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Computing the effects of excitatory-inhibitory balance on neuronal input-output properties.

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  1. [1] § Materials and methods › Stimulus parameters › Sustained stimulus. ↔ ScriptsPlos_sustained_final.m, lines 305–333 · score 0.55 · steady state, relative delay, onset, ramped, duration, ms

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The authors' code

MATLAB · 489 lines · 19 KB · MIT · 1 match

  1. %Scripts for re-creating Figs 1-5 of manuscript
  2. % Simulations for sustained stimulus; transient stimulus are in another script
  4. %Each script recreates figures in manuscript; data saved
  5. %common functions near bottom of page.
  7. %**NOTE: highlight segment you wish to run and push "Run Selection" in Editor tab; if you push "Run", it will run the entire program**
  9. %% ---------Fig. 2B of manuscript---------
  10. totalTime=200;delT=0.01;Rec_length=round(totalTime/delT);time=delT*(1:Rec_length); %time related variables in ms
  11. nE=250;%number of excitatory inputs
  12. kn=0.2; %ratio of inhibitory to excitatory neurons; must be between 0-1
  13. gEmax=0.0103; gImax=gEmax; %synaptic current peaks (current clamp); 250µV PSPs
  14. kq=gImax/gEmax;%ratio of I to E charge or conductance; must be between 0-1;
  15. rE=50; %rate of afferent input (Poisson process) to reference cell in Hz;
  16. pE=0.85; %excitatory input probability to reference cell
  17. pI=kn*kq; %effective inhibitory probability to reference cell
  18. pEtoI=0.35; %probability of inputs to I cells; controls input ratef
  19. pEavg=zeros(1,Rec_length);pIavg=pEavg;pNavg=pEavg;
  20. spkE=zeros(1,Rec_length);histE=spkE;
  23. %-UNCOMMENT DESIRED FIGURE----------
  24. % %----Fig 2Bi--------
  25. % pE=0.35;
  26. % piEtoI=0.35;
  27. % kn=0.3;
  28. %gMode=1; % current based, switch to 2 for conductance
  29. % sweeps=1000;
  30. %
  31. % %----Fig 2Bii--------
  32. % pE=0.55;
  33. % piEtoI=0.35;
  34. % kn=0.4;
  35. %gMode=1; % current based, switch to 2 for conductance
  36. % sweeps=1000;
  38. %----Fig 2Biii--------
  39. pE=0.85;
  40. piEtoI=0.35;
  41. kn=0.68;
  42. gMode=1; % current based, switch to 2 for conductance
  43. sweeps=1000;
  45. %------synaptic parameters
  46. if gMode==1 %current based
  47. gEmax=0.0103; gImax=gEmax; %synaptic current peaks (current clamp); 250µV PSPs
  48. end
  49. if gMode==2 %conductance based
  50. gEmax=1.47e-4; %for 250µV EPSP
  51. gImax=10.45e-4; %for -250µV IPSP
  52. end
  54. tic
  55. for sw=1:sweeps
  56. [pEt,pIt,rI,vE]=go(kn,kq,pE,piEtoI,rE,nE,gMode,gEmax,gImax,delT,Rec_length);
  57. pEavg=pEavg+pEt;
  58. pIavg=pIavg+pIt;
  59. pNavg=pNavg+(pEt.*(1-pIt));
  60. spkE((vE>=-10))=1;
  61. histE=histE+spkE;
  62. end
  63. pEavg=pEavg/sweeps;pIavg=pIavg/sweeps;pNavg=pNavg/sweeps;
  64. toc
  65. figure(1);clf;hold on;
  66. subplot(2,1,1);plot(time,histE);xlabel('time (ms)','FontSize',14);ylabel('counts','FontSize',14)
