# -*- coding: utf-8 -*-
from typing import Union, Callable, Optional
from jax.lax import stop_gradient
import brainpy.math as bm
from brainpy._src.context import share
from brainpy._src.dyn.neurons import lif
from brainpy._src.dyn.base import NeuDyn
from brainpy._src.initialize import (ZeroInit,
OneInit,
Initializer,
parameter,
variable_,
noise as init_noise)
from brainpy._src.integrators import sdeint, odeint, JointEq
from brainpy.check import is_initializer, is_callable, is_subclass
from brainpy.types import Shape, ArrayType
__all__ = [
'LeakyIntegrator',
'LIF',
'ExpIF',
'AdExIF',
'QuaIF',
'AdQuaIF',
'GIF',
'ALIFBellec2020',
'Izhikevich',
'HindmarshRose',
'FHN',
]
[docs]
class LeakyIntegrator(NeuDyn):
r"""Leaky Integrator Model.
**Model Descriptions**
This class implements a leaky integrator model, in which its dynamics is
given by:
.. math::
\tau \frac{dV}{dt} = - (V(t) - V_{rest}) + RI(t)
where :math:`V` is the membrane potential, :math:`V_{rest}` is the resting
membrane potential, :math:`\tau` is the time constant, and :math:`R` is the
resistance.
Parameters
----------
size: sequence of int, int
The size of the neuron group.
V_rest: float, ArrayType, Initializer, callable
Resting membrane potential.
R: float, ArrayType, Initializer, callable
Membrane resistance.
tau: float, ArrayType, Initializer, callable
Membrane time constant.
V_initializer: ArrayType, Initializer, callable
The initializer of membrane potential.
noise: ArrayType, Initializer, callable
The noise added onto the membrane potential
method: str
The numerical integration method.
name: str
The group name.
"""
[docs]
def __init__(
self,
# neuron group size
size: Shape,
keep_size: bool = False,
# neuron parameters
V_rest: Union[float, ArrayType, Initializer, Callable] = 0.,
R: Union[float, ArrayType, Initializer, Callable] = 1.,
tau: Union[float, ArrayType, Initializer, Callable] = 10.,
V_initializer: Union[Initializer, Callable, ArrayType] = ZeroInit(),
noise: Union[float, ArrayType, Initializer, Callable] = None,
# other parameter
input_var: bool = True,
name: str = None,
mode: bm.Mode = None,
method: str = 'exp_auto',
):
super().__init__(size=size,
mode=mode,
keep_size=keep_size,
name=name)
is_subclass(self.mode, (bm.TrainingMode, bm.NonBatchingMode))
# parameters
self.V_rest = parameter(V_rest, self.varshape, allow_none=False)
self.tau = parameter(tau, self.varshape, allow_none=False)
self.R = parameter(R, self.varshape, allow_none=False)
self.noise = init_noise(noise, self.varshape)
self.input_var = input_var
# initializers
self._V_initializer = is_initializer(V_initializer)
# integral
if self.noise is None:
self.integral = odeint(method=method, f=self.derivative)
else:
self.integral = sdeint(method=method, f=self.derivative, g=self.noise)
# variables
self.reset_state(self.mode)
def derivative(self, V, t, I_ext):
return (-V + self.V_rest + self.R * I_ext) / self.tau
def reset_state(self, batch_size=None):
self.V = variable_(self._V_initializer, self.varshape, batch_size)
if self.input_var:
self.input = variable_(bm.zeros, self.varshape, batch_size)
def update(self, x=None):
t = share.load('t')
dt = share.load('dt')
if self.input_var:
if x is not None:
self.input += x
x = self.input.value
else:
x = 0. if x is None else x
self.V.value = self.integral(self.V.value, t, x, dt)
return self.V.value
def clear_input(self):
if self.input_var:
self.input[:] = 0.
[docs]
class LIF(lif.LifRef):
r"""Leaky integrate-and-fire neuron model.
**Model Descriptions**
The formal equations of a LIF model [1]_ is given by:
.. math::
\tau \frac{dV}{dt} = - (V(t) - V_{rest}) + RI(t) \\
\text{after} \quad V(t) \gt V_{th}, V(t) = V_{reset} \quad
\text{last} \quad \tau_{ref} \quad \text{ms}
where :math:`V` is the membrane potential, :math:`V_{rest}` is the resting
membrane potential, :math:`V_{reset}` is the reset membrane potential,
:math:`V_{th}` is the spike threshold, :math:`\tau` is the time constant,
:math:`\tau_{ref}` is the refractory time period,
and :math:`I` is the time-variant synaptic inputs.
**Model Examples**
- `(Brette, Romain. 2004) LIF phase locking <https://brainpy-examples.readthedocs.io/en/latest/neurons/Romain_2004_LIF_phase_locking.html>`_
Parameters
----------
size: sequence of int, int
The size of the neuron group.
V_rest: float, ArrayType, Initializer, callable
Resting membrane potential.
V_reset: float, ArrayType, Initializer, callable
Reset potential after spike.
V_th: float, ArrayType, Initializer, callable
Threshold potential of spike.
R: float, ArrayType, Initializer, callable
Membrane resistance.
tau: float, ArrayType, Initializer, callable
Membrane time constant.
tau_ref: float, ArrayType, Initializer, callable
Refractory period length.(ms)
V_initializer: ArrayType, Initializer, callable
The initializer of membrane potential.
noise: ArrayType, Initializer, callable
The noise added onto the membrane potential
method: str
The numerical integration method.
name: str
The group name.
References
----------
.. [1] Abbott, Larry F. "Lapicque’s introduction of the integrate-and-fire model
neuron (1907)." Brain research bulletin 50, no. 5-6 (1999): 303-304.
"""
[docs]
def __init__(
self,
*args,
input_var: bool = True,
spike_fun: Callable = None,
**kwargs,
):
self.input_var = input_var
if spike_fun is not None:
kwargs['spk_fun'] = spike_fun
super().__init__(*args, **kwargs, init_var=False)
self.reset_state(self.mode)
def reset_state(self, batch_size=None):
super().reset_state(batch_size)
if self.input_var:
self.input = variable_(bm.zeros, self.varshape, batch_size)
def update(self, x=None):
if self.input_var:
if x is not None:
self.input += x
x = self.input.value
else:
x = 0. if x is None else x
return super().update(x)
def clear_input(self):
if self.input_var:
self.input.value = bm.zeros_like(self.input)
[docs]
class ExpIF(lif.ExpIFRef):
r"""Exponential integrate-and-fire neuron model.
