Source code for brainpy._src.dynold.neurons.biological_models

# -*- coding: utf-8 -*-

from typing import Union, Callable

import brainpy.math as bm
from brainpy import check
from brainpy._src.context import share
from brainpy._src.dyn.neurons import hh
from brainpy._src.dyn.base import NeuDyn
from brainpy._src.initialize import (OneInit,
                                     Initializer,
                                     parameter,
                                     noise as init_noise,
                                     variable_)
from brainpy._src.integrators.joint_eq import JointEq
from brainpy._src.integrators.ode.generic import odeint
from brainpy._src.integrators.sde.generic import sdeint
from brainpy.types import Shape, ArrayType

__all__ = [
  'HH',
  'MorrisLecar',
  'PinskyRinzelModel',
  'WangBuzsakiModel',
]


[docs] class HH(hh.HH): r"""Hodgkin–Huxley neuron model. **Model Descriptions** The Hodgkin-Huxley (HH; Hodgkin & Huxley, 1952) model [1]_ for the generation of the nerve action potential is one of the most successful mathematical models of a complex biological process that has ever been formulated. The basic concepts expressed in the model have proved a valid approach to the study of bio-electrical activity from the most primitive single-celled organisms such as *Paramecium*, right through to the neurons within our own brains. Mathematically, the model is given by, .. math:: C \frac {dV} {dt} = -(\bar{g}_{Na} m^3 h (V &-E_{Na}) + \bar{g}_K n^4 (V-E_K) + g_{leak} (V - E_{leak})) + I(t) \frac {dx} {dt} &= \alpha_x (1-x) - \beta_x, \quad x\in {\rm{\{m, h, n\}}} &\alpha_m(V) = \frac {0.1(V+40)}{1-\exp(\frac{-(V + 40)} {10})} &\beta_m(V) = 4.0 \exp(\frac{-(V + 65)} {18}) &\alpha_h(V) = 0.07 \exp(\frac{-(V+65)}{20}) &\beta_h(V) = \frac 1 {1 + \exp(\frac{-(V + 35)} {10})} &\alpha_n(V) = \frac {0.01(V+55)}{1-\exp(-(V+55)/10)} &\beta_n(V) = 0.125 \exp(\frac{-(V + 65)} {80}) The illustrated example of HH neuron model please see `this notebook <../neurons/HH_model.ipynb>`_. The Hodgkin–Huxley model can be thought of as a differential equation system with four state variables, :math:`V_{m}(t),n(t),m(t)`, and :math:`h(t)`, that change with respect to time :math:`t`. The system is difficult to study because it is a nonlinear system and cannot be solved analytically. However, there are many numeric methods available to analyze the system. Certain properties and general behaviors, such as limit cycles, can be proven to exist. *1. Center manifold* Because there are four state variables, visualizing the path in phase space can be difficult. Usually two variables are chosen, voltage :math:`V_{m}(t)` and the potassium gating variable :math:`n(t)`, allowing one to visualize the limit cycle. However, one must be careful because this is an ad-hoc method of visualizing the 4-dimensional system. This does not prove the existence of the limit cycle. .. image:: ../../../_static/Hodgkin_Huxley_Limit_Cycle.png :align: center A better projection can be constructed from a careful analysis of the Jacobian of the system, evaluated at the equilibrium point. Specifically, the eigenvalues of the Jacobian are indicative of the center manifold's existence. Likewise, the eigenvectors of the Jacobian reveal the center manifold's orientation. The Hodgkin–Huxley model has two negative eigenvalues and two complex eigenvalues with slightly positive real parts. The eigenvectors associated with the two negative eigenvalues will reduce to zero as time :math:`t` increases. The remaining two complex eigenvectors define the center manifold. In other words, the 4-dimensional system collapses onto a 2-dimensional plane. Any solution starting off the center manifold will decay towards the *center manifold*. Furthermore, the limit cycle is contained on the center manifold. *2. Bifurcations* If the injected current :math:`I` were used as a bifurcation parameter, then the Hodgkin–Huxley model undergoes a Hopf bifurcation. As with most neuronal models, increasing the injected