function lability__stdp_model
%based on the premise that lability.^exposures=change
%there is a multiplicative process each time the synapse is exposed to the plasticity potion
%this process works on the synapse's baseline lability, causing change
%if a correlate for lability could be measured from the microperfusion experiments -- for example, psp/psc ratio -- that would permit testing of this toy model
%should use psp(succ)./psc(succ) to keep probability out of this measure
%the utility would be: given a certain assumption regarding exposures (e.g., that it is the number of times during the 60 induction pulses that a detectable PSP is present),
%what best describes lability: large PSC success (more juice)? small PSC success (more room to grow)? large PSP_succ/PSC_succ ratio (closer to the postsyn soma, better clamped, higher driving force...)?
%because synapses with high initial Pr do not generally go to unity Pr postinduction, there is no need to invoke a saturation term to explain why the big don't get bigger --
%it's either their low lability or the low exposures they'd get if they depress during the one Hz stim during induc that keeps them from changing much
linewidth=2;
index=[.001:.001:1];
preinduction_probability=[.001:.001:1]; %this is roughly what is seen, a range of values
postinduction_probability=sqrt(1-((index-1).^2)); %this is roughly what the plot of postinduction pr_o from successful induction cases looks like
plasticity_ratio=postinduction_probability./preinduction_probability;
exposures=[.5005:.0005:1]; %how many times transmission at the synpase will succeed during the induction protocol and thus how many times the synapse will be exposed to the plasticity juice
lability=10.^(log10(plasticity_ratio)./exposures); %from lability.^exposures=change
figure(44),clf
subplot(2,2,[1:2]), hold on
plot(preinduction_probability,'k','linewidth',linewidth)
plot(exposures,'g','linewidth',linewidth)
plot(postinduction_probability,'r','linewidth',linewidth)
legend('preinduction probability','exposures (proportion of max)','postinduction probability')
set(gca,'ytick',[0 1])
set(gca,'xtick',[0 500 1000])
xlabel('Synapse number')
title('Measured preinduction and postinduction probabilities and exposures for each synapse')
subplot(2,2,[3:4]), hold on
%plot(plasticity_ratio,'g')
plot(lability,'b','linewidth',linewidth)
ylabel('Lability')
ylim([0 50])
set(gca,'xtick',[0 500 1000],'ytick',[0 25 50])
xlabel('Synapse number')
title('Lability calculated from lability^e^x^p^o^s^u^r^e^s=change')