**Model Descriptions**
In the exponential integrate-and-fire model [1]_, the differential
equation for the membrane potential is given by
.. math::
\tau\frac{d V}{d t}= - (V-V_{rest}) + \Delta_T e^{\frac{V-V_T}{\Delta_T}} + RI(t), \\
\text{after} \, V(t) \gt V_{th}, V(t) = V_{reset} \, \text{last} \, \tau_{ref} \, \text{ms}
This equation has an exponential nonlinearity with "sharpness" parameter :math:`\Delta_{T}`
and "threshold" :math:`\vartheta_{rh}`.
The moment when the membrane potential reaches the numerical threshold :math:`V_{th}`
defines the firing time :math:`t^{(f)}`. After firing, the membrane potential is reset to
:math:`V_{rest}` and integration restarts at time :math:`t^{(f)}+\tau_{\rm ref}`,
where :math:`\tau_{\rm ref}` is an absolute refractory time.
If the numerical threshold is chosen sufficiently high, :math:`V_{th}\gg v+\Delta_T`,
its exact value does not play any role. The reason is that the upswing of the action
potential for :math:`v\gg v +\Delta_{T}` is so rapid, that it goes to infinity in
an incredibly short time. The threshold :math:`V_{th}` is introduced mainly for numerical
convenience. For a formal mathematical analysis of the model, the threshold can be pushed
to infinity.
The model was first introduced by Nicolas Fourcaud-Trocmé, David Hansel, Carl van Vreeswijk
and Nicolas Brunel [1]_. The exponential nonlinearity was later confirmed by Badel et al. [3]_.
It is one of the prominent examples of a precise theoretical prediction in computational
neuroscience that was later confirmed by experimental neuroscience.
Two important remarks:
- (i) The right-hand side of the above equation contains a nonlinearity
that can be directly extracted from experimental data [3]_. In this sense the exponential
nonlinearity is not an arbitrary choice but directly supported by experimental evidence.
- (ii) Even though it is a nonlinear model, it is simple enough to calculate the firing
rate for constant input, and the linear response to fluctuations, even in the presence
of input noise [4]_.
**Model Examples**
>>> import brainpy as bp
>>> group = bp.neurons.ExpIF(1)
>>> runner = bp.DSRunner(group, monitors=['V'], inputs=('input', 10.))
>>> runner.run(300., )
>>> bp.visualize.line_plot(runner.mon.ts, runner.mon.V, ylabel='V', show=True)
**Model Parameters**
============= ============== ======== ===================================================
**Parameter** **Init Value** **Unit** **Explanation**
------------- -------------- -------- ---------------------------------------------------
V_rest -65 mV Resting potential.
V_reset -68 mV Reset potential after spike.
V_th -30 mV Threshold potential of spike.
V_T -59.9 mV Threshold potential of generating action potential.
delta_T 3.48 \ Spike slope factor.
R 1 \ Membrane resistance.
tau 10 \ Membrane time constant. Compute by R * C.
tau_ref 1.7 \ Refractory period length.
============= ============== ======== ===================================================
**Model Variables**
================== ================= =========================================================
**Variables name** **Initial Value** **Explanation**
------------------ ----------------- ---------------------------------------------------------
V 0 Membrane potential.
input 0 External and synaptic input current.
spike False Flag to mark whether the neuron is spiking.
refractory False Flag to mark whether the neuron is in refractory period.
t_last_spike -1e7 Last spike time stamp.
================== ================= =========================================================
**References**
.. [1] Fourcaud-Trocmé, Nicolas, et al. "How spike generation
mechanisms determine the neuronal response to fluctuating
inputs." Journal of Neuroscience 23.37 (2003): 11628-11640.
.. [2] Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014).
Neuronal dynamics: From single neurons to networks and models
of cognition. Cambridge University Press.
.. [3] Badel, Laurent, Sandrine Lefort, Romain Brette, Carl CH Petersen,
Wulfram Gerstner, and Magnus JE Richardson. "Dynamic IV curves
are reliable predictors of naturalistic pyramidal-neuron voltage
traces." Journal of Neurophysiology 99, no. 2 (2008): 656-666.
.. [4] Richardson, Magnus JE. "Firing-rate response of linear and nonlinear
integrate-and-fire neurons to modulated current-based and
conductance-based synaptic drive." Physical Review E 76, no. 2 (2007): 021919.
.. [5] https://en.wikipedia.org/wiki/Exponential_integrate-and-fire
"""
[docs]
def __init__(
self,
*args,
input_var: bool = True,
noise: Union[float, ArrayType, Initializer, Callable] = None,
spike_fun: Callable = None,
**kwargs,
):
self.input_var = input_var
if spike_fun is not None:
kwargs['spk_fun'] = spike_fun
super().__init__(*args, **kwargs, init_var=False)
self.reset_state(self.mode)
def reset_state(self, batch_size=None):
super().reset_state(batch_size)
if self.input_var:
self.input = variable_(bm.zeros, self.varshape, batch_size)
def update(self, x=None):
if self.input_var:
if x is not None:
self.input += x
x = self.input.value
else:
x = 0. if x is None else x
return super().update(x)
def clear_input(self):
if self.input_var:
self.input.value = bm.zeros_like(self.input)
[docs]
class AdExIF(lif.AdExIFRef):
r"""Adaptive exponential integrate-and-fire neuron model.
**Model Descriptions**
The **adaptive exponential integrate-and-fire model**, also called AdEx, is a
spiking neuron model with two variables [1]_ [2]_.
.. math::
\begin{aligned}
\tau_m\frac{d V}{d t} &= - (V-V_{rest}) + \Delta_T e^{\frac{V-V_T}{\Delta_T}} - Rw + RI(t), \\
\tau_w \frac{d w}{d t} &=a(V-V_{rest}) - w
\end{aligned}
once the membrane potential reaches the spike threshold,
.. math::
V \rightarrow V_{reset}, \\
w \rightarrow w+b.
The first equation describes the dynamics of the membrane potential and includes
an activation term with an exponential voltage dependence. Voltage is coupled to
a second equation which describes adaptation. Both variables are reset if an action
potential has been triggered. The combination of adaptation and exponential voltage
dependence gives rise to the name Adaptive Exponential Integrate-and-Fire model.
The adaptive exponential integrate-and-fire model is capable of describing known
neuronal firing patterns, e.g., adapting, bursting, delayed spike initiation,
initial bursting, fast spiking, and regular spiking.
**Model Examples**
- `Examples for different firing patterns <https://brainpy-examples.readthedocs.io/en/latest/neurons/Gerstner_2005_AdExIF_model.html>`_
**Model Parameters**
============= ============== ======== ========================================================================================================================
**Parameter** **Init Value** **Unit** **Explanation**
------------- -------------- -------- ------------------------------------------------------------------------------------------------------------------------
V_rest -65 mV Resting potential.