  67. subplot(2,1,2);hold on;plot(time,pEavg,'b');plot(time,pIavg,'r');plot(time,pNavg,'k');xlabel('time (ms)','FontSize',14);ylabel ('p','FontSize',14);lgd=legend('pE','pI','pNet');lgd.Title.FontSize = 14;
  69. %% Figure 3B
  70. gMode=1; %1 is current clamp; 2 is conductance
  71. totalTime=1000;delT=0.01;Rec_length=round(totalTime/delT);time=delT*(1:Rec_length); %time related variables in ms
  72. %-----------constants----------------------
  73. arStart=round(0.2*Rec_length);arEnd=round(0.8*Rec_length);
  75. %----set up parameters to vary--------
  76. sweeps=100;
  77. nTrialsE=20;
  78. gEmax=0.0103; gImax=gEmax; %synaptic current peaks (current clamp); 250µV PSPs
  79. probE=linspace(0.1,1,nTrialsE);
  80. piEtoI=[0 0.4 0.5 0.6 0.7];
  81. nTrialsI=numel(piEtoI);
  82. k=0.2;kq=1;
  85. rE=50*ones(1,Rec_length); %firing rate of afferents in Hz
  86. ntheta=60;
  87. dM=zeros(nTrialsI,nTrialsE,6); %data Matrix
  88. tic
  89. figure(300);clf;hold on;
  90. for jj=1:nTrialsI
  91. for j=1:nTrialsE
  92. [prbE,prbEstd,meanE,stdE,prbI,prbIstd,meanI,stdI,meanF,stdF]=goB(k,kq,probE(j),piEtoI(jj),rE(j),gEmax,gImax,gMode,sweeps,arStart,arEnd);
  93. ['nTrialsI: ' num2str(jj) ' nTrialsE: ' num2str(j) ' probE: ' num2str(prbE) ' probI: ' num2str(prbI) ' freqE: ' num2str(meanF) ]
  94. dM(jj,j,1)=prbE;
  95. dM(jj,j,2) = prbI;
  96. dM(jj,j,3) = meanE;
  97. dM(jj,j,4)=meanI;
  98. dM(jj,j,5)=meanF;
  99. dM(jj,j,6)=stdF;
  100. % ['pE is:' num2str(probE(j)) ' pI is: ' num2str(Iavg) ' piEtoI: ' num2str(piEtoI(jj))]
  101. end
  102. errorbar(dM(jj,:,1),dM(jj,:,5),dM(jj,:,6),'-sqk','MarkerFace','k');axis([0 1 0 200]);xlabel('pE','FontSize',20);ylabel('firing rate','FontSize',20);
  103. end
  104. save('Fig3B.mat','dM');
  105. toc
  108. %% Figure 3C
  109. gMode=1;%1 is current clamp; 2 is conductance
  110. totalTime=1000;delT=0.01;Rec_length=round(totalTime/delT);time=delT*(1:Rec_length); %time related variables in ms
  111. arStart=round(0.2*Rec_length);arEnd=round(0.8*Rec_length);
  112. %-----------constants----------------------
  113. gEmax=0.0103; gImax=gEmax; %synaptic current peaks (current clamp); 250µV PSPs
  116. %----set up parameters to vary--------
  117. sweeps=100;
  118. nTrialsE=20;
  119. probE=linspace(0.0,1,nTrialsE);
  120. kScalar=[0 0.2 0.4 0.6 0.8];
  121. nTrialsK=numel(kScalar);
  122. rE=50;
  123. k=1;kq=1;
  124. piEtoI=0.35;
  126. dM=zeros(nTrialsK,nTrialsE,6);
  127. tic
  128. figure(300);clf;hold on;
  129. for jj=1:nTrialsK
  130. k=kScalar(jj)*probE;
  131. for j=1:nTrialsE
  132. [prbE,prbEstd,meanE,stdE,prbI,prbIstd,meanI,stdI,meanF,stdF]=goB(k(j),kq,probE(j),piEtoI,rE,gEmax,gImax,gMode,sweeps,arStart,arEnd);
  133. ['nTrialsI: ' num2str(jj) ' nTrialsE: ' num2str(j) ' probE: ' num2str(prbE) ' probI: ' num2str(prbI) ' k: ' num2str(k(j)) ' freqE: ' num2str(meanF) ]
  134. dM(jj,j,1)=prbE;
  135. dM(jj,j,2) = prbI;
  136. dM(jj,j,3) = meanE;
  137. dM(jj,j,4)=meanI;
  138. dM(jj,j,5)=meanF;
  139. dM(jj,j,6)=stdF;
  140. end
  141. errorbar(dM(jj,:,1),dM(jj,:,5),dM(jj,:,6),'-sqk','MarkerFace','k');axis([0 1 0 200]);xlabel('pE','FontSize',20);ylabel('firing rate','FontSize',20)
