The triangular function is presented at the entrance neuron. Matlab package Csim

Cleofas

There's a code, there's a need to change the analogue function that comes on the inlet neuron with sinusoidal on the triangle. This line with the function.

input(2).data = 1*(1+sin(2*pi*6*[0:input(2).dt:Tsim]);

clear all
addpath('..');
dt_sim = 1e-4; % integration time step [sec]
Tsim = 0.6; % simulation time [sec]
dt_out = 0.005; % intervals at which VMs and PSCs are recorded [sec]
noise = 0.00; % the amount of noise [e.g. nA^2]
mySeed = 314159;
csim('destroy');
csim('set','dt',dt_sim);
csim('set','randSeed',mySeed);
% the input neuron
n1 = csim('create','SpikingInputNeuron');
% a leaky integrate and fire neuron
n2 = csim('create','IfbNeuron');
csim('set',n2,'Vthresh',0.015); % threshold
csim('set',n2,'Trefract',0.003); % refractory period
csim('set',n2,'Cm',0.03); % tau_m = Cm * Rm
csim('set',n2,'Vreset',0.005); % V_reset
csim('set',n2,'Iinject',0.002); % I_back
csim('set',n2,'Vinit',0.001); % V_init
csim('set',n2,'Rm',1.0);
csim('set',n2,'Inoise',noise);
csim('set',n2','Vresting',0);
% another leaky integrate and fire neuron (index 3)
n3 = csim('create','LifNeuron'); % type
csim('set',n3,'Vthresh',0.01,'Trefract',0.002,'Cm',0.05,'Vreset',0.0,'Iinject',0.0,'Vinit',0.0,'Rm',1.0,'Inoise',noise,'Vresting',0); % threshold
% an analog input wave form unit
n4 = csim('create','AnalogInputNeuron');
% exzitatory synapse 1 (a static one) connects neuron 1 (the input) to neuron 2;
syn1 = csim('create','StaticSpikingSynapse'); % type
csim('set',syn1,'W',0.5); % weight
csim('set',syn1,'delay',0.001); % delay
csim('set',syn1,'tau',0.003); % tau_s
csim('connect',n2,n1,syn1);
% exzitatory synapse 2 (a dynamic one) connects neuron 2 to neuron 3;
syn2 = csim('create','DynamicSpikingSynapse'); % type
csim('set',syn2,'W',0.5);% weight
csim('set',syn2,'delay',0.001);% delay
csim('set',syn2,'tau',0.003);% tau_s
csim('set',syn2,'U',0.2);% U
csim('set',syn2,'D',1.0);% D
csim('set',syn2,'F',0.3);% F
csim('set',syn2,'u0',0.2);
csim('set',syn2,'r0',1.0);
csim('connect',n3,n2,syn2);
% inhibitory synapse 3 (a dynamic one) connects neuron 3 back to neuron 2;
syn3 = csim('create','DynamicSpikingSynapse'); % type
csim('set',syn3,'W',-0.1);% weight
csim('set',syn3,'delay',0.0);% delay
csim('set',syn3,'tau',0.03);% tau_s
csim('set',syn3,'U',0.7);% U
csim('set',syn3,'D',0.1);% D
csim('set',syn3,'F',1.0);% F
csim('set',syn3,'u0',0.7);
csim('set',syn3,'r0',1.0);
csim('connect',n2,n3,syn3);
% exzitatory synapse 4 (an analog onw) connects neuron 3 back to neuron 2;

syn4 = csim('create','StaticAnalogSynapse');

csim('set',syn4,'W',0.018); % weight

csim('set',syn4,'Inoise',noise);% noise

csim('connect',n3,n4,syn4);

rec_psc = csim('create','MexRecorder');

csim('set',rec_psc,'dt',dt_out);

csim('connect',rec_psc,[syn1 syn2 syn3 syn4],'psr');

rec_vm = csim('create','MexRecorder');

csim('set',rec_vm,'dt',dt_out);

csim('connect',rec_vm,[ n2 n3 ],'Vm');

rec_sp = csim('create','MexRecorder');

csim('connect',rec_sp,[ n1 n2 n3 ],'spikes');

%

% Set up the input

%

input(1).spiking = 1;

input(1).idx = n1;

input(1).data = cumsum([0.00851522 0.0374612 0.0304122 0.00513629 0.0011292 0.00487756 0.0820378 0.0198986 0.0343706 0.016819 0.0179849 0.0783438 0.0407395 0.0287782 0.028933 0.0271107 0.0356939 0.00673942 0.0278204 0.0199695]);

input(1).dt = 0;

input(2).spiking = 0;

input(2).idx = n4;

input(2).dt = 5e-3;

input(2).data = 1*(1+sin(2pi6*[0:input(2).dt:Tsim]));