current will increase the firing rate of the neuron. One consequence of the Hopf bifurcation is that there is a minimum firing rate. This means that either the neuron is not firing at all (corresponding to zero frequency), or firing at the minimum firing rate. Because of the all-or-none principle, there is no smooth increase in action potential amplitude, but rather there is a sudden "jump" in amplitude. The resulting transition is known as a `canard <http://www.scholarpedia.org/article/Canards>`_. .. image:: ../../../_static/Hodgkins_Huxley_bifurcation_by_I.gif :align: center The following image shows the bifurcation diagram of the Hodgkin–Huxley model as a function of the external drive :math:`I` [3]_. The green lines show the amplitude of a stable limit cycle and the blue lines indicate unstable limit-cycle behaviour, both born from Hopf bifurcations. The solid red line shows the stable fixed point and the black line shows the unstable fixed point. .. image:: ../../../_static/Hodgkin_Huxley_bifurcation.png :align: center **Model Examples** >>> import brainpy as bp >>> group = bp.neurons.HH(2) >>> runner = bp.DSRunner(group, monitors=['V'], inputs=('input', 10.)) >>> runner.run(200.) >>> bp.visualize.line_plot(runner.mon.ts, runner.mon.V, show=True) >>> import brainpy as bp >>> import brainpy.math as bm >>> import matplotlib.pyplot as plt >>> >>> group = bp.neurons.HH(2) >>> >>> I1 = bp.inputs.spike_input(sp_times=[500., 550., 1000, 1030, 1060, 1100, 1200], sp_lens=5, sp_sizes=5., duration=2000, ) >>> I2 = bp.inputs.spike_input(sp_times=[600., 900, 950, 1500], sp_lens=5, sp_sizes=5., duration=2000, ) >>> I1 += bp.math.random.normal(0, 3, size=I1.shape) >>> I2 += bp.math.random.normal(0, 3, size=I2.shape) >>> I = bm.stack((I1, I2), axis=-1) >>> >>> runner = bp.DSRunner(group, monitors=['V'], inputs=('input', I, 'iter')) >>> runner.run(2000.) >>> >>> fig, gs = bp.visualize.get_figure(1, 1, 3, 8) >>> fig.add_subplot(gs[0, 0]) >>> plt.plot(runner.mon.ts, runner.mon.V[:, 0]) >>> plt.plot(runner.mon.ts, runner.mon.V[:, 1] + 130) >>> plt.xlim(10, 2000) >>> plt.xticks([]) >>> plt.yticks([]) >>> plt.show() Parameters ---------- size: sequence of int, int The size of the neuron group. ENa: float, ArrayType, Initializer, callable The reversal potential of sodium. Default is 50 mV. gNa: float, ArrayType, Initializer, callable The maximum conductance of sodium channel. Default is 120 msiemens. EK: float, ArrayType, Initializer, callable The reversal potential of potassium. Default is -77 mV. gK: float, ArrayType, Initializer, callable The maximum conductance of potassium channel. Default is 36 msiemens. EL: float, ArrayType, Initializer, callable The reversal potential of learky channel. Default is -54.387 mV. gL: float, ArrayType, Initializer, callable The conductance of learky channel. Default is 0.03 msiemens. V_th: float, ArrayType, Initializer, callable The threshold of the membrane spike. Default is 20 mV. C: float, ArrayType, Initializer, callable The membrane capacitance. Default is 1 ufarad. V_initializer: ArrayType, Initializer, callable The initializer of membrane potential. m_initializer: ArrayType, Initializer, callable The initializer of m channel. h_initializer: ArrayType, Initializer, callable The initializer of h channel. n_initializer: ArrayType, Initializer, callable The initializer of n channel. method: str The numerical integration method. name: str The group name. References ---------- .. [1] Hodgkin, Alan L., and Andrew F. Huxley. "A quantitative description of membrane current and its application to conduction and excitation in nerve." The Journal of physiology 117.4 (1952): 500. .. [2] https://en.wikipedia.org/wiki/Hodgkin%E2%80%93Huxley_model .. [3] Ashwin, Peter, Stephen Coombes, and Rachel Nicks. "Mathematical frameworks for oscillatory network dynamics in neuroscience." The Journal of Mathematical Neuroscience 6, no. 1 (2016): 1-92. """
