovery variable u.

 0
n2 float

Factor of the quadratic equation of the membrane potential v.

 0
n1 float

Factor of the quadratic equation of the membrane potential v.

 0
n0 float

Factor of the quadratic equation of the membrane potential v.

 0
tau_ampa float

Time constant of the AMPA conductance.

 1
tau_gaba float

Time constant of the GABA conductance.

 1
E_ampa float

Reversal potential of the AMPA conductance.

 0
E_gaba float

Reversal potential of the GABA conductance.

 0
I_app float

External applied current.

 0
base_mean float

Mean of the baseline current.

 0
base_noise float

Standard deviation of the baseline current.

 0
rate_base_noise float

Rate of the Poisson distributed noise in the baseline current, i.e. how often the baseline current is changed randomly.

 0
nonlin float

Exponent of the nonlinear function for the external current.

 1
Variables to record
  • g_ampa
  • g_gaba
  • offset_base
  • I_base
  • I
  • v
  • u
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/izhikevich_2003_like_nm.py 
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class Izhikevich2003NoisyBaseNonlin(Neuron):
    """
    TEMPLATE

    [Izhikevich (2003)](https://doi.org/10.1109/TNN.2003.820440)-like neuron model with
    additional conductance based synapses for AMPA and GABA currents and a noisy baseline
    current. With nonlinear function for external current.

    Parameters:
        a (float, optional):
            Time constant of the recovery variable u.
        b (float, optional):
            Sensitivity of the recovery variable u to the membrane potential v.
        c (float, optional):
            After-spike reset value of the membrane potential v.
        d (float, optional):
            After-spike change of the recovery variable u.
        n2 (float, optional):
            Factor of the quadratic equation of the membrane potential v.
        n1 (float, optional):
            Factor of the quadratic equation of the membrane potential v.
        n0 (float, optional):
            Factor of the quadratic equation of the membrane potential v.
        tau_ampa (float, optional):
            Time constant of the AMPA conductance.
        tau_gaba (float, optional):
            Time constant of the GABA conductance.
        E_ampa (float, optional):
            Reversal potential of the AMPA conductance.
        E_gaba (float, optional):
            Reversal potential of the GABA conductance.
        I_app (float, optional):
            External applied current.
        base_mean (float, optional):
            Mean of the baseline current.
        base_noise (float, optional):
            Standard deviation of the baseline current.
        rate_base_noise (float, optional):
            Rate of the Poisson distributed noise in the baseline current, i.e. how
            often the baseline current is changed randomly.
        nonlin (float, optional):
            Exponent of the nonlinear function for the external current.

    Variables to record:
        - g_ampa
        - g_gaba
        - offset_base
        - I_base
        - I
        - v
        - u
        - r
    """

    # For reporting
    _instantiated = []

    def __init__(
        self,
        a: float = 0,
        b: float = 0,
        c: float = 0,
        d: float = 0,
        n2: float = 0,
        n1: float = 0,
        n0: float = 0,
        tau_ampa: float = 1,
        tau_gaba: float = 1,
        E_ampa: float = 0,
        E_gaba: float = 0,
        I_app: float = 0,
        base_mean: float = 0,
        base_noise: float = 0,
        rate_base_noise: float = 0,
        nonlin: float = 1,
    ):
        # Create the arguments
        parameters = f"""
            a               = {a} : population
            b               = {b} : population
            c               = {c} : population
            d               = {d} : population
            n2              = {n2} : population
            n1              = {n1} : population
            n0              = {n0} : population
            tau_ampa        = {tau_ampa} : population
            tau_gaba        = {tau_gaba} : population
            E_ampa          = {E_ampa} : population
            E_gaba          = {E_gaba} : population
            I_app           = {I_app}
            base_mean       = {base_mean}
            base_noise      = {base_noise}
            rate_base_noise = {rate_base_noise}
            nonlin          = {nonlin} : population
        """

        super().__init__(
            parameters=parameters,
            equations="""
                dg_ampa/dt  = -g_ampa/tau_ampa
                dg_gaba/dt  = -g_gaba / tau_gaba
                offset_base = ite(Uniform(0.0, 1.0) * 1000.0 / dt > rate_base_noise, offset_base, Normal(0, 1) * base_noise)
                I_base      = base_mean + offset_base
                I           = I_app - neg(g_ampa*(v - E_ampa)) - pos(g_gaba*(v - E_gaba))
                dv/dt       = n2 * v * v + n1 * v + n0 - u + f(I,nonlin) + I_base
                du/dt       = a * (b * v - u)
            """,
            spike="""
                v >= 30
            """,
            reset="""
                v = c
                u = u + d
            """,
            functions="""
                f(x,y)=((abs(x))**(1/y))/((x+1e-20)/(abs(x)+ 1e-20))
            """,
            name="Izhikevich2003_noisy_I_nonlin",
            description="""
                Neuron model from Izhikevich (2003). With additional conductance based
                synapses for AMPA and GABA currents and a noisy baseline current.
                With nonlinear function for external current.
            """,
        )

        # For reporting
        self._instantiated.append(True)

Izhikevich (2007)-like Neurons#

Izhikevich2007 #

Bases: Neuron

TEMPLATE

Izhikevich (2007)-like neuron model.

Parameters:

Name Type Description Default
C float

Membrane capacitance.

 100.0
k float

Scaling factor for the quadratic term in the membrane potential.

 0.7
v_r float

Resting membrane potential.

 -60.0
v_t float

Instantaneous activation threshold potential.

 -40.0
a float

Time scale of the recovery variable u.

 0.03
b float

Sensitivity of the recovery variable u to the the membrane potential v.

 -2.0
c float

After-spike reset value of the membrane potential.

 -50.0
d float

After-spike change of the recovery variable u.

 100.0
v_peak float

Spike cut-off value for the membrane potential.

 35.0
I_app float

External applied input current.

 0.0
params_for_pop bool

If True, the parameters are population-wide and not neuron-specific.

 False
init dict

Initial values for the variables.

 {}
Variables to record
  • I_v
  • v
  • u
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/izhikevich_2007_like_nm.py 
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class Izhikevich2007(Neuron):
    """
    TEMPLATE

    [Izhikevich (2007)](https://isbnsearch.org/isbn/9780262090438)-like neuron model.

    Parameters:
        C (float, optional):
            Membrane capacitance.
        k (float, optional):
            Scaling factor for the quadratic term in the membrane potential.
        v_r (float, optional):
            Resting membrane potential.
        v_t (float, optional):
            Instantaneous activation threshold potential.
        a (float, optional):
            Time scale of the recovery variable u.
        b (float, optional):
            Sensitivity of the recovery variable u to the the membrane potential v.
        c (float, optional):
            After-spike reset value of the membrane potential.
        d (float, optional):
            After-spike change of the recovery variable u.
        v_peak (float, optional):
            Spike cut-off value for the membrane potential.
        I_app (float, optional):
            External applied input current.
        params_for_pop (bool, optional):
            If True, the parameters are population-wide and not neuron-specific.
        init (dict, optional):
            Initial values for the variables.