V_reset -68 mV Reset potential after spike.
V_th -30 mV Threshold potential of spike and reset.
V_T -59.9 mV Threshold potential of generating action potential.
delta_T 3.48 \ Spike slope factor.
a 1 \ The sensitivity of the recovery variable :math:`u` to the sub-threshold fluctuations of the membrane potential :math:`v`
b 1 \ The increment of :math:`w` produced by a spike.
R 1 \ Membrane resistance.
tau 10 ms Membrane time constant. Compute by R * C.
tau_w 30 ms Time constant of the adaptation current.
tau_ref 0. ms Refractory time.
============= ============== ======== ========================================================================================================================
**Model Variables**
================== ================= =========================================================
**Variables name** **Initial Value** **Explanation**
------------------ ----------------- ---------------------------------------------------------
V 0 Membrane potential.
w 0 Adaptation current.
input 0 External and synaptic input current.
spike False Flag to mark whether the neuron is spiking.
refractory False Flag to mark whether the neuron is in refractory period.
t_last_spike -1e7 Last spike time stamp.
================== ================= =========================================================
**References**
.. [1] Fourcaud-Trocmé, Nicolas, et al. "How spike generation
mechanisms determine the neuronal response to fluctuating
inputs." Journal of Neuroscience 23.37 (2003): 11628-11640.
.. [2] http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model
"""
[docs]
def __init__(
self,
*args,
input_var: bool = True,
spike_fun: Callable = None,
**kwargs,
):
self.input_var = input_var
if spike_fun is not None:
kwargs['spk_fun'] = spike_fun
super().__init__(*args, **kwargs, init_var=False)
self.reset_state(self.mode)
def reset_state(self, batch_size=None):
super().reset_state(batch_size)
if self.input_var:
self.input = variable_(bm.zeros, self.varshape, batch_size)
def update(self, x=None):
if self.input_var:
if x is not None:
self.input += x
x = self.input.value
else:
x = 0. if x is None else x
return super().update(x)
def clear_input(self):
if self.input_var:
self.input.value = bm.zeros_like(self.input)
[docs]
class QuaIF(lif.QuaIFRef):
r"""Quadratic Integrate-and-Fire neuron model.
**Model Descriptions**
In contrast to physiologically accurate but computationally expensive
neuron models like the Hodgkin–Huxley model, the QIF model [1]_ seeks only
to produce **action potential-like patterns** and ignores subtleties
like gating variables, which play an important role in generating action
potentials in a real neuron. However, the QIF model is incredibly easy
to implement and compute, and relatively straightforward to study and
understand, thus has found ubiquitous use in computational neuroscience.
.. math::
\tau \frac{d V}{d t}=c(V-V_{rest})(V-V_c) + RI(t)
where the parameters are taken to be :math:`c` =0.07, and :math:`V_c = -50 mV` (Latham et al., 2000).
**Model Examples**
>>> import brainpy as bp
>>>
>>> group = bp.neurons.QuaIF(1,)
>>>
>>> runner = bp.DSRunner(group, monitors=['V'], inputs=('input', 20.))
>>> runner.run(duration=200.)
>>> bp.visualize.line_plot(runner.mon.ts, runner.mon.V, show=True)
**Model Parameters**
============= ============== ======== ========================================================================================================================
**Parameter** **Init Value** **Unit** **Explanation**
------------- -------------- -------- ------------------------------------------------------------------------------------------------------------------------
V_rest -65 mV Resting potential.
V_reset -68 mV Reset potential after spike.
V_th -30 mV Threshold potential of spike and reset.
V_c -50 mV Critical voltage for spike initiation. Must be larger than V_rest.
c .07 \ Coefficient describes membrane potential update. Larger than 0.
R 1 \ Membrane resistance.
tau 10 ms Membrane time constant. Compute by R * C.
tau_ref 0 ms Refractory period length.
============= ============== ======== ========================================================================================================================
**Model Variables**
================== ================= =========================================================
**Variables name** **Initial Value** **Explanation**
------------------ ----------------- ---------------------------------------------------------
V 0 Membrane potential.
input 0 External and synaptic input current.
spike False Flag to mark whether the neuron is spiking.
refractory False Flag to mark whether the neuron is in refractory period.
t_last_spike -1e7 Last spike time stamp.
================== ================= =========================================================
**References**
.. [1] P. E. Latham, B.J. Richmond, P. Nelson and S. Nirenberg
(2000) Intrinsic dynamics in neuronal networks. I. Theory.
J. Neurophysiology 83, pp. 808–827.
"""
[docs]
def __init__(
self,
*args,
input_var: bool = True,
spike_fun: Callable = None,
**kwargs,
):
self.input_var = input_var
if spike_fun is not None:
kwargs['spk_fun'] = spike_fun
super().__init__(*args, **kwargs, init_var=False)
self.reset_state(self.mode)
def reset_state(self, batch_size=None):
super().reset_state(batch_size)
if self.input_var:
self.input = variable_(bm.zeros, self.varshape, batch_size)
def update(self, x=None):
if self.input_var:
if x is not None:
self.input += x
x = self.input.value
else:
x = 0. if x is None else x
return super().update(x)
def clear_input(self):
if self.input_var:
self.input.value = bm.zeros_like(self.input)
[docs]
class AdQuaIF(lif.AdQuaIFRef):
r"""Adaptive quadratic integrate-and-fire neuron model.
**Model Descriptions**
The adaptive quadratic integrate-and-fire neuron model [1]_ is given by:
.. math::
\begin{aligned}
\tau_m \frac{d V}{d t}&=c(V-V_{rest})(V-V_c) - w + I(t), \\
\tau_w \frac{d w}{d t}&=a(V-V_{rest}) - w,
\end{aligned}
once the membrane potential reaches the spike threshold,
.. math::
V \rightarrow V_{reset}, \\
w \rightarrow w+b.
**Model Examples**
>>> import brainpy as bp
>>> group = bp.neurons.AdQuaIF(1, )
>>> runner = bp.DSRunner(group, monitors=['V', 'w'], inputs=('input', 30.))
>>> runner.run(300)
>>> fig, gs = bp.visualize.get_figure(2, 1, 3, 8)
>>> fig.add_subplot(gs[0, 0])
>>> bp.visualize.line_plot(runner.mon.ts, runner.mon.V, ylabel='V')
>>> fig.add_subplot(gs[1, 0])
>>> bp.visualize.line_plot(runner.mon.ts, runner.mon.w, ylabel='w', show=True)
**Model Parameters**
============= ============== ======== =======================================================
**Parameter** **Init Value** **Unit** **Explanation**
------------- -------------- -------- -------------------------------------------------------
V_rest -65 mV Resting potential.
V_reset -68 mV Reset potential after spike.