  142. end
  143. save('Fig3C.mat','dM');
  144. toc
  145. %% Figure 3D
  146. gMode=1;
  147. totalTime=1000;delT=0.01;Rec_length=round(totalTime/delT);time=delT*(1:Rec_length); %time related variables in ms
  148. arStart=round(0.2*Rec_length);arEnd=round(0.8*Rec_length);
  149. %-----------constants----------------------
  152. %----set up parameters to vary--------
  153. sweeps=100;
  154. nTrialsE=20;
  155. probE=linspace(0.0,1,nTrialsE);
  156. pEtoI=[0 0.4 0.5 0.6 0.7 0.8];
  157. nTrialspEtoI=numel(pEtoI);
  158. gEmax=0.0103; gImax=gEmax; %synaptic current peaks (current clamp); 250µV PSPs
  159. k=0.2;kq=1;
  160. rE=50;
  162. dM=zeros(nTrialspEtoI,nTrialsE,6);
  164. tic
  165. figure(300);clf;hold on;
  166. for jj=1:nTrialspEtoI
  167. piEtoI=pEtoI(jj)*probE;
  168. for j=1:nTrialsE
  169. [prbE,prbEstd,meanE,stdE,prbI,prbIstd,meanI,stdI,meanF,stdF]=goB(k,kq,probE(j),piEtoI(j),rE,gEmax,gImax,gMode,sweeps,arStart,arEnd);
  170. ['nTrialspEtoI: ' num2str(jj) ' nTrialsE: ' num2str(j) ' probE: ' num2str(prbE) ' probI: ' num2str(prbI) ' freqE: ' num2str(meanF) ]
  171. dM(jj,j,1)=prbE;
  172. dM(jj,j,2) = prbI;
  173. dM(jj,j,3) = meanE;
  174. dM(jj,j,4)=meanI;
  175. dM(jj,j,5)=meanF;
  176. dM(jj,j,6)=stdF;
  177. end
  178. pause(0.1);
  179. errorbar(dM(jj,:,1),dM(jj,:,5),dM(jj,:,6),'-sqk','MarkerFace','k');axis([0 1 0 200]);xlabel('pE','FontSize',20);ylabel('firing rate','FontSize',20)
  180. end
  181. save('Fig3D.mat','dM');
  182. toc
  184. %% ----Figure 4B-------------
  185. gMode=1; %1 is current clamp; 2 is conductance
  186. totalTime=1000;delT=0.01;Rec_length=round(totalTime/delT);time=delT*(1:Rec_length); %time related variables in ms
  187. arStart=round(0.2*Rec_length);arEnd=round(0.8*Rec_length);
  188. %-----------constants----------------------
  189. gEmax=0.0103; gImax=gEmax; %synaptic current peaks (current clamp); 250µV PSPs
  192. %----set up parameters to vary--------
  193. sweeps=100;
  194. nTrialsE=50;
  195. x=linspace(2,8,nTrialsE);
  196. xctr=round(mean(x));sigX=1.5;
  197. k=0.2;kq=1;
  198. pEtoI=[0 0.275 0.3]; %synaptic efficacy from I cells to reference cell
  199. nTrialspEtoI=numel(pEtoI);
  200. aScalar=0.35;
  201. rE=50;
  203. dM=zeros(nTrialspEtoI,nTrialsE,6);
  205. tic
  206. figure(300);clf;hold on;
  207. probE=aScalar*exp(-0.5*((x-xctr)/sigX).^2);
  208. for jj=1:nTrialspEtoI
  209. for j=1:nTrialsE
  210. [prbE,prbEstd,meanE,stdE,prbI,prbIstd,meanI,stdI,meanF,stdF]=goB(k,kq,probE(j),pEtoI(jj),rE,gEmax,gImax,gMode,sweeps,arStart,arEnd);
  211. ['nTrialspEtoI: ' num2str(jj) ' nTrialsE: ' num2str(j) ' probE: ' num2str(prbE) ' probI: ' num2str(prbI) ' freqE: ' num2str(meanF) ]
  212. dM(jj,j,1)=prbE;
  213. dM(jj,j,2) = prbI;
  214. dM(jj,j,3) = meanE;
  215. dM(jj,j,4)=meanI;
  216. dM(jj,j,5)=meanF;
  217. dM(jj,j,6)=stdF;
  218. end
  219. figure(300);errorbar(x,dM(jj,:,5),dM(jj,:,6),'-sqk','MarkerFace','k');axis([min(x) max(x) -2 60]);xlabel('sensory Feature','FontSize',20);ylabel('firing rate','FontSize',20)