%

% run the simulation

%

tic;

csim('reset');

csim('simulate',Tsim/3,input);

csim('simulate',Tsim/3,input);

csim('simulate',Tsim/3,input);

vm = csim('get',rec_vm,'traces');

psc = csim('get',rec_psc,'traces');

spikes = csim('get',rec_sp,'traces');

toc

figure(1); clf reset;

subplot(4,1,1); cla reset;

t=spikes.channel(1).data;

line([t; t],1+[0.3; -0.3]ones(1,length(t)),'Color','k'); hold on

plot([0:input(2).dt:Tsim],2+input(2).data,'r-');

title('input');

subplot(4,1,2); cla reset;

t = spikes.channel(2).data;

plot(t,1ones(1,length(t)),'b.'); hold on

line([t; t],1+[0.3; -0.3]ones(1,length(t)),'Color','b');

t = spikes.channel(3).data;

plot(t,2ones(1,length(t)),'g.'); hold on

line([t; t],2+[0.3; -0.3]*ones(1,length(t)),'Color','g');

set(gca,'Xlim',[0 Tsim],'Ylim',[0.5 2.5]);

%legend('neuron 1','neuron 2');

title('spike times');

subplot(4,1,3); cla reset;

ldt=vm.channel(1).dt;

t=ldt:ldt:Tsim;

plot(t,vm.channel(1).data,'b'); hold on

plot(t,vm.channel(2).data,'g'); hold on

axis tight

set(gca,'Xlim',[0 Tsim]);

legend('neuron 1','neuron 2');

title('membrane voltage');

subplot(4,1,4); cla reset;

ldt=csim('get',rec_psc,'dt');

t=ldt:ldt:Tsim;

plot(t,psc.channel(1).data,'k'); hold on

plot(t,psc.channel(2).data,'b'); hold on

plot(t,psc.channel(3).data,'g'); hold on

plot(t,psc.channel(4).data,'r'); hold on

axis tight

set(gca,'Xlim',[0 Tsim]);

legend('syn 1','syn 2','syn 3','syn 4');

xlabel('time [sec]');

title('synaptic potentials');

Avante

The triangular function that is presented at the entrance neuron in Matlab in the Csim package is as follows:

5*tripuls((sawtooth(2*pi*5*[0:input(2).dt:Tsim])));

This is the full code:

clear all
addpath('..');
dt_sim = 1e-4; % integration time step [sec]
Tsim = 0.6; % simulation time [sec]
dt_out = 0.005; % intervals at which VMs and PSCs are recorded [sec]
noise = 0.00; % the amount of noise [e.g. nA^2]
mySeed = 314159;
csim('destroy');
csim('set','dt',dt_sim);
csim('set','randSeed',mySeed);
% the input neuron
n1 = csim('create','SpikingInputNeuron');
% a leaky integrate and fire neuron
n2 = csim('create','IfbNeuron');
csim('set',n2,'Vthresh',0.015); % threshold
csim('set',n2,'Trefract',0.003); % refractory period
csim('set',n2,'Cm',0.03); % tau_m = Cm * Rm
csim('set',n2,'Vreset',0.005); % V_reset
csim('set',n2,'Iinject',0.002); % I_back
csim('set',n2,'Vinit',0.001); % V_init
csim('set',n2,'Rm',1.0);
csim('set',n2,'Inoise',noise);
csim('set',n2','Vresting',0);
% another leaky integrate and fire neuron (index 3)
n3 = csim('create','LifNeuron'); % type
csim('set',n3,'Vthresh',0.01,'Trefract',0.002,'Cm',0.05,'Vreset',0.0,'Iinject',0.0,'Vinit',0.0,'Rm',1.0,'Inoise',noise,'Vresting',0); % threshold
% an analog input wave form unit
n4 = csim('create','AnalogInputNeuron');
% exzitatory synapse 1 (a static one) connects neuron 1 (the input) to neuron 2;
syn1 = csim('create','StaticSpikingSynapse'); % type
csim('set',syn1,'W',0.5); % weight
csim('set',syn1,'delay',0.001); % delay
csim('set',syn1,'tau',0.003); % tau_s
csim('connect',n2,n1,syn1);
% exzitatory synapse 2 (a dynamic one) connects neuron 2 to neuron 3;
syn2 = csim('create','DynamicSpikingSynapse'); % type
csim('set',syn2,'W',0.5);% weight
csim('set',syn2,'delay',0.001);% delay
csim('set',syn2,'tau',0.003);% tau_s
csim('set',syn2,'U',0.2);% U
csim('set',syn2,'D',1.0);% D
csim('set',syn2,'F',0.3);% F
csim('set',syn2,'u0',0.2);
csim('set',syn2,'r0',1.0);
csim('connect',n3,n2,syn2);
% inhibitory synapse 3 (a dynamic one) connects neuron 3 back to neuron 2;
syn3 = csim('create','DynamicSpikingSynapse'); % type
csim('set',syn3,'W',-0.1);% weight
csim('set',syn3,'delay',0.0);% delay
csim('set',syn3,'tau',0.03);% tau_s
csim('set',syn3,'U',0.7);% U
csim('set',syn3,'D',0.1);% D
csim('set',syn3,'F',1.0);% F
csim('set',syn3,'u0',0.7);
csim('set',syn3,'r0',1.0);
csim('connect',n2,n3,syn3);
% exzitatory synapse 4 (an analog onw) connects neuron 3 back to neuron 2;