V_th -30 mV Threshold potential of spike and reset.
V_c -50 mV Critical voltage for spike initiation. Must be larger
than :math:`V_{rest}`.
a 1 \ The sensitivity of the recovery variable :math:`u` to
the sub-threshold fluctuations of the membrane
potential :math:`v`
b .1 \ The increment of :math:`w` produced by a spike.
c .07 \ Coefficient describes membrane potential update.
Larger than 0.
tau 10 ms Membrane time constant.
tau_w 10 ms Time constant of the adaptation current.
============= ============== ======== =======================================================
**Model Variables**
================== ================= ==========================================================
**Variables name** **Initial Value** **Explanation**
------------------ ----------------- ----------------------------------------------------------
V 0 Membrane potential.
w 0 Adaptation current.
input 0 External and synaptic input current.
spike False Flag to mark whether the neuron is spiking.
t_last_spike -1e7 Last spike time stamp.
================== ================= ==========================================================
**References**
.. [1] Izhikevich, E. M. (2004). Which model to use for cortical spiking
neurons?. IEEE transactions on neural networks, 15(5), 1063-1070.
.. [2] Touboul, Jonathan. "Bifurcation analysis of a general class of
nonlinear integrate-and-fire neurons." SIAM Journal on Applied
Mathematics 68, no. 4 (2008): 1045-1079.
"""
[docs]
def __init__(
self,
*args,
input_var: bool = True,
spike_fun: Callable = None,
**kwargs,
):
self.input_var = input_var
if spike_fun is not None:
kwargs['spk_fun'] = spike_fun
super().__init__(*args, **kwargs, init_var=False)
self.reset_state(self.mode)
def reset_state(self, batch_size=None):
super().reset_state(batch_size)
if self.input_var:
self.input = variable_(bm.zeros, self.varshape, batch_size)
def update(self, x=None):
if self.input_var:
if x is not None:
self.input += x
x = self.input.value
else:
x = 0. if x is None else x
return super().update(x)
def clear_input(self):
if self.input_var:
self.input.value = bm.zeros_like(self.input)
[docs]
class GIF(lif.GifRef):
r"""Generalized Integrate-and-Fire model.
**Model Descriptions**
The generalized integrate-and-fire model [1]_ is given by
.. math::
&\frac{d I_j}{d t} = - k_j I_j
&\frac{d V}{d t} = ( - (V - V_{rest}) + R\sum_{j}I_j + RI) / \tau
&\frac{d V_{th}}{d t} = a(V - V_{rest}) - b(V_{th} - V_{th\infty})
When :math:`V` meet :math:`V_{th}`, Generalized IF neuron fires:
.. math::
&I_j \leftarrow R_j I_j + A_j
&V \leftarrow V_{reset}
&V_{th} \leftarrow max(V_{th_{reset}}, V_{th})
Note that :math:`I_j` refers to arbitrary number of internal currents.
**Model Examples**
- `Detailed examples to reproduce different firing patterns <https://brainpy-examples.readthedocs.io/en/latest/neurons/Niebur_2009_GIF.html>`_
**Model Parameters**
============= ============== ======== ====================================================================
**Parameter** **Init Value** **Unit** **Explanation**
------------- -------------- -------- --------------------------------------------------------------------
V_rest -70 mV Resting potential.
V_reset -70 mV Reset potential after spike.
V_th_inf -50 mV Target value of threshold potential :math:`V_{th}` updating.
V_th_reset -60 mV Free parameter, should be larger than :math:`V_{reset}`.
R 20 \ Membrane resistance.
tau 20 ms Membrane time constant. Compute by :math:`R * C`.
a 0 \ Coefficient describes the dependence of
:math:`V_{th}` on membrane potential.
b 0.01 \ Coefficient describes :math:`V_{th}` update.
k1 0.2 \ Constant pf :math:`I1`.
k2 0.02 \ Constant of :math:`I2`.
R1 0 \ Free parameter.
Describes dependence of :math:`I_1` reset value on
:math:`I_1` value before spiking.
R2 1 \ Free parameter.
Describes dependence of :math:`I_2` reset value on
:math:`I_2` value before spiking.
A1 0 \ Free parameter.
A2 0 \ Free parameter.
============= ============== ======== ====================================================================
**Model Variables**
================== ================= =========================================================
**Variables name** **Initial Value** **Explanation**
------------------ ----------------- ---------------------------------------------------------
V -70 Membrane potential.
input 0 External and synaptic input current.
spike False Flag to mark whether the neuron is spiking.
V_th -50 Spiking threshold potential.
I1 0 Internal current 1.
I2 0 Internal current 2.
t_last_spike -1e7 Last spike time stamp.
================== ================= =========================================================
**References**
.. [1] Mihalaş, Ştefan, and Ernst Niebur. "A generalized linear
integrate-and-fire neural model produces diverse spiking
behaviors." Neural computation 21.3 (2009): 704-718.
.. [2] Teeter, Corinne, Ramakrishnan Iyer, Vilas Menon, Nathan
Gouwens, David Feng, Jim Berg, Aaron Szafer et al. "Generalized
leaky integrate-and-fire models classify multiple neuron types."
Nature communications 9, no. 1 (2018): 1-15.
"""
[docs]
def __init__(
self,
*args,
input_var: bool = True,
spike_fun: Callable = None,
**kwargs,
):
self.input_var = input_var
if spike_fun is not None:
kwargs['spk_fun'] = spike_fun
super().__init__(*args, **kwargs, init_var=False)
self.reset_state(self.mode)
def reset_state(self, batch_size=None):
super().reset_state(batch_size)
if self.input_var:
self.input = variable_(bm.zeros, self.varshape, batch_size)
def update(self, x=None):
if self.input_var:
if x is not None:
self.input += x
x = self.input.value
else:
x = 0. if x is None else x
return super().update(x)
def clear_input(self):
if self.input_var:
self.input.value = bm.zeros_like(self.input)
[docs]
class Izhikevich(lif.IzhikevichRef):
r"""The Izhikevich neuron model.
