function []=my_simulatetoynetwork(connections,simlength)
networksize=length(connections);
y=.9;
flag_stimulatenodes=1;
flag_visualizenetwork=0;

%delays, divided by 10 from physical delays to make simulation less computationally intensive
time_conversion=200000;
refractorydelay=.03*time_conversion*.1;
synapticdelay=[.008 .012]*time_conversion*.1;

%random initialization, uses seeding to allow replication
spikes=sparse(logical(zeros(simlength,networksize)));
rand('state',0);
randstart=rand(1,length(connections));
for i=1:9
    if randstart(1,i)<.5
        spikes(1,i)=1;
    end
end

for stimulatednode=1:networksize
    spikes(2:end,:)=0;
    spikes=sparse(spikes);
    disp(sprintf('Simulation stimulation of neuron %d . . . ', stimulatednode))
    for i=2:simlength
        if mod(i,1000)==2
            disp(sprintf('    Time step %d . . .',(i-1)))
        end
        refractorywindow_min=round(i-refractorydelay);
        if (refractorywindow_min<1)
            refractorywindow_min=1;
        end
        
        synapticwindow_min=round(i-synapticdelay(1,2));
        synapticwindow_max=round(i-synapticdelay(1,1));
        if(synapticwindow_min<1)
            synapticwindow_min=1;
        end
        if synapticwindow_max<1
            synapticwindow_max=1;
        end
        
        
        for j=1:networksize
            if full(spikes(refractorywindow_min:i-1,j))'*full(spikes(refractorywindow_min:i-1,j))==0
                if (flag_stimulatenodes==1 & stimulatednode==j & rand<y)
                    spikes(i,j)=1;
                end
                for k=1:networksize
                    if full(spikes(synapticwindow_min:synapticwindow_max,k))'*full(spikes(synapticwindow_min:synapticwindow_max,k))~=0 & rand<connections(k,j)
                        spikes(i,j)=1;
                    end
                end
            end
        end
    end
    
    %visualize network spiking
    if flag_visualizenetwork==1
        for i=1:networksize
            subplot(networksize,1,i);
            plot([1:simlength]'/time_conversion,spikes(:,i));
            title(sprintf('Neuron %d',i));
            v=axis;
            v(1,3)=-.1;
            v(1,4)=2;
            axis(v);
        end
    end
    save(sprintf('toynetwork_pin%d.mat',stimulatednode),'spikes');
end