    Variables to record:
        - I_v
        - v
        - u
        - r
    """

    # For reporting
    _instantiated = []

    def __init__(
        self,
        C: float = 100.0,
        k: float = 0.7,
        v_r: float = -60.0,
        v_t: float = -40.0,
        a: float = 0.03,
        b: float = -2.0,
        c: float = -50.0,
        d: float = 100.0,
        v_peak: float = 35.0,
        I_app: float = 0.0,
        params_for_pop: bool = False,
        init: dict = {},
    ):
        # Create the arguments
        parameters = f"""
            C      = {C} {': population' if params_for_pop else ''} # pF
            k      = {k} {': population' if params_for_pop else ''} # pS * mV**-1
            v_r    = {v_r} {': population' if params_for_pop else ''} # mV
            v_t    = {v_t} {': population' if params_for_pop else ''} # mV
            a      = {a} {': population' if params_for_pop else ''} # ms**-1
            b      = {b} {': population' if params_for_pop else ''} # nS
            c      = {c} {': population' if params_for_pop else ''} # mV
            d      = {d} {': population' if params_for_pop else ''} # pA
            v_peak = {v_peak} {': population' if params_for_pop else ''} # mV
            I_app  = {I_app} # pA
        """

        # get equations
        equations = _get_equation_izhikevich_2007()

        # set initial values
        equations = _set_init(equations, init)

        # create the neuron
        super().__init__(
            parameters=parameters,
            equations=equations,
            spike="v >= v_peak",
            reset="""
                v = c
                u = u + d
            """,
            name="Izhikevich2007",
            description="Neuron model equations from Izhikevich (2007).",
        )

        # For reporting
        self._instantiated.append(True)

Izhikevich2007RecCur #

Bases: Neuron

TEMPLATE

Izhikevich (2007)-like neuron model with separate currents to record.

Parameters:

Name Type Description Default
C float

Membrane capacitance.

 100.0
k float

Scaling factor for the quadratic term in the membrane potential.

 0.7
v_r float

Resting membrane potential.

 -60.0
v_t float

Instantaneous activation threshold potential.

 -40.0
a float

Time scale of the recovery variable u.

 0.03
b float

Sensitivity of the recovery variable u to the the membrane potential v.

 -2.0
c float

After-spike reset value of the membrane potential.

 -50.0
d float

After-spike change of the recovery variable u.

 100.0
v_peak float

Spike cut-off value for the membrane potential.

 35.0
I_app float

External applied input current.

 0.0
params_for_pop bool

If True, the parameters are population-wide and not neuron-specific.

 False
init dict

Initial values for the variables.

 {}
Variables to record
  • I_v
  • v
  • u
  • I_u
  • I_k
  • I_a
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/izhikevich_2007_like_nm.py 
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class Izhikevich2007RecCur(Neuron):
    """
    TEMPLATE

    [Izhikevich (2007)](https://isbnsearch.org/isbn/9780262090438)-like neuron model
    with separate currents to record.

    Parameters:
        C (float, optional):
            Membrane capacitance.
        k (float, optional):
            Scaling factor for the quadratic term in the membrane potential.
        v_r (float, optional):
            Resting membrane potential.
        v_t (float, optional):
            Instantaneous activation threshold potential.
        a (float, optional):
            Time scale of the recovery variable u.
        b (float, optional):
            Sensitivity of the recovery variable u to the the membrane potential v.
        c (float, optional):
            After-spike reset value of the membrane potential.
        d (float, optional):
            After-spike change of the recovery variable u.
        v_peak (float, optional):
            Spike cut-off value for the membrane potential.
        I_app (float, optional):
            External applied input current.
        params_for_pop (bool, optional):
            If True, the parameters are population-wide and not neuron-specific.
        init (dict, optional):
            Initial values for the variables.

    Variables to record:
        - I_v
        - v
        - u
        - I_u
        - I_k
        - I_a
        - r
    """

    # For reporting
    _instantiated = []

    def __init__(
        self,
        C: float = 100.0,
        k: float = 0.7,
        v_r: float = -60.0,
        v_t: float = -40.0,
        a: float = 0.03,
        b: float = -2.0,
        c: float = -50.0,
        d: float = 100.0,
        v_peak: float = 35.0,
        I_app: float = 0.0,
        params_for_pop: bool = False,
        init: dict = {},
    ):
        # Create the arguments
        parameters = f"""
            C      = {C} {': population' if params_for_pop else ''} # pF
            k      = {k} {': population' if params_for_pop else ''} # pS * mV**-1
            v_r    = {v_r} {': population' if params_for_pop else ''} # mV
            v_t    = {v_t} {': population' if params_for_pop else ''} # mV
            a      = {a} {': population' if params_for_pop else ''} # ms**-1
            b      = {b} {': population' if params_for_pop else ''} # nS
            c      = {c} {': population' if params_for_pop else ''} # mV
            d      = {d} {': population' if params_for_pop else ''} # pA
            v_peak = {v_peak} {': population' if params_for_pop else ''} # mV
            I_app  = {I_app} # pA
        """

        affix = """
            I_u = -u
            I_k = k*(v - v_r)*(v - v_t)
            I_a = I_app
        """

        # get equations
        equations = _get_equation_izhikevich_2007(affix=affix)

        # set initial values
        equations = _set_init(equations, init)

        super().__init__(
            parameters=parameters,
            equations=equations,
            spike="v >= v_peak",
            reset="""
                v = c
                u = u + d
            """,
            name="Izhikevich2007_record_currents",
            description="""
                Neuron model equations from Izhikevich (2007) with separate
                currents to record.
            """,
        )

        # For reporting
        self._instantiated.append(True)

Izhikevich2007VoltageClamp #

Bases: Neuron

TEMPLATE

Izhikevich (2007)-like neuron model with voltage clamp to record I_inf.

Parameters:

Name Type Description Default
C float

Membrane capacitance.

 100.0
k float

Scaling factor for the quadratic term in the membrane potential.

 0.7
v_r float

Resting membrane potential.

 -60.0
v_t float

Instantaneous activation threshold potential.

 -40.0
a float

Time scale of the recovery variable u.

 0.03
b float

Sensitivity of the recovery variable u to the the membrane potential v.

 -2.0
c float

After-spike reset value of the membrane potential.

 -50.0
d float

After-spike change of the recovery variable u.

 100.0
v_peak float

Spike cut-off value for the membrane potential.

 35.0
I_app float

External applied input current.

 0.0
params_for_pop bool

If True, the parameters are population-wide and not neuron-specific.

 False
init dict

Initial values for the variables.

 {}
Variables to record
  • I_v
  • v
  • u
  • I_inf
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/izhikevich_2007_like_nm.py 
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class Izhikevich2007VoltageClamp(Neuron):
    """
    TEMPLATE

    [Izhikevich (2007)](https://isbnsearch.org/isbn/9780262090438)-like neuron model
    with voltage clamp to record I_inf.

    Parameters:
        C (float, optional):
            Membrane capacitance.
        k (float, optional):
            Scaling factor for the quadratic term in the membrane potential.
        v_r (float, optional):
            Resting membrane potential.
        v_t (float, optional):
            Instantaneous activation threshold potential.
        a (float, optional):
            Time scale of the recovery variable u.
        b (float, optional):
            Sensitivity of the recovery variable u to the the membrane potential v.
        c (float, optional):
            After-spike reset value of the membrane potential.
        d (float, optional):
            After-spike change of the recovery variable u.
        v_peak (float, optional):
            Spike cut-off value for the membrane potential.
        I_app (float, optional):
            External applied input current.
        params_for_pop (bool, optional):
            If True, the parameters are population-wide and not neuron-specific.
        init (dict, optional):
            Initial values for the variables.