**Model Descriptions**
The dynamics of the Izhikevich neuron model [1]_ [2]_ is given by:
.. math ::
\frac{d V}{d t} &= 0.04 V^{2}+5 V+140-u+I
\frac{d u}{d t} &=a(b V-u)
.. math ::
\text{if} v \geq 30 \text{mV}, \text{then}
\begin{cases} v \leftarrow c \\
u \leftarrow u+d \end{cases}
**Model Examples**
- `Detailed examples to reproduce different firing patterns <https://brainpy-examples.readthedocs.io/en/latest/neurons/Izhikevich_2003_Izhikevich_model.html>`_
**Model Parameters**
============= ============== ======== ================================================================================
**Parameter** **Init Value** **Unit** **Explanation**
------------- -------------- -------- --------------------------------------------------------------------------------
a 0.02 \ It determines the time scale of
the recovery variable :math:`u`.
b 0.2 \ It describes the sensitivity of the
recovery variable :math:`u` to
the sub-threshold fluctuations of the
membrane potential :math:`v`.
c -65 \ It describes the after-spike reset value
of the membrane potential :math:`v` caused by
the fast high-threshold :math:`K^{+}`
conductance.
d 8 \ It describes after-spike reset of the
recovery variable :math:`u`
caused by slow high-threshold
:math:`Na^{+}` and :math:`K^{+}` conductance.
tau_ref 0 ms Refractory period length. [ms]
V_th 30 mV The membrane potential threshold.
============= ============== ======== ================================================================================
**Model Variables**
================== ================= =========================================================
**Variables name** **Initial Value** **Explanation**
------------------ ----------------- ---------------------------------------------------------
V -65 Membrane potential.
u 1 Recovery variable.
input 0 External and synaptic input current.
spike False Flag to mark whether the neuron is spiking.
refractory False Flag to mark whether the neuron is in refractory period.
t_last_spike -1e7 Last spike time stamp.
================== ================= =========================================================
**References**
.. [1] Izhikevich, Eugene M. "Simple model of spiking neurons." IEEE
Transactions on neural networks 14.6 (2003): 1569-1572.
.. [2] Izhikevich, Eugene M. "Which model to use for cortical spiking neurons?."
IEEE transactions on neural networks 15.5 (2004): 1063-1070.
"""
[docs]
def __init__(
self,
*args,
input_var: bool = True,
spike_fun: Callable = None,
**kwargs,
):
self.input_var = input_var
if spike_fun is not None:
kwargs['spk_fun'] = spike_fun
super().__init__(*args, **kwargs, init_var=False)
self.reset_state(self.mode)
def reset_state(self, batch_size=None, **kwargs):
super().reset_state(batch_size)
if self.input_var:
self.input = variable_(bm.zeros, self.varshape, batch_size)
def update(self, x=None):
if self.input_var:
if x is not None:
self.input += x
x = self.input.value
else:
x = 0. if x is None else x
return super().update(x)
def clear_input(self):
if self.input_var:
self.input.value = bm.zeros_like(self.input)
[docs]
class HindmarshRose(NeuDyn):
r"""Hindmarsh-Rose neuron model.
**Model Descriptions**
The Hindmarsh–Rose model [1]_ [2]_ of neuronal activity is aimed to study the
spiking-bursting behavior of the membrane potential observed in experiments
made with a single neuron.
The model has the mathematical form of a system of three nonlinear ordinary
differential equations on the dimensionless dynamical variables :math:`x(t)`,
:math:`y(t)`, and :math:`z(t)`. They read:
.. math::
\begin{aligned}
\frac{d V}{d t} &= y - a V^3 + b V^2 - z + I \\
\frac{d y}{d t} &= c - d V^2 - y \\
\frac{d z}{d t} &= r (s (V - V_{rest}) - z)
\end{aligned}
where :math:`a, b, c, d` model the working of the fast ion channels,
:math:`I` models the slow ion channels.
**Model Examples**
>>> import brainpy.math as bm
>>> import brainpy as bp
>>> import matplotlib.pyplot as plt
>>>
>>> bp.math.set_dt(dt=0.01)
>>> bp.ode.set_default_odeint('rk4')
>>>
>>> types = ['quiescence', 'spiking', 'bursting', 'irregular_spiking', 'irregular_bursting']
>>> bs = bm.array([1.0, 3.5, 2.5, 2.95, 2.8])
>>> Is = bm.array([2.0, 5.0, 3.0, 3.3, 3.7])
>>>
>>> # define neuron type
>>> group = bp.neurons.HindmarshRose(len(types), b=bs)
>>> runner = bp.DSRunner(group, monitors=['V'], inputs=['input', Is],)
>>> runner.run(1e3)
>>>
>>> fig, gs = bp.visualize.get_figure(row_num=3, col_num=2, row_len=3, col_len=5)
>>> for i, mode in enumerate(types):
>>> fig.add_subplot(gs[i // 2, i % 2])
>>> plt.plot(runner.mon.ts, runner.mon.V[:, i])
>>> plt.title(mode)
>>> plt.xlabel('Time [ms]')
>>> plt.show()
**Model Parameters**
============= ============== ========= ============================================================
**Parameter** **Init Value** **Unit** **Explanation**
------------- -------------- --------- ------------------------------------------------------------
a 1 \ Model parameter.
Fixed to a value best fit neuron activity.
b 3 \ Model parameter.
Allows the model to switch between bursting
and spiking, controls the spiking frequency.
c 1 \ Model parameter.
Fixed to a value best fit neuron activity.
d 5 \ Model parameter.
Fixed to a value best fit neuron activity.
r 0.01 \ Model parameter.
Controls slow variable z's variation speed.
Governs spiking frequency when spiking, and affects the
number of spikes per burst when bursting.
s 4 \ Model parameter. Governs adaption.
============= ============== ========= ============================================================
**Model Variables**
=============== ================= =====================================
**Member name** **Initial Value** **Explanation**
--------------- ----------------- -------------------------------------
V -1.6 Membrane potential.
y -10 Gating variable.
z 0 Gating variable.
spike False Whether generate the spikes.
input 0 External and synaptic input current.
t_last_spike -1e7 Last spike time stamp.
=============== ================= =====================================
**References**
.. [1] Hindmarsh, James L., and R. M. Rose. "A model of neuronal bursting using
three coupled first order differential equations." Proceedings of the
Royal society of London. Series B. Biological sciences 221.1222 (1984):
87-102.
.. [2] Storace, Marco, Daniele Linaro, and Enno de Lange. "The Hindmarsh–Rose
neuron model: bifurcation analysis and piecewise-linear approximations."
Chaos: An Interdisciplinary Journal of Nonlinear Science 18.3 (2008):
033128.