[docs] def __init__( self, *args, input_var: bool = True, noise: Union[float, ArrayType, Initializer, Callable] = None, **kwargs, ): self.input_var = input_var super().__init__(*args, **kwargs, init_var=False) self.noise = init_noise(noise, self.varshape, num_vars=4) if self.noise is not None: self.integral = sdeint(method=self.method, f=self.derivative, g=self.noise) 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 MorrisLecar(hh.MorrisLecar): r"""The Morris-Lecar neuron model. **Model Descriptions** The Morris-Lecar model [4]_ (Also known as :math:`I_{Ca}+I_K`-model) is a two-dimensional "reduced" excitation model applicable to systems having two non-inactivating voltage-sensitive conductances. This model was named after Cathy Morris and Harold Lecar, who derived it in 1981. Because it is two-dimensional, the Morris-Lecar model is one of the favorite conductance-based models in computational neuroscience. The original form of the model employed an instantaneously responding voltage-sensitive Ca2+ conductance for excitation and a delayed voltage-dependent K+ conductance for recovery. The equations of the model are: .. math:: \begin{aligned} C\frac{dV}{dt} =& - g_{Ca} M_{\infty} (V - V_{Ca})- g_{K} W(V - V_{K}) - g_{Leak} (V - V_{Leak}) + I_{ext} \\ \frac{dW}{dt} =& \frac{W_{\infty}(V) - W}{ \tau_W(V)} \end{aligned} Here, :math:`V` is the membrane potential, :math:`W` is the "recovery variable", which is almost invariably the normalized :math:`K^+`-ion conductance, and :math:`I_{ext}` is the applied current stimulus. **Model Examples** >>> import brainpy as bp >>> >>> group = bp.neurons.MorrisLecar(1) >>> runner = bp.DSRunner(group, monitors=['V', 'W'], inputs=('input', 100.)) >>> runner.run(1000) >>> >>> 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.W, ylabel='W') >>> fig.add_subplot(gs[1, 0]) >>> bp.visualize.line_plot(runner.mon.ts, runner.mon.V, ylabel='V', show=True) **Model Parameters** ============= ============== ======== ======================================================= **Parameter** **Init Value** **Unit** **Explanation** ------------- -------------- -------- ------------------------------------------------------- V_Ca 130 mV Equilibrium potentials of Ca+.(mV) g_Ca 4.4 \ Maximum conductance of corresponding Ca+.(mS/cm2) V_K -84 mV Equilibrium potentials of K+.(mV) g_K 8 \ Maximum conductance of corresponding K+.(mS/cm2) V_Leak -60 mV Equilibrium potentials of leak current.(mV) g_Leak 2 \ Maximum conductance of leak current.(mS/cm2) C 20 \ Membrane capacitance.(uF/cm2) V1 -1.2 \ Potential at which M_inf = 0.5.(mV) V2 18 \ Reciprocal of slope of voltage dependence of M_inf.(mV) V3 2 \ Potential at which W_inf = 0.5.(mV) V4 30 \ Reciprocal of slope of voltage dependence of W_inf.(mV) phi 0.04 \ A temperature factor. (1/s) V_th 10 mV The spike threshold. ============= ============== ======== ======================================================= References ---------- .. [4] Lecar, Harold. "Morris-lecar model." Scholarpedia 2.10 (2007): 1333. .. [5] http://www.scholarpedia.org/article/Morris-Lecar_model .. [6] https://en.wikipedia.org/wiki/Morris%E2%80%93Lecar_model """
[docs] def __init__( self, *args, input_var: bool = True, noise: Union[float, ArrayType, Initializer, Callable] = None, **kwargs, ): self.input_var = input_var super().__init__(*args, **kwargs, init_var=False) self.noise = init_noise(noise, self.varshape, num_vars=2) if self.noise is not None: self.integral = sdeint(method=self.method, f=self.derivative, g=self.noise) 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 PinskyRinzelModel(NeuDyn): r"""The Pinsky and Rinsel (1994) model. The Pinsky and Rinsel (1994) model [7]_ is a 2-compartment (soma and dendrite), conductance-based (Hodgin-Huxley type) model of a hippocampal CA3 pyramidal neuron. It is a reduced version of an earlier, 19-compartment model by Traub, et. al. (1991) [8]_. This model demonstrates how similar qualitative and quantitative spiking behaviors can be obtained despite the reduction in model complexity. Specifically, this model demonstrates calcium bursting behavior and