syn4 = csim('create','StaticAnalogSynapse');

csim('set',syn4,'W',0.018); % weight

csim('set',syn4,'Inoise',noise);% noise

csim('connect',n3,n4,syn4);

rec_psc = csim('create','MexRecorder');

csim('set',rec_psc,'dt',dt_out);

csim('connect',rec_psc,[syn1 syn2 syn3 syn4],'psr');

rec_vm = csim('create','MexRecorder');

csim('set',rec_vm,'dt',dt_out);

csim('connect',rec_vm,[ n2 n3 ],'Vm');

rec_sp = csim('create','MexRecorder');

csim('connect',rec_sp,[ n1 n2 n3 ],'spikes');

%

% Set up the input

%

input(1).spiking = 1;

input(1).idx = n1;

input(1).data = cumsum([0.00851522 0.0374612 0.0304122 0.00513629 0.0011292 0.00487756 0.0820378 0.0198986 0.0343706 0.016819 0.0179849 0.0783438 0.0407395 0.0287782 0.028933 0.0271107 0.0356939 0.00673942 0.0278204 0.0199695]);

input(1).dt = 0;

input(2).spiking = 0;

input(2).idx = n4;

input(2).dt = 5e-3;

input(2).data = 5tripuls((sawtooth(2pi5[0:input(2).dt:Tsim])));

%

% run the simulation

%

tic;

csim('reset');

csim('simulate',Tsim/3,input);

csim('simulate',Tsim/3,input);

csim('simulate',Tsim/3,input);

vm = csim('get',rec_vm,'traces');

psc = csim('get',rec_psc,'traces');

spikes = csim('get',rec_sp,'traces');

toc

figure(1); clf reset;

subplot(4,1,1); cla reset;

t=spikes.channel(1).data;

line([t; t],1+[0.3; -0.3]ones(1,length(t)),'Color','k'); hold on

plot([0:input(2).dt:Tsim],2+input(2).data,'r-');

title('input');

subplot(4,1,2); cla reset;

t = spikes.channel(2).data;

plot(t,1ones(1,length(t)),'b.'); hold on

line([t; t],1+[0.3; -0.3]ones(1,length(t)),'Color','b');

t = spikes.channel(3).data;

plot(t,2ones(1,length(t)),'g.'); hold on

line([t; t],2+[0.3; -0.3]*ones(1,length(t)),'Color','g');

set(gca,'Xlim',[0 Tsim],'Ylim',[0.5 2.5]);

%legend('neuron 1','neuron 2');

title('spike times');

subplot(4,1,3); cla reset;

ldt=vm.channel(1).dt;

t=ldt:ldt:Tsim;

plot(t,vm.channel(1).data,'b'); hold on

plot(t,vm.channel(2).data,'g'); hold on

axis tight

set(gca,'Xlim',[0 Tsim]);

legend('neuron 1','neuron 2');

title('membrane voltage');

subplot(4,1,4); cla reset;

ldt=csim('get',rec_psc,'dt');

t=ldt:ldt:Tsim;

plot(t,psc.channel(1).data,'k'); hold on

plot(t,psc.channel(2).data,'b'); hold on

plot(t,psc.channel(3).data,'g'); hold on

plot(t,psc.channel(4).data,'r'); hold on

axis tight

set(gca,'Xlim',[0 Tsim]);

legend('syn 1','syn 2','syn 3','syn 4');

xlabel('time [sec]');

title('synaptic potentials');

The triangular function is presented at the entrance neuron. Matlab package Csim

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