"""
[docs]
def __init__(
self,
size: Shape,
a: Union[float, ArrayType, Initializer, Callable] = 1.,
b: Union[float, ArrayType, Initializer, Callable] = 3.,
c: Union[float, ArrayType, Initializer, Callable] = 1.,
d: Union[float, ArrayType, Initializer, Callable] = 5.,
r: Union[float, ArrayType, Initializer, Callable] = 0.01,
s: Union[float, ArrayType, Initializer, Callable] = 4.,
V_rest: Union[float, ArrayType, Initializer, Callable] = -1.6,
V_th: Union[float, ArrayType, Initializer, Callable] = 1.0,
V_initializer: Union[Initializer, Callable, ArrayType] = ZeroInit(),
y_initializer: Union[Initializer, Callable, ArrayType] = OneInit(-10.),
z_initializer: Union[Initializer, Callable, ArrayType] = ZeroInit(),
noise: Union[float, ArrayType, Initializer, Callable] = None,
method: str = 'exp_auto',
keep_size: bool = False,
input_var: bool = True,
name: str = None,
# parameters for training
mode: bm.Mode = None,
spike_fun: Callable = bm.surrogate.inv_square_grad2,
):
# initialization
super(HindmarshRose, self).__init__(size=size,
keep_size=keep_size,
name=name,
mode=mode)
is_subclass(self.mode, (bm.TrainingMode, bm.NonBatchingMode))
# parameters
self.a = parameter(a, self.varshape, allow_none=False)
self.b = parameter(b, self.varshape, allow_none=False)
self.c = parameter(c, self.varshape, allow_none=False)
self.d = parameter(d, self.varshape, allow_none=False)
self.r = parameter(r, self.varshape, allow_none=False)
self.s = parameter(s, self.varshape, allow_none=False)
self.V_th = parameter(V_th, self.varshape, allow_none=False)
self.V_rest = parameter(V_rest, self.varshape, allow_none=False)
self.noise = init_noise(noise, self.varshape, num_vars=3)
self.spike_fun = is_callable(spike_fun, 'spike_fun')
self.input_var = input_var
# variables
is_initializer(V_initializer, 'V_initializer', allow_none=False)
is_initializer(y_initializer, 'y_initializer', allow_none=False)
is_initializer(z_initializer, 'z_initializer', allow_none=False)
self._V_initializer = V_initializer
self._y_initializer = y_initializer
self._z_initializer = z_initializer
# variables
self.reset_state(self.mode)
# integral
if self.noise is None:
self.integral = odeint(method=method, f=self.derivative)
else:
self.integral = sdeint(method=method, f=self.derivative, g=self.noise)
def reset_state(self, batch_size=None):
self.V = variable_(self._V_initializer, self.varshape, batch_size)
self.y = variable_(self._y_initializer, self.varshape, batch_size)
self.z = variable_(self._z_initializer, self.varshape, batch_size)
if self.input_var:
self.input = variable_(bm.zeros, self.varshape, batch_size)
sp_type = bm.float_ if isinstance(self.mode, bm.TrainingMode) else bool
self.spike = variable_(lambda s: bm.zeros(s, dtype=sp_type), self.varshape, batch_size)
def dV(self, V, t, y, z, I_ext):
return y - self.a * V * V * V + self.b * V * V - z + I_ext
def dy(self, y, t, V):
return self.c - self.d * V * V - y
def dz(self, z, t, V):
return self.r * (self.s * (V - self.V_rest) - z)
@property
def derivative(self):
return JointEq([self.dV, self.dy, self.dz])
def update(self, x=None):
t = share.load('t')
dt = share.load('dt')
if self.input_var:
if x is not None:
self.input += x
x = self.input.value
else:
x = 0. if x is None else x
V, y, z = self.integral(self.V, self.y, self.z, t, x, dt=dt)
if isinstance(self.mode, bm.TrainingMode):
self.spike.value = self.spike_fun(V - self.V_th, self.V - self.V_th)
else:
self.spike.value = bm.logical_and(V >= self.V_th, self.V < self.V_th)
self.V.value = V
self.y.value = y
self.z.value = z
return self.spike.value
def clear_input(self):
if self.input_var:
self.input[:] = 0.
[docs]
class FHN(NeuDyn):
r"""FitzHugh-Nagumo neuron model.
**Model Descriptions**
The FitzHugh–Nagumo model (FHN), named after Richard FitzHugh (1922–2007)
who suggested the system in 1961 [1]_ and J. Nagumo et al. who created the
equivalent circuit the following year, describes a prototype of an excitable
system (e.g., a neuron).
The motivation for the FitzHugh-Nagumo model was to isolate conceptually
the essentially mathematical properties of excitation and propagation from
the electrochemical properties of sodium and potassium ion flow. The model
consists of
- a *voltage-like variable* having cubic nonlinearity that allows regenerative
self-excitation via a positive feedback, and
- a *recovery variable* having a linear dynamics that provides a slower negative feedback.
.. math::
\begin{aligned}
{\dot {v}} &=v-{\frac {v^{3}}{3}}-w+RI_{\rm {ext}}, \\
\tau {\dot {w}}&=v+a-bw.
\end{aligned}
The FHN Model is an example of a relaxation oscillator
because, if the external stimulus :math:`I_{\text{ext}}`
exceeds a certain threshold value, the system will exhibit
a characteristic excursion in phase space, before the
variables :math:`v` and :math:`w` relax back to their rest values.
This behaviour is typical for spike generations (a short,
nonlinear elevation of membrane voltage :math:`v`,
diminished over time by a slower, linear recovery variable
:math:`w`) in a neuron after stimulation by an external
input current.
**Model Examples**
>>> import brainpy as bp
>>> fhn = bp.neurons.FHN(1)
>>> runner = bp.DSRunner(fhn, inputs=('input', 1.), monitors=['V', 'w'])
>>> runner.run(100.)
>>> bp.visualize.line_plot(runner.mon.ts, runner.mon.w, legend='w')
>>> bp.visualize.line_plot(runner.mon.ts, runner.mon.V, legend='V', show=True)
**Model Parameters**
============= ============== ======== ========================
**Parameter** **Init Value** **Unit** **Explanation**
------------- -------------- -------- ------------------------
a 1 \ Positive constant
b 1 \ Positive constant
tau 10 ms Membrane time constant.
V_th 1.8 mV Threshold potential of spike.
============= ============== ======== ========================
**Model Variables**
================== ================= =========================================================
**Variables name** **Initial Value** **Explanation**
------------------ ----------------- ---------------------------------------------------------
V 0 Membrane potential.
w 0 A recovery variable which represents
the combined effects of sodium channel
de-inactivation and potassium channel
deactivation.
input 0 External and synaptic input current.
spike False Flag to mark whether the neuron is spiking.
t_last_spike -1e7 Last spike time stamp.
================== ================= =========================================================
**References**
.. [1] FitzHugh, Richard. "Impulses and physiological states in theoretical models of nerve membrane." Biophysical journal 1.6 (1961): 445-466.