how the 'ping-pong' interplay between somatic and dendritic currents results in a complex shape of the burst. .. image:: ../../../_static/Pinsky-Rinzel-model-illustration.png :align: center Mathematically, the model is given by: .. math:: \begin{aligned} &\mathrm{C}_{\mathrm{m}} \mathrm{V}_{\mathrm{s}}^{\prime}=-\mathrm{I}_{\mathrm{Leak}}-\mathrm{I}_{\mathrm{Na}}-\mathrm{I}_{\mathrm{K}_{\mathrm{DR}}}-\frac{\mathrm{I}_{\mathrm{DS}}}{\mathrm{p}}+\frac{\mathrm{I}_{\mathrm{S}_{\mathrm{app}}}}{\mathrm{p}} \\ &\mathrm{C}_{\mathrm{m}} \mathrm{V}_{\mathrm{d}}^{\prime}=-\mathrm{I}_{\mathrm{Leak}}-\mathrm{I}_{\mathrm{Ca}}-\mathrm{I}_{\mathrm{K}_{\mathrm{Ca}}}-\mathrm{I}_{\mathrm{K}_{\mathrm{AHP}}}+\frac{\mathrm{I}_{\mathrm{SD}}}{(1-\mathrm{p})}+\frac{\mathrm{I}_{\mathrm{D}_{\mathrm{app}}}}{(1-\mathrm{p})} \\ &\frac{\mathrm{dCa}}{\mathrm{dt}}=-0.13 \mathrm{I}_{\mathrm{Ca}}-0.075 \mathrm{Ca} \end{aligned} The currents of the model are functions of potentials as follows: .. math:: \begin{aligned} \mathrm{I}_{\mathrm{Na}} &=\mathrm{g}_{\mathrm{Na}} m_{\infty}^{2}\left(\mathrm{~V}_{\mathrm{s}}\right) h\left(\mathrm{~V}_{\mathrm{s}}-\mathrm{V}_{\mathrm{Na}}\right) \\ \mathrm{I}_{\mathrm{K}_{\mathrm{DR}}} &=\mathrm{g}_{\mathrm{K}_{\mathrm{DR}}} n\left(\mathrm{~V}_{\mathrm{s}}-\mathrm{V}_{\mathrm{K}}\right) \\ \mathrm{I}_{\mathrm{Ca}} &=\mathrm{g}_{\mathrm{Ca}}{ }^{2}\left(\mathrm{~V}_{\mathrm{d}}-\mathrm{V}_{\mathrm{N}}\right) \\ \mathrm{I}_{\mathrm{K}_{\mathrm{Ca}}} &=\mathrm{g}_{\mathrm{k}_{\mathrm{Ca}}} C \chi(\mathrm{Ca})\left(\mathrm{V}_{\mathrm{d}}-\mathrm{V}_{\mathrm{Ca}}\right) \\ \mathrm{I}_{\mathrm{K}_{\mathrm{AHP}}} &=\mathrm{g}_{\mathrm{K}_{\mathrm{AHP}}} q\left(\mathrm{~V}_{\mathrm{d}}-\mathrm{V}_{\mathrm{K}}\right) \\ \mathrm{I}_{\mathrm{SD}} &=-\mathrm{I}_{\mathrm{DS}}=\mathrm{g}_{\mathrm{c}}\left(\mathrm{V}_{\mathrm{d}}-\mathrm{V}_{\mathrm{s}}\right) \\ \mathrm{I}_{\mathrm{Leak}} &=\mathrm{g}_{\mathrm{L}}\left(\mathrm{V}-\mathrm{V}_{\mathrm{L}}\right) \end{aligned} The activation and inactivation variables should satisfy these equations .. math:: \begin{aligned} \omega^{\prime}(\mathrm{V}) &=\frac{\omega_{\infty}(\mathrm{V})-\omega}{\tau_{\omega}(\mathrm{V})} \\ \omega_{\infty}(\mathrm{V}) &=\frac{\alpha_{\omega}(\mathrm{V})}{\alpha_{\omega}(\mathrm{V})+\beta_{\omega}(\mathrm{V})} \\ \tau_{\omega}(\mathrm{V}) &=\frac{1}{\alpha_{\omega}(\mathrm{V})+\beta_{\omega}(\mathrm{V})} \end{aligned} where, independently, we consider :math:`\omega = h, n, s, m, c, q`. The rate functions are defined as follows .. math:: \begin{aligned} \alpha_{m}\left(\mathrm{~V}_{\mathrm{s}}\right) &=\frac{0.32\left(-46.9-\mathrm{V}_{\mathrm{s}}\right)}{\exp \left(\frac{-46.9-\mathrm{V}_{\mathrm{s}}}{4}\right)-1} \\ \beta_{m}\left(\mathrm{~V}_{\mathrm{s}}\right) &=\frac{0.28\left(\mathrm{~V}_{\mathrm{s}}+19.9\right)}{\exp \left(\frac{\mathrm{V}_{\mathrm{s}}+19.9}{5}\right)-1}, \\ \alpha_{n}\left(\mathrm{~V}_{\mathrm{s}}\right) &=\frac{0.016\left(-24.9-\mathrm{V}_{\mathrm{s}}\right)}{\exp \left(\frac{-24.9-\mathrm{V}_{\mathrm{s}}}{5}\right)-1} \\ \beta_{n}\left(\mathrm{~V}_{\mathrm{s}}\right) &=0.25 \exp \left(-1-0.025 \mathrm{~V}_{\mathrm{s}}\right) \\ \alpha_{h}\left(\mathrm{~V}_{\mathrm{s}}\right) &=0.128 \exp \left(\frac{-43-\mathrm{V}_{\mathrm{s}}}{18}\right) \\ \beta_{h}\left(\mathrm{~V}_{\mathrm{s}}\right) &=\frac{4}{1+\exp \left(\frac{\left(-20-\mathrm{V}_{\mathrm{s}}\right.}{5}\right)}, \\ \alpha_{s}\left(\mathrm{~V}_{\mathrm{d}}\right) &=\frac{1.6}{1+\exp \left(-0.072\left(\mathrm{~V}_{\mathrm{d}}-5\right)\right)} \\ \beta_{s}\left(\mathrm{~V}_{\mathrm{d}}\right) &=\frac{0.02\left(\mathrm{~V}_{\mathrm{d}}+8.9\right)}{\exp \left(\frac{\left(\mathrm{V}_{\mathrm{d}}+8.9\right)}{5}\right)-1}, \\ \alpha_{C}\left(\mathrm{~V}_{\mathrm{d}}\right) &=\frac{\left(1-H\left(\mathrm{~V}_{\mathrm{d}}+10\right)\right) \exp \left(\frac{\left(\mathrm{V}_{\mathrm{d}}+50\right)}{11}-\frac{\left(\mathrm{V}_{\mathrm{d}}+53.5\right)}{27}\right)}{18.975}+H\left(\mathrm{~V}_{\mathrm{d}}+10\right)\left(2 \exp \left(\frac{\left(-53.5-\mathrm{V}_{\mathrm{d}}\right.