SNN脉冲神经网络中Izhikevich模型的matlab仿真,含仿真操作录像

close all; clc; clear; clear Ne=800; Ni=200; N_sum=Ne+Ni; re=rand(Ne,1); ri=rand(Ni,1); a=[0.02*ones(Ne,1); 0.02+0.08*ri]; b=[0.2*ones(Ne,1); 0.25-0.05*ri]; c=[-65+15*re.^2; -65*ones(Ni,1)]; d=[8-6*re.^2; 2*ones(Ni,1)]; S=[0.5*rand(Ne+Ni,Ne), -rand(Ne+Ni,Ni)]; v=-65*ones(Ne+Ni,1); % Initial values of v u=b.*v; % Initial values of u disp_v=zeros(N_sum,1000); disp_t=zeros(N_sum,1000); firings=[]; nn=1; for t=1:1000 % simulation of 1000 ms I=[5*randn(Ne,1);2*randn(Ni,1)]; % thalamic input,rangn()产生均值为0方差为1的正态分布 fired=find(v>=30);% indices of spikes find返回的是v>=30的横坐标 firings=[firings; t+0*fired,fired];%前面的firings是把上一次循环的作为现在firing的上半部分，后面为下半部分，之所以t+0*fired是为了和fired同维 v(fired)=c(fired); u(fired)=u(fired)+d(fired); I=I+sum(S(:,fired),2); disp_v(:,nn)=v; disp_v(fired,t)=30; v=v+0.5*(0.04*v.^2+5*v+140-u+I); % step 0.5 ms v=v+0.5*(0.04*v.^2+5*v+140-u+I); % for numerical u=u+a.*(b.*v-u); % stability disp_t(nn)=t; nn=nn+1; end figure(1) subplot(211) plot(disp_v(1,:)); subplot(212) plot(firings(:,1),firings(:,2),'.');