    Variables to record:
        - I_v
        - v
        - u
        - I_inf
        - r
    """

    # For reporting
    _instantiated = []

    def __init__(
        self,
        C: float = 100.0,
        k: float = 0.7,
        v_r: float = -60.0,
        v_t: float = -40.0,
        a: float = 0.03,
        b: float = -2.0,
        c: float = -50.0,
        d: float = 100.0,
        v_peak: float = 35.0,
        I_app: float = 0.0,
        params_for_pop: bool = False,
        init: dict = {},
    ):
        # Create the arguments
        parameters = f"""
            C      = {C} {': population' if params_for_pop else ''} # pF
            k      = {k} {': population' if params_for_pop else ''} # pS * mV**-1
            v_r    = {v_r} {': population' if params_for_pop else ''} # mV
            v_t    = {v_t} {': population' if params_for_pop else ''} # mV
            a      = {a} {': population' if params_for_pop else ''} # ms**-1
            b      = {b} {': population' if params_for_pop else ''} # nS
            c      = {c} {': population' if params_for_pop else ''} # mV
            d      = {d} {': population' if params_for_pop else ''} # pA
            v_peak = {v_peak} {': population' if params_for_pop else ''} # mV
            I_app  = {I_app} # pA
        """

        dv = "0"
        affix = f"I_inf = {_dv_default}"

        # get equations
        equations = _get_equation_izhikevich_2007(dv=dv, affix=affix)

        # set initial values
        equations = _set_init(equations, init)

        super().__init__(
            parameters=parameters,
            equations=equations,
            spike="v >= v_peak",
            reset="""
                v = c
                u = u + d
            """,
            name="Izhikevich2007_voltage_clamp",
            description="""
                Neuron model equations from Izhikevich (2007) with voltage clamp
                to record I_inf.
            """,
        )

        # For reporting
        self._instantiated.append(True)

Izhikevich2007Syn #

Bases: Neuron

TEMPLATE

Izhikevich (2007)-like neuron model with conductance-based synapses.

Parameters:

Name Type Description Default
C float

Membrane capacitance.

 100.0
k float

Scaling factor for the quadratic term in the membrane potential.

 0.7
v_r float

Resting membrane potential.

 -60.0
v_t float

Instantaneous activation threshold potential.

 -40.0
a float

Time scale of the recovery variable u.

 0.03
b float

Sensitivity of the recovery variable u to the the membrane potential v.

 -2.0
c float

After-spike reset value of the membrane potential.

 -50.0
d float

After-spike change of the recovery variable u.

 100.0
v_peak float

Spike cut-off value for the membrane potential.

 35.0
I_app float

External applied input current.

 0.0
tau_ampa float

Time constant of the AMPA synapse.

 10.0
tau_gaba float

Time constant of the GABA synapse.

 10.0
E_ampa float

Reversal potential of the AMPA synapse.

 0.0
E_gaba float

Reversal potential of the GABA synapse.

 -90.0
params_for_pop bool

If True, the parameters are population-wide and not neuron-specific.

 False
init dict

Initial values for the variables.

 {}
Variables to record
  • g_ampa
  • g_gaba
  • I_v
  • v
  • u
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/izhikevich_2007_like_nm.py 
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class Izhikevich2007Syn(Neuron):
    """
    TEMPLATE

    [Izhikevich (2007)](https://isbnsearch.org/isbn/9780262090438)-like neuron model
    with conductance-based synapses.

    Parameters:
        C (float, optional):
            Membrane capacitance.
        k (float, optional):
            Scaling factor for the quadratic term in the membrane potential.
        v_r (float, optional):
            Resting membrane potential.
        v_t (float, optional):
            Instantaneous activation threshold potential.
        a (float, optional):
            Time scale of the recovery variable u.
        b (float, optional):
            Sensitivity of the recovery variable u to the the membrane potential v.
        c (float, optional):
            After-spike reset value of the membrane potential.
        d (float, optional):
            After-spike change of the recovery variable u.
        v_peak (float, optional):
            Spike cut-off value for the membrane potential.
        I_app (float, optional):
            External applied input current.
        tau_ampa (float, optional):
            Time constant of the AMPA synapse.
        tau_gaba (float, optional):
            Time constant of the GABA synapse.
        E_ampa (float, optional):
            Reversal potential of the AMPA synapse.
        E_gaba (float, optional):
            Reversal potential of the GABA synapse.
        params_for_pop (bool, optional):
            If True, the parameters are population-wide and not neuron-specific.
        init (dict, optional):
            Initial values for the variables.

    Variables to record:
        - g_ampa
        - g_gaba
        - I_v
        - v
        - u
        - r
    """

    # For reporting
    _instantiated = []

    def __init__(
        self,
        C: float = 100.0,
        k: float = 0.7,
        v_r: float = -60.0,
        v_t: float = -40.0,
        a: float = 0.03,
        b: float = -2.0,
        c: float = -50.0,
        d: float = 100.0,
        v_peak: float = 35.0,
        I_app: float = 0.0,
        tau_ampa: float = 10.0,
        tau_gaba: float = 10.0,
        E_ampa: float = 0.0,
        E_gaba: float = -90.0,
        params_for_pop: bool = False,
        init: dict = {},
    ):
        # Create the arguments
        parameters = f"""
            C      = {C} {': population' if params_for_pop else ''}
            k      = {k} {': population' if params_for_pop else ''}
            v_r    = {v_r} {': population' if params_for_pop else ''}
            v_t    = {v_t} {': population' if params_for_pop else ''}
            a      = {a} {': population' if params_for_pop else ''}
            b      = {b} {': population' if params_for_pop else ''}
            c      = {c} {': population' if params_for_pop else ''}
            d      = {d} {': population' if params_for_pop else ''}
            v_peak = {v_peak} {': population' if params_for_pop else ''}
            I_app  = {I_app} # pA
            tau_ampa = {tau_ampa} {': population' if params_for_pop else ''}
            tau_gaba = {tau_gaba} {': population' if params_for_pop else ''}
            E_ampa   = {E_ampa} {': population' if params_for_pop else ''}
            E_gaba   = {E_gaba} {': population' if params_for_pop else ''}
        """

        syn = _syn_default
        i_v = f"I_app {_I_syn}"

        # get equations
        equations = _get_equation_izhikevich_2007(syn=syn, i_v=i_v)

        # set initial values
        equations = _set_init(equations, init)

        super().__init__(
            parameters=parameters,
            equations=equations,
            spike="v >= v_peak",
            reset="""
                v = c
                u = u + d
            """,
            name="Izhikevich2007_syn",
            description="""
                Neuron model equations from Izhikevich (2007) with conductance-based
                AMPA and GABA synapses/currents.
            """,
        )

        # For reporting
        self._instantiated.append(True)

Izhikevich2007NoisyAmpa #

Bases: Neuron

TEMPLATE

Izhikevich (2007)-like neuron model with conductance-based AMPA and GABA synapses with noise in the AMPA conductance.

Parameters:

Name Type Description Default
C float

Membrane capacitance.

 100.0
k float

Scaling factor for the quadratic term in the membrane potential.

 0.7
v_r float

Resting membrane potential.

 -60.0
v_t float

Instantaneous activation threshold potential.

 -40.0
a float

Time scale of the recovery variable u.

 0.03
b float

Sensitivity of the recovery variable u to the the membrane potential v.

 -2.0
c float

After-spike reset value of the membrane potential.

 -50.0
d float

After-spike change of the recovery variable u.

 100.0
v_peak float

Spike cut-off value for the membrane potential.

 35.0
I_app float

External applied input current.

 0.0
tau_ampa float

Time constant of the AMPA synapse.

 10.0
tau_gaba float

Time constant of the GABA synapse.

 10.0
E_ampa float

Reversal potential of the AMPA synapse.

 0.0
E_gaba float

Reversal potential of the GABA synapse.

 -90.0
increase_noise float

Increase of AMPA conductance due to noise (equal to a Poisson distributed spike train as input).

 0.0
rates_noise float

Rate of the noise in the AMPA conductance.

 0.0
params_for_pop bool

If True, the parameters are population-wide and not neuron-specific.