}{27}\right)\right) \\ \beta_{C}\left(\mathrm{~V}_{\mathrm{d}}\right) &=\left(1-H\left(\mathrm{~V}_{\mathrm{d}}+10\right)\right)\left(2 \exp \left(\frac{\left(-53.5-\mathrm{V}_{\mathrm{d}}\right)}{27}\right)-\alpha_{c}\left(\mathrm{~V}_{\mathrm{d}}\right)\right) \\ \alpha_{q}(\mathrm{Ca}) &=\min (0.00002 \mathrm{Ca}, 0.01) \\ \beta_{q}(\mathrm{Ca}) &=0.001 \\ \chi(\mathrm{Ca}) &=\min \left(\frac{\mathrm{Ca}}{250}, 1\right) \end{aligned} The standard values of the parameters are given below. The maximal conductances (in :math:`\mathrm{mS} / \mathrm{cm}^{2}`) are :math:`\bar{g}_{L}=0.1`, :math:`\bar{g}_{\mathrm{Na}}=30`, :math:`\bar{g}_{\mathrm{K}-\mathrm{DR}}=15`, :math:`\bar{g}_{\mathrm{Ca}}=10`, :math:`\bar{g}_{\mathrm{K}-\mathrm{AHP}}=0.8`, :math:`\bar{g}_{\mathrm{K}-\mathrm{C}}=15`, :math:`\bar{g}_{\mathrm{NMDA}}=0.0` and :math:`\bar{g}_{\mathrm{AMPA}}=0.0`. The reversal potentials (in :math:`\mathrm{mV}` ) are :math:`V_{\mathrm{Na}}=120, V_{\mathrm{C}}=140, V_{\mathrm{K}}=-15 \mathrm{mV})` are :math:`V_{\mathrm{Na}}=120, V_{\mathrm{Ca}}=140, V_{\mathrm{K}}=-15, $V_{L}=0` and :math:`V_{\text {Syn }}=60`. The applied currents (in :math:`\mu \mathrm{A} / \mathrm{cm}^{2}` ) are :math:`I_{s}=-0.5` and :math:`I_{d}=0.0`. The coupling parameters are :math:`g_{c}=2.1 \mathrm{mS} / \mathrm{cm}^{2}` and :math:`p=0.5`. The capacitance, :math:`C_{M}`, is :math:`3 \mu \mathrm{F} / \mathrm{cm}^{2}` and :math:`\chi(C a)=\min (C a / 250,1)`. Values for these parameters, and these function definitions, are taken from Traub et al, 1991. Parameters ---------- size: sequence of int, int The size of the neuron group. gNa: float, ArrayType, Initializer, callable The maximum conductance of sodium channel. gK: float, ArrayType, Initializer, callable The maximum conductance of potassium delayed-rectifier channel. gCa: float, ArrayType, Initializer, callable The maximum conductance of calcium channel. gAHP: float, ArrayType, Initializer, callable The maximum conductance of potassium after-hyper-polarization channel. gC: float, ArrayType, Initializer, callable The maximum conductance of calcium activated potassium channel. gL: float, ArrayType, Initializer, callable The conductance of leaky channel. ENa: float, ArrayType, Initializer, callable The reversal potential of sodium channel. EK: float, ArrayType, Initializer, callable The reversal potential of potassium delayed-rectifier channel. ECa: float, ArrayType, Initializer, callable The reversal potential of calcium channel. EL: float, ArrayType, Initializer, callable The reversal potential of leaky channel. gc: float, ArrayType, Initializer, callable The coupling strength between the soma and dendrite. V_th: float, ArrayType, Initializer, callable The threshold of the membrane spike. Cm: float, ArrayType, Initializer, callable The threshold of the membrane spike. A: float, ArrayType, Initializer, callable The total cell membrane area, which is normalized to 1. p: float, ArrayType, Initializer, callable The proportion of cell area taken up by the soma. Vs_initializer: ArrayType, Initializer, callable The initializer of somatic membrane potential. Vd_initializer: ArrayType, Initializer, callable The initializer of dendritic membrane potential. Ca_initializer: ArrayType, Initializer, callable The initializer of Calcium concentration. method: str The numerical integration method. name: str The group name. References ---------- .. [7] Pinsky, Paul F., and John Rinzel. "Intrinsic and network rhythmogenesis in a reduced Traub model for CA3 neurons." Journal of computational neuroscience 1.1 (1994): 39-60. .. [8] Traub, R. D., Wong, R. K., Miles, R., & Michelson, H. (1991). A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. Journal of neurophysiology, 66(2), 635-650. """ supported_modes = (bm.BatchingMode, bm.NonBatchingMode)
