Source code for brainmodels.neurons.MorrisLecar

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

import brainpy as bp
import brainpy.math as bm
from .base import Neuron

__all__ = [
'MorrisLecar'
]

[docs]class MorrisLecar(Neuron): r"""The Morris-Lecar neuron model. **Model Descriptions** The Morris-Lecar model _ (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** .. plot:: :include-source: True >>> import brainpy as bp >>> import brainmodels >>> >>> group = brainmodels.neurons.MorrisLecar(1) >>> runner = bp.StructRunner(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. ============= ============== ======== ======================================================= **Model Variables** ================== ================= ========================================================= **Variables name** **Initial Value** **Explanation** ------------------ ----------------- --------------------------------------------------------- V -20 Membrane potential. W 0.02 Gating variable, refers to the fraction of opened K+ channels. 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** ..  Meier, Stephen R., Jarrett L. Lancaster, and Joseph M. Starobin. "Bursting regimes in a reaction-diffusion system with action potential-dependent equilibrium." PloS one 10.3 (2015): e0122401. ..  http://www.scholarpedia.org/article/Morris-Lecar_model ..  https://en.wikipedia.org/wiki/Morris%E2%80%93Lecar_model """
[docs] def __init__(self, size, V_Ca=130., g_Ca=4.4, V_K=-84., g_K=8., V_leak=-60., g_leak=2., C=20., V1=-1.2, V2=18., V3=2., V4=30., phi=0.04, V_th=10., method='exp_auto', name=None): # initialization super(MorrisLecar, self).__init__(size=size, method=method, name=name) # params self.V_Ca = V_Ca self.g_Ca = g_Ca self.V_K = V_K self.g_K = g_K self.V_leak = V_leak self.g_leak = g_leak self.C = C self.V1 = V1 self.V2 = V2 self.V3 = V3 self.V4 = V4 self.phi = phi self.V_th = V_th # vars self.W = bm.Variable(bm.ones(self.num) * 0.02)
def dV(self, V, t, W, Iext): M_inf = (1 / 2) * (1 + bm.tanh((V - self.V1) / self.V2)) I_Ca = self.g_Ca * M_inf * (V - self.V_Ca) I_K = self.g_K * W * (V - self.V_K) I_Leak = self.g_leak * (V - self.V_leak) dVdt = (- I_Ca - I_K - I_Leak + Iext) / self.C return dVdt def dW(self, W, t, V): tau_W = 1 / (self.phi * bm.cosh((V - self.V3) / (2 * self.V4))) W_inf = (1 / 2) * (1 + bm.tanh((V - self.V3) / self.V4)) dWdt = (W_inf - W) / tau_W return dWdt @property def derivative(self): return bp.JointEq([self.dV, self.dW])
[docs] def update(self, _t, _dt): V, self.W.value = self.integral(self.V, self.W, _t, self.input, dt=_dt) spike = bm.logical_and(self.V < self.V_th, V >= self.V_th) self.t_last_spike.value = bm.where(spike, _t, self.t_last_spike) self.V.value = V self.spike.value = spike self.input[:] = 0.