 False
init dict

Initial values for the variables.

 {}
Variables to record
  • g_ampa
  • g_gaba
  • I_v
  • v
  • u
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/izhikevich_2007_like_nm.py 
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class Izhikevich2007NoisyAmpa(Neuron):
    """
    TEMPLATE

    [Izhikevich (2007)](https://isbnsearch.org/isbn/9780262090438)-like neuron model
    with conductance-based AMPA and GABA synapses with noise in the AMPA conductance.

    Parameters:
        C (float, optional):
            Membrane capacitance.
        k (float, optional):
            Scaling factor for the quadratic term in the membrane potential.
        v_r (float, optional):
            Resting membrane potential.
        v_t (float, optional):
            Instantaneous activation threshold potential.
        a (float, optional):
            Time scale of the recovery variable u.
        b (float, optional):
            Sensitivity of the recovery variable u to the the membrane potential v.
        c (float, optional):
            After-spike reset value of the membrane potential.
        d (float, optional):
            After-spike change of the recovery variable u.
        v_peak (float, optional):
            Spike cut-off value for the membrane potential.
        I_app (float, optional):
            External applied input current.
        tau_ampa (float, optional):
            Time constant of the AMPA synapse.
        tau_gaba (float, optional):
            Time constant of the GABA synapse.
        E_ampa (float, optional):
            Reversal potential of the AMPA synapse.
        E_gaba (float, optional):
            Reversal potential of the GABA synapse.
        increase_noise (float, optional):
            Increase of AMPA conductance due to noise (equal to a Poisson distributed
            spike train as input).
        rates_noise (float, optional):
            Rate of the noise in the AMPA conductance.
        params_for_pop (bool, optional):
            If True, the parameters are population-wide and not neuron-specific.
        init (dict, optional):
            Initial values for the variables.

    Variables to record:
        - g_ampa
        - g_gaba
        - I_v
        - v
        - u
        - r
    """

    # For reporting
    _instantiated = []

    def __init__(
        self,
        C: float = 100.0,
        k: float = 0.7,
        v_r: float = -60.0,
        v_t: float = -40.0,
        a: float = 0.03,
        b: float = -2.0,
        c: float = -50.0,
        d: float = 100.0,
        v_peak: float = 35.0,
        I_app: float = 0.0,
        tau_ampa: float = 10.0,
        tau_gaba: float = 10.0,
        E_ampa: float = 0.0,
        E_gaba: float = -90.0,
        increase_noise: float = 0.0,
        rates_noise: float = 0.0,
        params_for_pop: bool = False,
        init: dict = {},
    ):
        # Create the arguments
        parameters = f"""
            C              = {C} {': population' if params_for_pop else ''}
            k              = {k} {': population' if params_for_pop else ''}
            v_r            = {v_r} {': population' if params_for_pop else ''}
            v_t            = {v_t} {': population' if params_for_pop else ''}
            a              = {a} {': population' if params_for_pop else ''}
            b              = {b} {': population' if params_for_pop else ''}
            c              = {c} {': population' if params_for_pop else ''}
            d              = {d} {': population' if params_for_pop else ''}
            v_peak         = {v_peak} {': population' if params_for_pop else ''}
            tau_ampa       = {tau_ampa} {': population' if params_for_pop else ''}
            tau_gaba       = {tau_gaba} {': population' if params_for_pop else ''}
            E_ampa         = {E_ampa} {': population' if params_for_pop else ''}
            E_gaba         = {E_gaba} {': population' if params_for_pop else ''}
            I_app          = {I_app} # pA
            increase_noise = {increase_noise} {': population' if params_for_pop else ''}
            rates_noise    = {rates_noise}
        """

        syn = _syn_noisy
        i_v = f"I_app {_I_syn}"

        # get equations
        equations = _get_equation_izhikevich_2007(syn=syn, i_v=i_v)

        # set initial values
        equations = _set_init(equations, init)

        super().__init__(
            parameters=parameters,
            equations=equations,
            spike="v >= v_peak",
            reset="""
                v = c
                u = u + d
            """,
            name="Izhikevich2007_noisy_AMPA",
            description="""
                Standard neuron model from Izhikevich (2007) with additional
                conductance based synapses for AMPA and GABA currents with noise
                in AMPA conductance.
            """,
        )

        # For reporting
        self._instantiated.append(True)

Izhikevich2007NoisyBase #

Bases: Neuron

TEMPLATE

Izhikevich (2007)-like neuron model with conductance-based AMPA and GABA synapses with noise in the baseline current.

Parameters:

Name Type Description Default
C float

Membrane capacitance.

 100.0
k float

Scaling factor for the quadratic term in the membrane potential.

 0.7
v_r float

Resting membrane potential.

 -60.0
v_t float

Instantaneous activation threshold potential.

 -40.0
a float

Time scale of the recovery variable u.

 0.03
b float

Sensitivity of the recovery variable u to the the membrane potential v.

 -2.0
c float

After-spike reset value of the membrane potential.

 -50.0
d float

After-spike change of the recovery variable u.

 100.0
v_peak float

Spike cut-off value for the membrane potential.

 35.0
I_app float

External applied input current.

 0.0
tau_ampa float

Time constant of the AMPA synapse.

 10.0
tau_gaba float

Time constant of the GABA synapse.

 10.0
E_ampa float

Reversal potential of the AMPA synapse.

 0.0
E_gaba float

Reversal potential of the GABA synapse.

 -90.0
base_mean float

Mean of the baseline current.

 0.0
base_noise float

Standard deviation of the baseline current noise.

 0.0
rate_base_noise float

Rate of the noise update (Poisson distributed) in the baseline current.

 0.0
params_for_pop bool

If True, the parameters are population-wide and not neuron-specific.

 False
init dict

Initial values for the variables.

 {}
Variables to record
  • offset_base
  • I_base
  • g_ampa
  • g_gaba
  • I_v
  • v
  • u
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/izhikevich_2007_like_nm.py 
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class Izhikevich2007NoisyBase(Neuron):
    """
    TEMPLATE

    [Izhikevich (2007)](https://isbnsearch.org/isbn/9780262090438)-like neuron model
    with conductance-based AMPA and GABA synapses with noise in the baseline current.

    Parameters:
        C (float, optional):
            Membrane capacitance.
        k (float, optional):
            Scaling factor for the quadratic term in the membrane potential.
        v_r (float, optional):
            Resting membrane potential.
        v_t (float, optional):
            Instantaneous activation threshold potential.
        a (float, optional):
            Time scale of the recovery variable u.
        b (float, optional):
            Sensitivity of the recovery variable u to the the membrane potential v.
        c (float, optional):
            After-spike reset value of the membrane potential.
        d (float, optional):
            After-spike change of the recovery variable u.
        v_peak (float, optional):
            Spike cut-off value for the membrane potential.
        I_app (float, optional):
            External applied input current.
        tau_ampa (float, optional):
            Time constant of the AMPA synapse.
        tau_gaba (float, optional):
            Time constant of the GABA synapse.
        E_ampa (float, optional):
            Reversal potential of the AMPA synapse.
        E_gaba (float, optional):
            Reversal potential of the GABA synapse.
        base_mean (float, optional):
            Mean of the baseline current.
        base_noise (float, optional):
            Standard deviation of the baseline current noise.
        rate_base_noise (float, optional):
            Rate of the noise update (Poisson distributed) in the baseline current.
        params_for_pop (bool, optional):
            If True, the parameters are population-wide and not neuron-specific.
        init (dict, optional):
            Initial values for the variables.