[docs] def __init__( self, size: Shape, keep_size: bool = False, # maximum conductance gNa: Union[float, ArrayType, Initializer, Callable] = 30., gK: Union[float, ArrayType, Initializer, Callable] = 15., gCa: Union[float, ArrayType, Initializer, Callable] = 10., gAHP: Union[float, ArrayType, Initializer, Callable] = 0.8, gC: Union[float, ArrayType, Initializer, Callable] = 15., gL: Union[float, ArrayType, Initializer, Callable] = 0.1, # reversal potential ENa: Union[float, ArrayType, Initializer, Callable] = 60., EK: Union[float, ArrayType, Initializer, Callable] = -75., ECa: Union[float, ArrayType, Initializer, Callable] = 80., EL: Union[float, ArrayType, Initializer, Callable] = -60., # other parameters gc: Union[float, ArrayType, Initializer, Callable] = 2.1, V_th: Union[float, ArrayType, Initializer, Callable] = 20., Cm: Union[float, ArrayType, Initializer, Callable] = 3.0, p: Union[float, ArrayType, Initializer, Callable] = 0.5, A: Union[float, ArrayType, Initializer, Callable] = 1., # initializers Vs_initializer: Union[Initializer, Callable, ArrayType] = OneInit(-64.6), Vd_initializer: Union[Initializer, Callable, ArrayType] = OneInit(-64.5), Ca_initializer: Union[Initializer, Callable, ArrayType] = OneInit(0.2), # others noise: Union[float, ArrayType, Initializer, Callable] = None, method: str = 'exp_auto', name: str = None, mode: bm.Mode = None, ): # initialization super(PinskyRinzelModel, self).__init__(size=size, keep_size=keep_size, name=name, mode=mode) # conductance parameters self.gAHP = parameter(gAHP, self.varshape, allow_none=False) self.gCa = parameter(gCa, self.varshape, allow_none=False) self.gNa = parameter(gNa, self.varshape, allow_none=False) self.gK = parameter(gK, self.varshape, allow_none=False) self.gL = parameter(gL, self.varshape, allow_none=False) self.gC = parameter(gC, self.varshape, allow_none=False) # reversal potential parameters self.ENa = parameter(ENa, self.varshape, allow_none=False) self.ECa = parameter(ECa, self.varshape, allow_none=False) self.EK = parameter(EK, self.varshape, allow_none=False) self.EL = parameter(EL, self.varshape, allow_none=False) # other neuronal parameters self.V_th = parameter(V_th, self.varshape, allow_none=False) self.Cm = parameter(Cm, self.varshape, allow_none=False) self.gc = parameter(gc, self.varshape, allow_none=False) self.p = parameter(p, self.varshape, allow_none=False) self.A = parameter(A, self.varshape, allow_none=False) self.noise = init_noise(noise, self.varshape, num_vars=8) # initializers check.is_initializer(Vs_initializer, 'Vs_initializer', allow_none=False) check.is_initializer(Vd_initializer, 'Vd_initializer', allow_none=False) check.is_initializer(Ca_initializer, 'Ca_initializer', allow_none=False) self._Vs_initializer = Vs_initializer self._Vd_initializer = Vd_initializer self._Ca_initializer = Ca_initializer # variables self.Vs = variable_(self._Vs_initializer, self.varshape, self.mode) self.Vd = variable_(self._Vd_initializer, self.varshape, self.mode) self.Ca = variable_(self._Ca_initializer, self.varshape, self.mode) self.h = bm.Variable(self.inf_h(self.Vs), batch_axis=0 if isinstance(self.mode, bm.BatchingMode) else None) self.n = bm.Variable(self.inf_n(self.Vs), batch_axis=0 if isinstance(self.mode, bm.BatchingMode) else None) self.s = bm.Variable(self.inf_s(self.Vd), batch_axis=0 if isinstance(self.mode, bm.BatchingMode) else None) self.c = bm.Variable(self.inf_c(self.Vd), batch_axis=0 if isinstance(self.mode, bm.BatchingMode) else None) self.q = bm.Variable(self.inf_q(self.Ca), batch_axis=0 if isinstance(self.mode, bm.BatchingMode) else None) self.Id = variable_(bm.zeros, self.varshape, self.mode) # input to soma self.Is = variable_(bm.zeros, self.varshape, self.mode) # input to dendrite # 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.Vd.value = variable_(self._Vd_initializer, self.varshape, batch_size) self.Vs.value = variable_(self._Vs_initializer, self.varshape, batch_size) self.Ca.value = variable_(self._Ca_initializer, self.varshape, batch_size) batch_axis = 0 if isinstance(self.mode, bm.BatchingMode) else None self.h.value = bm.Variable(self.inf_h(self.Vs), batch_axis=batch_axis) self.n.value = bm.Variable(self.inf_n(self.Vs), batch_axis=batch_axis) self.s.value = bm.Variable(self.inf_s(self.Vd), batch_axis=batch_axis) self.c.value = bm.Variable(self.inf_c(self.Vd), batch_axis=batch_axis) self.q.value = bm.Variable(self.inf_q(self.Ca), batch_axis=batch_axis) self.Id.value = variable_(bm.zeros, self.varshape, batch_size) self.Is.value = variable_(bm.zeros, self.varshape, batch_size) def dCa(self, Ca, t, s, Vd): ICa = self.gCa * s * s * (Vd - self.ECa) return -0.13 * ICa - 0.075 * Ca def dh(self, h, t, Vs): return self.alpha_h(Vs) * (1 - h) - self.beta_h(Vs) * h def dn(self, n, t, Vs): return self.alpha_n(Vs) * (1 - n) - self.beta_n(Vs) * n def ds(self, s, t, Vd): return self.alpha_s(Vd) * (1 - s) - self.beta_s(Vd) * s def dc(self, c, t, Vd): return self.alpha_c(Vd) * (1 - c) - self.beta_c(Vd) * c def dq(self, q, t, Ca): return self.alpha_q(Ca) * (1 - q) - self.beta_q(Ca) * q def dVs(self, Vs, t, h, n, Vd): I_Na = (self.gNa * self.inf_m(Vs) ** 2 * h) * (Vs - self.ENa) I_KDR = (self.gK * n) * (Vs - self.EK) I_leak = self.gL * (Vs - self.EL) I_gj = self.gc / self.p * (Vd - Vs) dVdt = (- I_Na - I_KDR - I_leak + I_gj + self.Is / self.p) / self.Cm return dVdt def dVd(self, Vd, t, s, q, c, Ca, Vs): I_leak = self.gL * (Vd - self.EL) I_Ca = self.gCa * s * s * (Vd - self.ECa) I_AHP = self.gAHP * q * (Vd - self.EK) I_C = self.gC * bm.minimum(Ca / 250., 1.) * (Vd - self.EK) p = 1 - self.p I_gj = self.gc / p * (Vs - Vd) dVdt = (- I_leak - I_Ca - I_AHP - I_C + I_gj + self.Id / p) / self.Cm return dVdt @property def derivative(self): return JointEq(self.dVs, self.dVd, self.dCa, self.dh, self.dn, self.ds, self.dc, self.dq) def update(self, x=None): assert x is None t = share.load('t') dt = share.load('dt') Vs, Vd, Ca, h, n, s, c, q = self.integral(Vs=self.Vs.value, Vd=self.Vd.value, Ca=self.Ca.value, h=self.h.value, n=self.n.value, s=self.s.value, c=self.c.value, q=self.q.value, t=t, dt=dt) self.Vs.value = Vs self.Vd.value = Vd self.Ca.value = Ca self.h.value = h self.n.value = n self.s.value = s self.c.value = c self.q.value = q def clear_input(self): self.Id.value = bm.zeros_like(self.Id) self.Is.value = bm.zeros_like(self.Is) def alpha_m(self, Vs): return 0.32 * (13.1 - (Vs + 60.)) / (bm.exp((13.1 - (Vs + 60.)) / 4.) - 1.) def beta_m(self, Vs): return 0.28 * ((Vs + 60.) - 40.1) / (bm.exp(((Vs + 60.) - 40.1) / 5.) - 1.) def inf_m(self, Vs): alpha = self.alpha_m(Vs) beta = self.beta_m(Vs) return alpha / (alpha + beta) def alpha_n(self, Vs): return 0.016 * (35.1 - (Vs + 60.)) / (bm.exp((35.1 - (Vs + 60.)) / 5) - 1) def beta_n(self, Vs): return 0.25 * bm.exp(0.5 - 0.025 * (Vs + 60.)) def inf_n(self, Vs): alpha = self.alpha_n(Vs) beta = self.beta_n(Vs) return alpha / (alpha + beta) def alpha_h(self, Vs): return 0.128 * bm.exp((17. - (Vs + 60.)) / 18.) def beta_h(self, Vs): return 4. / (1 + bm.exp((40. - (Vs + 60.)) / 5)) def inf_h(self, Vs): alpha = self.alpha_h(Vs) beta = self.beta_h(Vs) return alpha / (alpha + beta) def alpha_s(self, Vd): return 1.6 / (1 + bm.exp(-0.072 * ((Vd + 60.) - 65.))) def beta_s(self, Vd): return 0.02 * ((Vd + 60.) - 51.1) / (bm.exp(((Vd + 60.) - 51.1) / 5.) - 1.) def inf_s(self, Vd): alpha = self.alpha_s(Vd) beta = self.beta_s(Vd) return alpha / (alpha + beta) def alpha_c(self, Vd): return bm.where((Vd + 60.) <= 50., (bm.exp(((Vd + 60.) - 10.) / 11.) - bm.exp(((Vd + 60.) - 6.5) / 27.)) / 18.975, 2. * bm.exp((6.5 - (Vd + 60.)) / 27.)) def beta_c(self, Vd): alpha_c = (bm.exp(((Vd + 60.) - 10.) / 11.) - bm.exp(((Vd + 60.) - 6.5) / 27.)) / 18.975 return bm.where((Vd + 60.) <= 50., 2. * bm.exp((6.5 - (Vd + 60.)) / 27.) - alpha_c, 0.) def inf_c(self, Vd): alpha_c = self.alpha_c(Vd) beta_c = self.beta_c(Vd) return alpha_c / (alpha_c + beta_c) def alpha_q(self, Ca): return bm.minimum(2e-5 * Ca, 1e-2) def beta_q(self, Ca): return 1e-3 def inf_q(self, Ca): alpha = self.alpha_q(Ca) beta = self.beta_q(Ca) return alpha / (alpha + beta)