.. [2] https://en.wikipedia.org/wiki/FitzHugh%E2%80%93Nagumo_model
.. [3] http://www.scholarpedia.org/article/FitzHugh-Nagumo_model
"""
[docs]
def __init__(
self,
size: Shape,
a: Union[float, ArrayType, Initializer, Callable] = 0.7,
b: Union[float, ArrayType, Initializer, Callable] = 0.8,
tau: Union[float, ArrayType, Initializer, Callable] = 12.5,
Vth: Union[float, ArrayType, Initializer, Callable] = 1.8,
V_initializer: Union[Initializer, Callable, ArrayType] = ZeroInit(),
w_initializer: Union[Initializer, Callable, ArrayType] = ZeroInit(),
noise: Union[float, ArrayType, Initializer, Callable] = None,
method: str = 'exp_auto',
keep_size: bool = False,
input_var: bool = True,
name: str = None,
# parameters for training
mode: bm.Mode = None,
):
# initialization
super(FHN, self).__init__(size=size,
keep_size=keep_size,
name=name,
mode=mode)
is_subclass(self.mode, (bm.NonBatchingMode,))
# parameters
self.a = parameter(a, self.varshape, allow_none=False)
self.b = parameter(b, self.varshape, allow_none=False)
self.tau = parameter(tau, self.varshape, allow_none=False)
self.Vth = parameter(Vth, self.varshape, allow_none=False)
self.noise = init_noise(noise, self.varshape, num_vars=2)
self.input_var = input_var
# initializers
is_initializer(V_initializer, 'V_initializer')
is_initializer(w_initializer, 'w_initializer')
self._V_initializer = V_initializer
self._w_initializer = w_initializer
# variables
self.reset_state(self.mode)
# integral
if self.noise is None:
self.integral = odeint(method=method, f=self.derivative)
else:
self.integral = sdeint(method=method, f=self.derivative, g=self.noise)
def reset_state(self, batch_size=None):
self.V = variable_(self._V_initializer, self.varshape, batch_size)
self.w = variable_(self._w_initializer, self.varshape, batch_size)
if self.input_var:
self.input = variable_(bm.zeros, self.varshape, batch_size)
self.spike = variable_(lambda s: bm.zeros(s, dtype=bool), self.varshape, batch_size)
def dV(self, V, t, w, I_ext):
return V - V * V * V / 3 - w + I_ext
def dw(self, w, t, V):
return (V + self.a - self.b * w) / self.tau
@property
def derivative(self):
return JointEq([self.dV, self.dw])
def update(self, x=None):
t = share.load('t')
dt = share.load('dt')
if self.input_var:
if x is not None:
self.input += x
x = self.input.value
else:
x = 0. if x is None else x
V, w = self.integral(self.V.value, self.w.value, t, x, dt=dt)
self.spike.value = bm.logical_and(V >= self.Vth, self.V < self.Vth)
self.V.value = V
self.w.value = w
return self.spike.value
def clear_input(self):
if self.input_var:
self.input[:] = 0.
[docs]
class ALIFBellec2020(NeuDyn):
r"""Leaky Integrate-and-Fire model with SFA [1]_.
This model is similar to the GLIF2 model in the Technical White Paper
on generalized LIF (GLIF) models from AllenInstitute [2]_.
Formally, this model is given by:
.. math::
\tau \dot{V} = -(V - V_{\mathrm{rest}}) + R*I \\
\tau_a \dot{a} = -a
Once a spike is induced by :math:`V(t) > V_{\mathrm{th}} + \beta a`, then
.. math::
V \gets V - V_{\mathrm{th}} \\
a \gets a + 1
References
----------
.. [1] Bellec, Guillaume, et al. "A solution to the learning dilemma for
recurrent networks of spiking neurons."
Nature communications 11.1 (2020): 1-15.
.. [2] Allen Institute: Cell Types Database. © 2018 Allen Institute for
Brain Science. Allen Cell Types Database, cell feature search.
Available from: celltypes.brain-map.org/data (2018).
"""
[docs]
def __init__(
self,
size: Shape,
keep_size: bool = False,
# model parameters
V_rest: Union[float, ArrayType, Initializer, Callable] = -70.,
V_th: Union[float, ArrayType, Initializer, Callable] = -60.,
R: Union[float, ArrayType, Initializer, Callable] = 1.,
beta: Union[float, ArrayType, Initializer, Callable] = 1.6,
tau: Union[float, ArrayType, Initializer, Callable] = 20.,
tau_a: Union[float, ArrayType, Initializer, Callable] = 2000.,
tau_ref: Union[float, ArrayType, Initializer, Callable] = None,
noise: Union[float, ArrayType, Initializer, Callable] = None,
# initializers
V_initializer: Union[Initializer, Callable, ArrayType] = OneInit(-70.),
a_initializer: Union[Initializer, Callable, ArrayType] = OneInit(-50.),
# parameter for training
spike_fun: Callable = bm.surrogate.relu_grad,
input_var: bool = True,
# other parameters
method: str = 'exp_auto',
name: str = None,
mode: bm.Mode = None,
eprop: bool = False
):
super().__init__(name=name,
size=size,
keep_size=keep_size,
mode=mode)
is_subclass(self.mode, (bm.TrainingMode, bm.NonBatchingMode))
# parameters
self.V_rest = parameter(V_rest, self.varshape, allow_none=False)
self.V_th = parameter(V_th, self.varshape, allow_none=False)
self.R = parameter(R, self.varshape, allow_none=False)
self.beta = parameter(beta, self.varshape, allow_none=False)
self.tau = parameter(tau, self.varshape, allow_none=False)
self.tau_a = parameter(tau_a, self.varshape, allow_none=False)
self.tau_ref = parameter(tau_ref, self.varshape, allow_none=True)
self.noise = init_noise(noise, self.varshape, num_vars=2)
self.spike_fun = is_callable(spike_fun, 'spike_fun')
self.eprop = eprop
self.input_var = input_var
# initializers
self._V_initializer = is_initializer(V_initializer, 'V_initializer')
self._a_initializer = is_initializer(a_initializer, 'a_initializer')
# variables
self.reset_state(self.mode)
# integral
if self.noise is None:
self.integral = odeint(method=method, f=self.derivative)
else:
self.integral = sdeint(method=method, f=self.derivative, g=self.noise)
def da(self, a, t):
return -a / self.tau_a
def dV(self, V, t, I_ext):
return (- (V - self.V_rest) + self.R * I_ext) / self.tau
@property
def derivative(self):
return JointEq([self.dV, self.da])
def reset_state(self, batch_size=None):
self.a = variable_(self._a_initializer, self.varshape, batch_size)
self.V = variable_(self._V_initializer, self.varshape, batch_size)
if self.input_var:
self.input = variable_(bm.zeros, self.varshape, batch_size)
sp_type = bm.float_ if isinstance(self.mode, bm.TrainingMode) else bool
self.spike = variable_(lambda s: bm.zeros(s, dtype=sp_type), self.varshape, batch_size)
if self.tau_ref is not None:
self.t_last_spike = variable_(lambda s: bm.ones(s) * -1e7, self.varshape, batch_size)
self.refractory = variable_(lambda s: bm.zeros(s, dtype=bool), self.varshape, batch_size)
def update(self, x=None):
t = share.load('t')
dt = share.load('dt')
if self.input_var:
if x is not None:
self.input += x
x = self.input.value
else:
x = 0. if x is None else x
V, a = self.integral(self.V, self.a, t, x, dt)
if self.tau_ref is not None:
# refractory
refractory = (t - self.t_last_spike) <= self.tau_ref
if isinstance(self.mode, bm.TrainingMode):
refractory = stop_gradient(refractory)
V = bm.where(refractory, self.V.value, V)
# spike and reset
if isinstance(self.mode, bm.TrainingMode):
spike = self.spike_fun((V - self.V_th - self.beta * self.a) / self.V_th)
V -= self.V_th * (stop_gradient(spike) if self.eprop else spike)
# will be used in other place, like Delta Synapse, so stop its gradient
spike_ = spike > 0.