    Variables to record:
        - offset_base
        - I_base
        - g_ampa
        - g_gaba
        - I_v
        - v
        - u
        - r
    """

    # For reporting
    _instantiated = []

    def __init__(
        self,
        C: float = 100.0,
        k: float = 0.7,
        v_r: float = -60.0,
        v_t: float = -40.0,
        a: float = 0.03,
        b: float = -2.0,
        c: float = -50.0,
        d: float = 100.0,
        v_peak: float = 35.0,
        I_app: float = 0.0,
        tau_ampa: float = 10.0,
        tau_gaba: float = 10.0,
        E_ampa: float = 0.0,
        E_gaba: float = -90.0,
        base_mean: float = 0.0,
        base_noise: float = 0.0,
        rate_base_noise: float = 0.0,
        params_for_pop: bool = False,
        init: dict = {},
    ):
        # Create the arguments
        parameters = f"""
            C              = {C} {': population' if params_for_pop else ''}
            k              = {k} {': population' if params_for_pop else ''}
            v_r            = {v_r} {': population' if params_for_pop else ''}
            v_t            = {v_t} {': population' if params_for_pop else ''}
            a              = {a} {': population' if params_for_pop else ''}
            b              = {b} {': population' if params_for_pop else ''}
            c              = {c} {': population' if params_for_pop else ''}
            d              = {d} {': population' if params_for_pop else ''}
            v_peak         = {v_peak} {': population' if params_for_pop else ''}
            tau_ampa       = {tau_ampa} {': population' if params_for_pop else ''}
            tau_gaba       = {tau_gaba} {': population' if params_for_pop else ''}
            E_ampa         = {E_ampa} {': population' if params_for_pop else ''}
            E_gaba         = {E_gaba} {': population' if params_for_pop else ''}
            I_app          = {I_app} # pA
            base_mean      = {base_mean}
            base_noise     = {base_noise}
            rate_base_noise = {rate_base_noise}
        """

        syn = _syn_default
        i_v = f"I_app {_I_syn} + I_base"
        prefix = _I_base_noise

        # get equations
        equations = _get_equation_izhikevich_2007(syn=syn, i_v=i_v, prefix=prefix)

        # set initial values
        equations = _set_init(equations, init)

        super().__init__(
            parameters=parameters,
            equations=equations,
            spike="v >= v_peak",
            reset="""
                v = c
                u = u + d
            """,
            name="Izhikevich2007_noisy_base",
            description="""
                Standard neuron model from Izhikevich (2007) with additional
                conductance based synapses for AMPA and GABA currents and noisy
                baseline current.
            """,
        )

        # For reporting
        self._instantiated.append(True)

Izhikevich2007FsiNoisyAmpa #

Bases: Neuron

TEMPLATE

Izhikevich (2007)-like neuron model for fast-spiking neurons, with conductance-based AMPA and GABA synapses with noise in the AMPA conductance.

Parameters:

Name Type Description Default
C float

Membrane capacitance.

 20.0
k float

Scaling factor for the quadratic term in the membrane potential.

 1.0
v_r float

Resting membrane potential.

 -55.0
v_t float

Instantaneous activation threshold potential.

 -40.0
v_b float

Instantaneous activation threshold potential for the recovery variable u.

 -55.0
a float

Time scale of the recovery variable u.

 0.1
b float

Sensitivity of the recovery variable u to the the membrane potential v.

 -2.0
c float

After-spike reset value of the membrane potential.

 -50.0
d float

After-spike change of the recovery variable u.

 2.0
v_peak float

Spike cut-off value for the membrane potential.

 25.0
I_app float

External applied input current.

 0.0
tau_ampa float

Time constant of the AMPA synapse.

 2.0
tau_gaba float

Time constant of the GABA synapse.

 5.0
E_ampa float

Reversal potential of the AMPA synapse.

 0.0
E_gaba float

Reversal potential of the GABA synapse.

 -80.0
increase_noise float

Increase of AMPA conductance due to noise (equal to a Poisson distributed spike train as input).

 0.0
rates_noise float

Rate of the noise in the AMPA conductance.

 0.0
params_for_pop bool

If True, the parameters are population-wide and not neuron-specific.

 False
init dict

Initial values for the variables.

 {}
Variables to record
  • g_ampa
  • g_gaba
  • I_v
  • v
  • u
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/izhikevich_2007_like_nm.py 
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class Izhikevich2007FsiNoisyAmpa(Neuron):
    """
    TEMPLATE

    [Izhikevich (2007)](https://isbnsearch.org/isbn/9780262090438)-like neuron model
    for fast-spiking neurons, with conductance-based AMPA and GABA synapses with noise
    in the AMPA conductance.

    Parameters:
        C (float, optional):
            Membrane capacitance.
        k (float, optional):
            Scaling factor for the quadratic term in the membrane potential.
        v_r (float, optional):
            Resting membrane potential.
        v_t (float, optional):
            Instantaneous activation threshold potential.
        v_b (float, optional):
            Instantaneous activation threshold potential for the recovery variable u.
        a (float, optional):
            Time scale of the recovery variable u.
        b (float, optional):
            Sensitivity of the recovery variable u to the the membrane potential v.
        c (float, optional):
            After-spike reset value of the membrane potential.
        d (float, optional):
            After-spike change of the recovery variable u.
        v_peak (float, optional):
            Spike cut-off value for the membrane potential.
        I_app (float, optional):
            External applied input current.
        tau_ampa (float, optional):
            Time constant of the AMPA synapse.
        tau_gaba (float, optional):
            Time constant of the GABA synapse.
        E_ampa (float, optional):
            Reversal potential of the AMPA synapse.
        E_gaba (float, optional):
            Reversal potential of the GABA synapse.
        increase_noise (float, optional):
            Increase of AMPA conductance due to noise (equal to a Poisson distributed
            spike train as input).
        rates_noise (float, optional):
            Rate of the noise in the AMPA conductance.
        params_for_pop (bool, optional):
            If True, the parameters are population-wide and not neuron-specific.
        init (dict, optional):
            Initial values for the variables.

    Variables to record:
        - g_ampa
        - g_gaba
        - I_v
        - v
        - u
        - r
    """

    # For reporting
    _instantiated = []

    def __init__(
        self,
        C: float = 20.0,
        k: float = 1.0,
        v_r: float = -55.0,
        v_t: float = -40.0,
        v_b: float = -55.0,
        a: float = 0.1,
        b: float = -2.0,
        c: float = -50.0,
        d: float = 2.0,
        v_peak: float = 25.0,
        I_app: float = 0.0,
        tau_ampa: float = 2.0,
        tau_gaba: float = 5.0,
        E_ampa: float = 0.0,
        E_gaba: float = -80.0,
        increase_noise: float = 0.0,
        rates_noise: float = 0.0,
        params_for_pop: bool = False,
        init: dict = {},
    ):
        # Create the arguments
        parameters = f"""
            C              = {C} {': population' if params_for_pop else ''}
            k              = {k} {': population' if params_for_pop else ''}
            v_r            = {v_r} {': population' if params_for_pop else ''}
            v_t            = {v_t} {': population' if params_for_pop else ''}
            v_b            = {v_b} {': population' if params_for_pop else ''}
            a              = {a} {': population' if params_for_pop else ''}
            b              = {b} {': population' if params_for_pop else ''}
            c              = {c} {': population' if params_for_pop else ''}
            d              = {d} {': population' if params_for_pop else ''}
            v_peak         = {v_peak} {': population' if params_for_pop else ''}
            tau_ampa       = {tau_ampa} {': population' if params_for_pop else ''}
            tau_gaba       = {tau_gaba} {': population' if params_for_pop else ''}
            E_ampa         = {E_ampa} {': population' if params_for_pop else ''}
            E_gaba         = {E_gaba} {': population' if params_for_pop else ''}
            I_app          = {I_app} # pA
            increase_noise = {increase_noise} {': population' if params_for_pop else ''}
            rates_noise    = {rates_noise}
        """

        syn = _syn_noisy
        i_v = f"I_app {_I_syn}"
        du = "if v<v_b: -a * u else: a * (b * (v - v_b)**3 - u)"