[docs] class WangBuzsakiModel(hh.WangBuzsakiHH): r"""Wang-Buzsaki model [9]_, an implementation of a modified Hodgkin-Huxley model. Each model is described by a single compartment and obeys the current balance equation: .. math:: C_{m} \frac{d V}{d t}=-I_{\mathrm{Na}}-I_{\mathrm{K}}-I_{\mathrm{L}}-I_{\mathrm{syn}}+I_{\mathrm{app}} where :math:`C_{m}=1 \mu \mathrm{F} / \mathrm{cm}^{2}` and :math:`I_{\mathrm{app}}` is the injected current (in :math:`\mu \mathrm{A} / \mathrm{cm}^{2}` ). The leak current :math:`I_{\mathrm{L}}=g_{\mathrm{L}}\left(V-E_{\mathrm{L}}\right)` has a conductance :math:`g_{\mathrm{L}}=0.1 \mathrm{mS} / \mathrm{cm}^{2}`, so that the passive time constant :math:`\tau_{0}=C_{m} / g_{\mathrm{L}}=10 \mathrm{msec} ; E_{\mathrm{L}}=-65 \mathrm{mV}`. The spike-generating :math:`\mathrm{Na}^{+}` and :math:`\mathrm{K}^{+}` voltage-dependent ion currents :math:`\left(I_{\mathrm{Na}}\right.` and :math:`I_{\mathrm{K}}` ) are of the Hodgkin-Huxley type (Hodgkin and Huxley, 1952). The transient sodium current :math:`I_{\mathrm{Na}}=g_{\mathrm{Na}} m_{\infty}^{3} h\left(V-E_{\mathrm{Na}}\right)`, where the activation variable :math:`m` is assumed fast and substituted by its steady-state function :math:`m_{\infty}=\alpha_{m} /\left(\alpha_{m}+\beta_{m}\right)` ; :math:`\alpha_{m}(V)=-0.1(V+35) /(\exp (-0.1(V+35))-1), \beta_{m}(V)=4 \exp (-(V+60) / 18)`. The inactivation variable :math:`h` obeys a first-order kinetics: .. math:: \frac{d h}{d t}=\phi\left(\alpha_{h}(1-h)-\beta_{h} h\right) where :math:`\alpha_{h}(V)=0.07 \exp (-(V+58) / 20)` and :math:`\beta_{h}(V)=1 /(\exp (-0.1(V+28)) +1) \cdot g_{\mathrm{Na}}=35 \mathrm{mS} / \mathrm{cm}^{2}` ; :math:`E_{\mathrm{Na}}=55 \mathrm{mV}, \phi=5 .` The delayed rectifier :math:`I_{\mathrm{K}}=g_{\mathrm{K}} n^{4}\left(V-E_{\mathrm{K}}\right)`, where the activation variable :math:`n` obeys the following equation: .. math:: \frac{d n}{d t}=\phi\left(\alpha_{n}(1-n)-\beta_{n} n\right) with :math:`\alpha_{n}(V)=-0.01(V+34) /(\exp (-0.1(V+34))-1)` and :math:`\beta_{n}(V)=0.125\exp (-(V+44) / 80)` ; :math:`g_{\mathrm{K}}=9 \mathrm{mS} / \mathrm{cm}^{2}`, and :math:`E_{\mathrm{K}}=-90 \mathrm{mV}`. Parameters ---------- size: sequence of int, int The size of the neuron group. ENa: float, ArrayType, Initializer, callable The reversal potential of sodium. Default is 50 mV. gNa: float, ArrayType, Initializer, callable The maximum conductance of sodium channel. Default is 120 msiemens. EK: float, ArrayType, Initializer, callable The reversal potential of potassium. Default is -77 mV. gK: float, ArrayType, Initializer, callable The maximum conductance of potassium channel. Default is 36 msiemens. EL: float, ArrayType, Initializer, callable The reversal potential of learky channel. Default is -54.387 mV. gL: float, ArrayType, Initializer, callable The conductance of learky channel. Default is 0.03 msiemens. V_th: float, ArrayType, Initializer, callable The threshold of the membrane spike. Default is 20 mV. C: float, ArrayType, Initializer, callable The membrane capacitance. Default is 1 ufarad. phi: float, ArrayType, Initializer, callable The temperature regulator constant. V_initializer: ArrayType, Initializer, callable The initializer of membrane potential. h_initializer: ArrayType, Initializer, callable The initializer of h channel. n_initializer: ArrayType, Initializer, callable The initializer of n channel. method: str The numerical integration method. name: str The group name. References ---------- .. [9] Wang, X.J. and Buzsaki, G., (1996) Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model. Journal of neuroscience, 16(20), pp.6402-6413. """
[docs] def __init__( self, *args, input_var: bool = True, noise: Union[float, ArrayType, Initializer, Callable] = None, **kwargs, ): self.input_var = input_var super().__init__(*args, **kwargs, init_var=False) self.noise = init_noise(noise, self.varshape, num_vars=3) if self.noise is not None: self.integral = sdeint(method=self.method, f=self.derivative, g=self.noise) 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)