refractory = stop_gradient(bm.logical_or(refractory, spike_))
t_last_spike = stop_gradient(bm.where(spike_, t, self.t_last_spike.value))
else:
spike = V >= (self.V_th + self.beta * self.a)
refractory = bm.logical_or(refractory, spike)
t_last_spike = bm.where(spike, t, self.t_last_spike.value)
V -= self.V_th * spike
self.refractory.value = refractory
self.t_last_spike.value = t_last_spike
else:
# spike and reset
if isinstance(self.mode, bm.TrainingMode):
spike = self.spike_fun((V - self.V_th - self.beta * self.a) / self.V_th)
V -= self.V_th * (stop_gradient(spike) if self.eprop else spike)
else:
spike = V >= (self.V_th + self.beta * self.a)
V -= self.V_th * spike
self.spike.value = spike
self.V.value = V
self.a.value = a + spike
return spike
def clear_input(self):
if self.input_var:
self.input[:] = 0.
[docs]
class LIF_SFA_Bellec2020(NeuDyn):
r"""Leaky Integrate-and-Fire model with SFA [1]_.
This model is similar to the GLIF2 model in the Technical White Paper
on generalized LIF (GLIF) models from AllenInstitute [2]_.
Formally, this model is given by:
.. math::
\tau \dot{V} = -(V - V_{\mathrm{rest}}) + R*I \\
\tau_a \dot{a} = -a
Once a spike is induced by :math:`V(t) > V_{\mathrm{th}} + \beta a`, then
.. math::
V \gets V - V_{\mathrm{th}} \\
a \gets a + 1
References
----------
.. [1] Bellec, Guillaume, et al. "A solution to the learning dilemma for
recurrent networks of spiking neurons."
Nature communications 11.1 (2020): 1-15.
.. [2] Allen Institute: Cell Types Database. © 2018 Allen Institute for
Brain Science. Allen Cell Types Database, cell feature search.
Available from: celltypes.brain-map.org/data (2018).
"""
[docs]
def __init__(
self,
size: Shape,
keep_size: bool = False,
# model parameters
V_rest: Union[float, ArrayType, Initializer, Callable] = -70.,
V_th: Union[float, ArrayType, Initializer, Callable] = -60.,
R: Union[float, ArrayType, Initializer, Callable] = 1.,
beta: Union[float, ArrayType, Initializer, Callable] = 1.6,
tau: Union[float, ArrayType, Initializer, Callable] = 20.,
tau_a: Union[float, ArrayType, Initializer, Callable] = 2000.,
tau_ref: Union[float, ArrayType, Initializer, Callable] = None,
# initializers
V_initializer: Union[Initializer, Callable, ArrayType] = OneInit(-70.),
a_initializer: Union[Initializer, Callable, ArrayType] = OneInit(-50.),
# parameter for training
spike_fun: Callable = bm.surrogate.relu_grad,
# other parameters
method: str = 'exp_auto',
name: str = None,
mode: bm.Mode = None,
):
super().__init__(name=name, size=size, keep_size=keep_size, mode=mode)
is_subclass(self.mode, (bm.TrainingMode, bm.NonBatchingMode))
# parameters
self.V_rest = parameter(V_rest, self.varshape, allow_none=False)
self.V_th = parameter(V_th, self.varshape, allow_none=False)
self.R = parameter(R, self.varshape, allow_none=False)
self.beta = parameter(beta, self.varshape, allow_none=False)
self.tau = parameter(tau, self.varshape, allow_none=False)
self.tau_a = parameter(tau_a, self.varshape, allow_none=False)
self.tau_ref = parameter(tau_ref, self.varshape, allow_none=True)
self.spike_fun = is_callable(spike_fun, 'spike_fun')
# initializers
self._V_initializer = is_initializer(V_initializer)
self._a_initializer = is_initializer(a_initializer)
# variables
self.reset_state(self.mode)
# integral
self.integral = odeint(method=method, f=self.derivative)
def da(self, a, t):
return -a / self.tau_a
def dV(self, V, t, I_ext):
return (- (V - self.V_rest) + self.R * I_ext) / self.tau
@property
def derivative(self):
return JointEq([self.dV, self.da])
def reset_state(self, batch_size=None):
self.a = variable_(self._a_initializer, self.varshape, batch_size)
self.V = variable_(self._V_initializer, self.varshape, batch_size)
self.spike = variable_(bm.zeros, self.varshape, batch_size)
if self.tau_ref is not None:
self.t_last_spike = variable_(OneInit(-1e7), self.varshape, batch_size)
def update(self, x=None):
t = share.load('t')
dt = share.load('dt')
x = 0. if x is None else x
# integral
V, a = self.integral(self.V, self.a, t, x, dt)
if self.tau_ref is not None:
# refractory
refractory = stop_gradient((t - self.t_last_spike) <= self.tau_ref)
V = bm.where(refractory, self.V.value, V)
# spike and reset
spike = self.spike_fun((V - self.V_th - self.beta * self.a) / self.V_th)
V -= self.V_th * spike
t_last_spike = stop_gradient(bm.where(spike, t, self.t_last_spike.value))
self.t_last_spike.value = t_last_spike
else:
# spike and reset
spike = self.spike_fun((V - self.V_th - self.beta * self.a) / self.V_th)
V -= self.V_th * spike
self.spike.value = spike
self.V.value = V
self.a.value = a + spike
return spike
![[docs]](../../../../../apis/deprecated/generated/brainpy.neurons.GIF.html#brainpy.neurons.GIF){kind=link}
![[docs]](../../../../../apis/deprecated/generated/brainpy.neurons.GIF.html#brainpy.neurons.GIF.__init__){kind=link}