        # get equations
        equations = _get_equation_izhikevich_2007(syn=syn, i_v=i_v, du=du)

        # set initial values
        equations = _set_init(equations, init)

        super().__init__(
            parameters=parameters,
            equations=equations,
            spike="v >= v_peak",
            reset="""
                v = c
                u = u + d
            """,
            name="Izhikevich2007_FSI_noisy_AMPA",
            description="""
                Standard neuron model from Izhikevich (2007) with additional
                conductance based synapses for AMPA and GABA currents with noise
                in AMPA conductance.
            """,
        )

        # For reporting
        self._instantiated.append(True)

Izhikevich2007CorbitFsiNoisyAmpa #

Bases: Neuron

TEMPLATE

Izhikevich (2007)-like neuron model with conductance-based AMPA and GABA synapses with noise in the AMPA conductance. Additional slow currents were added to fit the striatal FSI neuron model from Corbit et al. (2016). The additional currents should allow the neuron to produce late spiking.

Parameters:

Name Type Description Default
C float

Membrane capacitance.

 20.0
k float

Scaling factor for the quadratic term in the membrane potential.

 1.0
b_n float

Sensitivity of the slow current n to the difference between the slow current s and the recovery variable u.

 0.1
a_s float

Time scale of the slow current s.

 0.1
a_n float

Time scale of the slow current n.

 0.1
v_r float

Resting membrane potential.

 -55.0
v_t float

Instantaneous activation threshold potential.

 -40.0
a float

Time scale of the recovery variable u.

 0.1
b float

Sensitivity of the recovery variable u to the the membrane potential v.

 -2.0
c float

After-spike reset value of the membrane potential.

 -50.0
d float

After-spike change of the recovery variable u.

 2.0
v_peak float

Spike cut-off value for the membrane potential.

 25.0
nonlin float

Nonlinearity of the input current. (1.0 = linear, 2.0 = square, etc.)

 0.1
I_app float

External applied input current.

 0.0
tau_ampa float

Time constant of the AMPA synapse.

 2.0
tau_gaba float

Time constant of the GABA synapse.

 5.0
E_ampa float

Reversal potential of the AMPA synapse.

 0.0
E_gaba float

Reversal potential of the GABA synapse.

 -80.0
increase_noise float

Increase of AMPA conductance due to noise (equal to a Poisson distributed spike train as input).

 0.0
rates_noise float

Rate of the noise in the AMPA conductance.

 0.0
params_for_pop bool

If True, the parameters are population-wide and not neuron-specific.

 False
init dict

Initial values for the variables.

 {}
Variables to record
  • g_ampa
  • g_gaba
  • I_v
  • v
  • u
  • s
  • n
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/izhikevich_2007_like_nm.py 
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class Izhikevich2007CorbitFsiNoisyAmpa(Neuron):
    """
    TEMPLATE

    [Izhikevich (2007)](https://isbnsearch.org/isbn/9780262090438)-like neuron model
    with conductance-based AMPA and GABA synapses with noise in the AMPA conductance.
    Additional slow currents were added to fit the striatal FSI neuron model from
    [Corbit et al. (2016)](https://doi.org/10.1523/JNEUROSCI.0339-16.2016). The
    additional currents should allow the neuron to produce late spiking.

    Parameters:
        C (float, optional):
            Membrane capacitance.
        k (float, optional):
            Scaling factor for the quadratic term in the membrane potential.
        b_n (float, optional):
            Sensitivity of the slow current n to the difference between the slow current
            s and the recovery variable u.
        a_s (float, optional):
            Time scale of the slow current s.
        a_n (float, optional):
            Time scale of the slow current n.
        v_r (float, optional):
            Resting membrane potential.
        v_t (float, optional):
            Instantaneous activation threshold potential.
        a (float, optional):
            Time scale of the recovery variable u.
        b (float, optional):
            Sensitivity of the recovery variable u to the the membrane potential v.
        c (float, optional):
            After-spike reset value of the membrane potential.
        d (float, optional):
            After-spike change of the recovery variable u.
        v_peak (float, optional):
            Spike cut-off value for the membrane potential.
        nonlin (float, optional):
            Nonlinearity of the input current. (1.0 = linear, 2.0 = square, etc.)
        I_app (float, optional):
            External applied input current.
        tau_ampa (float, optional):
            Time constant of the AMPA synapse.
        tau_gaba (float, optional):
            Time constant of the GABA synapse.
        E_ampa (float, optional):
            Reversal potential of the AMPA synapse.
        E_gaba (float, optional):
            Reversal potential of the GABA synapse.
        increase_noise (float, optional):
            Increase of AMPA conductance due to noise (equal to a Poisson distributed
            spike train as input).
        rates_noise (float, optional):
            Rate of the noise in the AMPA conductance.
        params_for_pop (bool, optional):
            If True, the parameters are population-wide and not neuron-specific.
        init (dict, optional):
            Initial values for the variables.

    Variables to record:
        - g_ampa
        - g_gaba
        - I_v
        - v
        - u
        - s
        - n
        - r
    """

    # For reporting
    _instantiated = []

    def __init__(
        self,
        C: float = 20.0,
        k: float = 1.0,
        b_n: float = 0.1,
        a_s: float = 0.1,
        a_n: float = 0.1,
        v_r: float = -55.0,
        v_t: float = -40.0,
        a: float = 0.1,
        b: float = -2.0,
        c: float = -50.0,
        d: float = 2.0,
        v_peak: float = 25.0,
        nonlin: float = 0.1,
        I_app: float = 0.0,
        tau_ampa: float = 2.0,
        tau_gaba: float = 5.0,
        E_ampa: float = 0.0,
        E_gaba: float = -80.0,
        increase_noise: float = 0.0,
        rates_noise: float = 0.0,
        params_for_pop: bool = False,
        init: dict = {},
    ):
        # Create the arguments
        parameters = f"""
            C              = {C} {': population' if params_for_pop else ''}
            k              = {k} {': population' if params_for_pop else ''}
            b_n            = {b_n} {': population' if params_for_pop else ''}
            a_s            = {a_s} {': population' if params_for_pop else ''}
            a_n            = {a_n} {': population' if params_for_pop else ''}
            v_r            = {v_r} {': population' if params_for_pop else ''}
            v_t            = {v_t} {': population' if params_for_pop else ''}
            a              = {a} {': population' if params_for_pop else ''}
            b              = {b} {': population' if params_for_pop else ''}
            c              = {c} {': population' if params_for_pop else ''}
            d              = {d} {': population' if params_for_pop else ''}
            v_peak         = {v_peak} {': population' if params_for_pop else ''}
            nonlin         = {nonlin} {': population' if params_for_pop else ''}
            tau_ampa       = {tau_ampa} {': population' if params_for_pop else ''}
            tau_gaba       = {tau_gaba} {': population' if params_for_pop else ''}
            E_ampa         = {E_ampa} {': population' if params_for_pop else ''}
            E_gaba         = {E_gaba} {': population' if params_for_pop else ''}
            I_app          = {I_app} # pA
            increase_noise = {increase_noise} {': population' if params_for_pop else ''}
            rates_noise    = {rates_noise}
        """

        syn = _syn_noisy
        i_v = f"root_func(I_app {_I_syn}, nonlin) - n"
        affix = """
            ds/dt     = a_s*(pos(u)**0.1 - s)
            dn/dt     = a_n*(b_n*(pos(u)**0.1-s) - n)
        """

