这里给出我自己写的一个python的函数,利用一阶欧拉法迭代计算该微分方程进行求解,并且对阈值、不应期等情况进行一个计算和区分。 importnumpyasnpimportmatplotlib.pyplotaspltdefcalc_next_step(Vm,I,step_t,remaining_refrac_time):Vl=-70Gl=0.025C=0.5ifVm>-50andVm<0:#thresholdVm=30#spike potentialelifVm>...
start: Optional. An integer number specifying at which position to start. Default is 0. stop: Required!! An integer number specifying at which position to stop (not included). step: Optional. An integer number specifying the incrementation. Default is 1. ''' LIF MODEL 嗯 微积分已经忘完了...
TheLeakyIntegrate-and-FireNeuronModel TheLeakyIntegrate-and-FireNeuronModel EminOrhan eorhan@bcs.rochester.edu November20,2012 Inthisnote,Ireviewthebehaviorofaleakyintegrate-and-fire(LIF)neuronunderdifferentstimulation conditions.Icloselyfollowchapter4.1ofGerstnerandKistler(2002).Iconsiderthreedifferent...
TheLeakyIntegrate-and-FireNeuronModelEminOrhaneorhan@bcs.rochester.eduNovember20,2012Inthisnote,Ireviewthebehaviorofaleakyintegrate-and-fire(LIF)neuronunderdifferentstimulationconditions.Icloselyfollowchapter4.1ofGerstnerandKistler(2002).Iconsiderthreedifferentstimulationconditionsbelowandshowhoweachconditionca...
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leaky integrate and fire models (for example, ref.12). We wanted to understand how much may be gained (or lost) in adding complexity to such models. Thus, we characterized how adding phenomenological complexity to a model influences its ability to reproduce neuronal spike times and classify ...
integrate-fire and self-reset (LIFT) features based on the tailored DWM in the spin-polarized ferro-coupler layers of SAF heterostructure, intrinsically mimicking the LIFT behaviors of neurons under a synergistic effect of built-in field (Hbuilt-in) and Ruderman–Kittel–Kasuya–Yosida (RKKY) ...
Analysis of sinusoidal noisy leaky integrate-and-fire models and comparison with experimental data are important to understand the neural code and neural synchronization and rhythms. In this paper, we propose two methods to estimate input parameters usin
Analysis of sinusoidal noisy leaky integrate-and-fire models and comparison with experimental data are important to understand the neural code and neural synchronization and rhythms. In this paper, we propose two methods to estimate input parameters usin
这里给出我自己写的一个python的函数,利用一阶欧拉法迭代计算该微分方程进行求解,并且对阈值、不应期等情况进行一个计算和区分。 import numpy as np import matplotlib.pyplot as plt def calc_next_step(Vm, I, step_t, remaining_refrac_time): Vl = -70 Gl = 0.025 C = 0.5 if Vm > -50 and Vm ...