        # get equations
        equations = _get_equation_izhikevich_2007(syn=syn, i_v=i_v, affix=affix)

        # set initial values
        equations = _set_init(equations, init)

        super().__init__(
            parameters=parameters,
            equations=equations,
            functions="""
                root_func(x,y)=((abs(x))**(1/y))/((x+1e-20)/(abs(x)+ 1e-20))
            """,
            spike="v >= v_peak",
            reset="""
                v = c
                u = u + d
            """,
            name="Izhikevich2007_Corbit_FSI_noisy_AMPA",
            description="""
                Standard neuron model from Izhikevich (2007) with additional
                conductance based synapses for AMPA and GABA currents with noise
                in AMPA conductance. Additional slow currents were added to fit
                the striatal FSI neuron model from Corbit et al. (2016).
            """,
        )

        # For reporting
        self._instantiated.append(True)

Izhikevich2007CorbitFsiNoisyBase #

Bases: Neuron

TEMPLATE

Izhikevich (2007)-like neuron model with conductance-based AMPA and GABA synapses with noise in the baseline current. Additional slow currents were added to fit the striatal FSI neuron model from Corbit et al. (2016). The additional currents should allow the neuron to produce late spiking.

Parameters:

Name Type Description Default
C float

Membrane capacitance.

 20.0
k float

Scaling factor for the quadratic term in the membrane potential.

 1.0
b_n float

Sensitivity of the slow current n to the difference between the slow current s and the recovery variable u.

 0.1
a_s float

Time scale of the slow current s.

 0.1
a_n float

Time scale of the slow current n.

 0.1
v_r float

Resting membrane potential.

 -55.0
v_t float

Instantaneous activation threshold potential.

 -40.0
a float

Time scale of the recovery variable u.

 0.1
b float

Sensitivity of the recovery variable u to the the membrane potential v.

 -2.0
c float

After-spike reset value of the membrane potential.

 -50.0
d float

After-spike change of the recovery variable u.

 2.0
v_peak float

Spike cut-off value for the membrane potential.

 25.0
nonlin float

Nonlinearity of the input current. (1.0 = linear, 2.0 = square, etc.)

 0.1
I_app float

External applied input current.

 0.0
tau_ampa float

Time constant of the AMPA synapse.

 2.0
tau_gaba float

Time constant of the GABA synapse.

 5.0
E_ampa float

Reversal potential of the AMPA synapse.

 0.0
E_gaba float

Reversal potential of the GABA synapse.

 -80.0
base_mean float

Mean of the baseline current.

 0.0
base_noise float

Standard deviation of the baseline current noise.

 0.0
rate_base_noise float

Rate of the noise update (Poisson distributed) in the baseline current.

 0.0
params_for_pop bool

If True, the parameters are population-wide and not neuron-specific.

 False
init dict

Initial values for the variables.

 {}
Variables to record
  • offset_base
  • I_base
  • g_ampa
  • g_gaba
  • I_v
  • v
  • u
  • s
  • n
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/izhikevich_2007_like_nm.py 
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class Izhikevich2007CorbitFsiNoisyBase(Neuron):
    """
    TEMPLATE

    [Izhikevich (2007)](https://isbnsearch.org/isbn/9780262090438)-like neuron model
    with conductance-based AMPA and GABA synapses with noise in the baseline current.
    Additional slow currents were added to fit the striatal FSI neuron model from
    [Corbit et al. (2016)](https://doi.org/10.1523/JNEUROSCI.0339-16.2016). The
    additional currents should allow the neuron to produce late spiking.

    Parameters:
        C (float, optional):
            Membrane capacitance.
        k (float, optional):
            Scaling factor for the quadratic term in the membrane potential.
        b_n (float, optional):
            Sensitivity of the slow current n to the difference between the slow current
            s and the recovery variable u.
        a_s (float, optional):
            Time scale of the slow current s.
        a_n (float, optional):
            Time scale of the slow current n.
        v_r (float, optional):
            Resting membrane potential.
        v_t (float, optional):
            Instantaneous activation threshold potential.
        a (float, optional):
            Time scale of the recovery variable u.
        b (float, optional):
            Sensitivity of the recovery variable u to the the membrane potential v.
        c (float, optional):
            After-spike reset value of the membrane potential.
        d (float, optional):
            After-spike change of the recovery variable u.
        v_peak (float, optional):
            Spike cut-off value for the membrane potential.
        nonlin (float, optional):
            Nonlinearity of the input current. (1.0 = linear, 2.0 = square, etc.)
        I_app (float, optional):
            External applied input current.
        tau_ampa (float, optional):
            Time constant of the AMPA synapse.
        tau_gaba (float, optional):
            Time constant of the GABA synapse.
        E_ampa (float, optional):
            Reversal potential of the AMPA synapse.
        E_gaba (float, optional):
            Reversal potential of the GABA synapse.
        base_mean (float, optional):
            Mean of the baseline current.
        base_noise (float, optional):
            Standard deviation of the baseline current noise.
        rate_base_noise (float, optional):
            Rate of the noise update (Poisson distributed) in the baseline current.
        params_for_pop (bool, optional):
            If True, the parameters are population-wide and not neuron-specific.
        init (dict, optional):
            Initial values for the variables.

    Variables to record:
        - offset_base
        - I_base
        - g_ampa
        - g_gaba
        - I_v
        - v
        - u
        - s
        - n
        - r
    """

    # For reporting
    _instantiated = []

    def __init__(
        self,
        C: float = 20.0,
        k: float = 1.0,
        b_n: float = 0.1,
        a_s: float = 0.1,
        a_n: float = 0.1,
        v_r: float = -55.0,
        v_t: float = -40.0,
        a: float = 0.1,
        b: float = -2.0,
        c: float = -50.0,
        d: float = 2.0,
        v_peak: float = 25.0,
        nonlin: float = 0.1,
        I_app: float = 0.0,
        tau_ampa: float = 2.0,
        tau_gaba: float = 5.0,
        E_ampa: float = 0.0,
        E_gaba: float = -80.0,
        base_mean: float = 0.0,
        base_noise: float = 0.0,
        rate_base_noise: float = 0.0,
        params_for_pop: bool = False,
        init: dict = {},
    ):
        # Create the arguments
        parameters = f"""
            C              = {C} {': population' if params_for_pop else ''}
            k              = {k} {': population' if params_for_pop else ''}
            b_n            = {b_n} {': population' if params_for_pop else ''}
            a_s            = {a_s} {': population' if params_for_pop else ''}
            a_n            = {a_n} {': population' if params_for_pop else ''}
            v_r            = {v_r} {': population' if params_for_pop else ''}
            v_t            = {v_t} {': population' if params_for_pop else ''}
            a              = {a} {': population' if params_for_pop else ''}
            b              = {b} {': population' if params_for_pop else ''}
            c              = {c} {': population' if params_for_pop else ''}
            d              = {d} {': population' if params_for_pop else ''}
            v_peak         = {v_peak} {': population' if params_for_pop else ''}
            nonlin         = {nonlin} {': population' if params_for_pop else ''}
            tau_ampa       = {tau_ampa} {': population' if params_for_pop else ''}
            tau_gaba       = {tau_gaba} {': population' if params_for_pop else ''}
            E_ampa         = {E_ampa} {': population' if params_for_pop else ''}
            E_gaba         = {E_gaba} {': population' if params_for_pop else ''}
            I_app          = {I_app} # pA
            base_mean      = {base_mean}
            base_noise     = {base_noise}
            rate_base_noise = {rate_base_noise}
        """

        syn = _syn_default
        i_v = f"root_func(I_app {_I_syn}, nonlin) - n + I_base"
        prefix = _I_base_noise
        affix = """
            ds/dt     = a_s*(pos(u)**0.1 - s)
            dn/dt     = a_n*(b_n*(pos(u)**0.1-s) - n)
        """

        # get equations
        equations = _get_equation_izhikevich_2007(
            syn=syn, i_v=i_v, prefix=prefix, affix=affix
        )

        # set initial values
        equations = _set_init(equations, init)

        super().__init__(
            parameters=parameters,
            equations=equations,
            functions="""
                root_func(x,y)=((abs(x))**(1/y))/((x+1e-20)/(abs(x)+ 1e-20))
            """,
            spike="v >= v_peak",
            reset="""
                v = c
                u = u + d
            """,
            name="Izhikevich2007_Corbit_FSI_noisy_base",
            description="""
                Standard neuron model from Izhikevich (2007) with additional
                conductance based synapses for AMPA and GABA currents with noise
                in the baseline current. Additional slow currents were added to fit
                the striatal FSI neuron model from Corbit et al. (2016).
            """,
        )

        # For reporting
        self._instantiated.append(True)

Izhikevich2007NoisyAmpaOscillating #

Bases: Neuron

TEMPLATE

Izhikevich (2007)-like neuron model with conductance-based AMPA and GABA synapses with noise in the AMPA conductance. An additional oscillating current was added to the model.

Parameters:

Name Type Description Default
C float

Membrane capacitance.

 20.0
k float

Scaling factor for the quadratic term in the membrane potential.

 1.0
v_r float

Resting membrane potential.

 -55.0
v_t float

Instantaneous activation threshold potential.

 -40.0
a float

Time scale of the recovery variable u.

 0.1
b float

Sensitivity of the recovery variable u to the the membrane potential v.

 -2.0
c float

After-spike reset value of the membrane potential.

 -50.0
d float

After-spike change of the recovery variable u.

 2.0
v_peak float

Spike cut-off value for the membrane potential.

 25.0
I_app float

External applied input current.

 0.0
tau_ampa float

Time constant of the AMPA synapse.

 2.0
tau_gaba float

Time constant of the GABA synapse.

 5.0
E_ampa float

Reversal potential of the AMPA synapse.

 0.0
E_gaba float

Reversal potential of the GABA synapse.

 -80.0
increase_noise float

Increase of AMPA conductance due to noise (equal to a Poisson distributed spike train as input).

 0.0
rates_noise float

Rate of the noise in the AMPA conductance.

 0.0
freq float

Frequency of the oscillating current.

 0.0
amp float

Amplitude of the oscillating current.

 300.0
params_for_pop bool

If True, the parameters are population-wide and not neuron-specific.

 False
init dict

Initial values for the variables.

 {}
Variables to record
  • osc
  • g_ampa
  • g_gaba
  • I_v
  • v
  • u
  • r
Source code in src/CompNeuroPy/neuron_models/final_models/izhikevich_2007_like_nm.py 
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class Izhikevich2007NoisyAmpaOscillating(Neuron):
    """
    TEMPLATE

    [Izhikevich (2007)](https://isbnsearch.org/isbn/9780262090438)-like neuron model
    with conductance-based AMPA and GABA synapses with noise in the AMPA conductance.
    An additional oscillating current was added to the model.

    Parameters:
        C (float, optional):
            Membrane capacitance.
        k (float, optional):
            Scaling factor for the quadratic term in the membrane potential.
        v_r (float, optional):
            Resting membrane potential.
        v_t (float, optional):
            Instantaneous activation threshold potential.
        a (float, optional):
            Time scale of the recovery variable u.
        b (float, optional):
            Sensitivity of the recovery variable u to the the membrane potential v.
        c (float, optional):
            After-spike reset value of the membrane potential.
        d (float, optional):
            After-spike change of the recovery variable u.
        v_peak (float, optional):
            Spike cut-off value for the membrane potential.
        I_app (float, optional):
            External applied input current.
        tau_ampa (float, optional):
            Time constant of the AMPA synapse.
        tau_gaba (float, optional):
            Time constant of the GABA synapse.
        E_ampa (float, optional):
            Reversal potential of the AMPA synapse.
        E_gaba (float, optional):
            Reversal potential of the GABA synapse.
        increase_noise (float, optional):
            Increase of AMPA conductance due to noise (equal to a Poisson distributed
            spike train as input).
        rates_noise (float, optional):
            Rate of the noise in the AMPA conductance.
        freq (float, optional):
            Frequency of the oscillating current.
        amp (float, optional):
            Amplitude of the oscillating current.
        params_for_pop (bool, optional):
            If True, the parameters are population-wide and not neuron-specific.
        init (dict, optional):
            Initial values for the variables.

    Variables to record:
        - osc
        - g_ampa
        - g_gaba
        - I_v
        - v
        - u
        - r
    """

    # For reporting
    _instantiated = []

    def __init__(
        self,
        C: float = 20.0,
        k: float = 1.0,
        v_r: float = -55.0,
        v_t: float = -40.0,
        a: float = 0.1,
        b: float = -2.0,
        c: float = -50.0,
        d: float = 2.0,
        v_peak: float = 25.0,
        I_app: float = 0.0,
        tau_ampa: float = 2.0,
        tau_gaba: float = 5.0,
        E_ampa: float = 0.0,
        E_gaba: float = -80.0,
        increase_noise: float = 0.0,
        rates_noise: float = 0.0,
        freq: float = 0.0,
        amp: float = 300.0,
        params_for_pop: bool = False,
        init: dict = {},
    ):
        # Create the arguments
        parameters = f"""
            C              = {C} {': population' if params_for_pop else ''}
            k              = {k} {': population' if params_for_pop else ''}
            v_r            = {v_r} {': population' if params_for_pop else ''}
            v_t            = {v_t} {': population' if params_for_pop else ''}
            a              = {a} {': population' if params_for_pop else ''}
            b              = {b} {': population' if params_for_pop else ''}
            c              = {c} {': population' if params_for_pop else ''}
            d              = {d} {': population' if params_for_pop else ''}
            v_peak         = {v_peak} {': population' if params_for_pop else ''}
            tau_ampa       = {tau_ampa} {': population' if params_for_pop else ''}
            tau_gaba       = {tau_gaba} {': population' if params_for_pop else ''}
            E_ampa         = {E_ampa} {': population' if params_for_pop else ''}
            E_gaba         = {E_gaba} {': population' if params_for_pop else ''}
            I_app          = {I_app} # pA
            increase_noise = {increase_noise} {': population' if params_for_pop else ''}
            rates_noise    = {rates_noise}
            freq           = {freq}
            amp            = {amp}
        """

        syn = _syn_noisy
        i_v = f"I_app {_I_syn} + osc"
        prefix = "osc = amp * sin(t * 2 * pi * (freq  /1000))"

        # get equations
        equations = _get_equation_izhikevich_2007(syn=syn, i_v=i_v, prefix=prefix)

        # set initial values
        equations = _set_init(equations, init)

        super().__init__(
            parameters=parameters,
            equations=equations,
            spike="v >= v_peak",
            reset="""
                v = c
                u = u + d
            """,
            name="Izhikevich2007_noisy_AMPA_oscillating",
            description="""
                Standard neuron model from Izhikevich (2007) with additional
                conductance based synapses for AMPA and GABA currents with noise
                in AMPA conductance. An additional oscillating current was added
                to the model.
            """,
        )

        # For reporting
        self._instantiated.append(True)