  220. end
  221. save('Fig4B.mat','dM');
  222. toc
  224. %% Figure 4C
  225. gMode=1;
  226. totalTime=1000;delT=0.01;Rec_length=round(totalTime/delT);time=delT*(1:Rec_length); %time related variables in ms
  227. arStart=round(0.2*Rec_length);arEnd=round(0.8*Rec_length);
  228. %-----------constants----------------------
  229. gEmax=0.0103; gImax=gEmax; %synaptic current peaks (current clamp); 250µV PSPs
  231. %----set up parameters to vary--------
  232. sweeps=100;
  233. nTrialsE=20;
  234. x=linspace(2,8,nTrialsE);
  235. xctr=round(mean(x));sigX=1.5;
  236. kScalar=[0 0.2 0.4];
  237. nTrialsK=numel(kScalar);
  238. aScalar=0.35;
  239. dM=zeros(nTrialsK,nTrialsE,6);
  240. rE=50;
  241. k=1;
  242. piEtoI=0.35;
  243. tic
  244. figure(300);clf;hold on;
  245. for jj=1:nTrialsK
  246. probE=aScalar*exp(-0.5*((x-xctr)/sigX).^2);
  247. k=kScalar(jj)*probE;
  248. for j=1:nTrialsE
  249. [prbE,prbEstd,meanE,stdE,prbI,prbIstd,meanI,stdI,meanF,stdF]=goB(k(j),kq,probE(j),piEtoI,rE,gEmax,gImax,gMode,sweeps,arStart,arEnd);
  250. ['nTrialsI: ' num2str(jj) ' nTrialsE: ' num2str(j) ' probE: ' num2str(prbE) ' probI: ' num2str(prbI) ' k: ' num2str(k(j)) ' freqE: ' num2str(meanF) ]
  251. dM(jj,j,1)=prbE;
  252. dM(jj,j,2) = prbI;
  253. dM(jj,j,3) = meanE;
  254. dM(jj,j,4)=meanI;
  255. dM(jj,j,5)=meanF;
  256. dM(jj,j,6)=stdF;
  257. end
  258. figure(300);errorbar(x,dM(jj,:,5),dM(jj,:,6),'-sqk','MarkerFace','k');axis([min(x) max(x) -2 60]);xlabel('sensory Feature','FontSize',20);ylabel('firing rate','FontSize',20)
  259. end
  260. save('Fig4C.mat','dM');
  261. toc
  263. %% Figure 4D
  264. gMode=1;
  265. totalTime=1000;delT=0.01;Rec_length=round(totalTime/delT);time=delT*(1:Rec_length); %time related variables in ms
  266. arStart=round(0.2*Rec_length);arEnd=round(0.8*Rec_length);
  267. %-----------constants----------------------
  268. gEmax=0.0103; gImax=gEmax; %synaptic current peaks (current clamp); 250µV PSPs
  270. %----set up parameters to vary--------
  271. sweeps=100;
  272. nTrialsE=20;
  273. x=linspace(2,8,nTrialsE);
  274. xctr=round(mean(x));sigX=1.5;
  275. pEtoI=[0.7 0.8 0.9];
  276. nTrialspEtoI=numel(pEtoI);
  277. k=0.2;kq=1;
  278. rE=50;
  279. aScalar=0.35;
  281. dM=zeros(nTrialspEtoI,nTrialsE,6);
  283. tic
  284. figure(300);clf;hold on;
  285. probE=aScalar*exp(-0.5*((x-xctr)/sigX).^2);
  286. for jj=1:nTrialspEtoI
  287. piEtoI=pEtoI(jj)*probE;
  288. for j=1:nTrialsE
  289. [prbE,prbEstd,meanE,stdE,prbI,prbIstd,meanI,stdI,meanF,stdF]=goB(k,kq,probE(j),piEtoI(j),rE,gEmax,gImax,gMode,sweeps,arStart,arEnd);
  290. ['nTrialspEtoI: ' num2str(jj) ' nTrialsE: ' num2str(j) ' probE: ' num2str(prbE) ' probI: ' num2str(prbI) ' freqE: ' num2str(meanF) ]
  291. dM(jj,j,1)=prbE;
  292. dM(jj,j,2) = prbI;
  293. dM(jj,j,3) = meanE;
  294. dM(jj,j,4)=meanI;
  295. dM(jj,j,5)=meanF;
  296. dM(jj,j,6)=stdF;
  297. end
  298. pause(0.1);
  299. errorbar(x,dM(jj,:,5),dM(jj,:,6),'-sqk','MarkerFace','k');axis([min(x) max(x) -2 60]);xlabel('sensory Feature','FontSize',20);ylabel('firing rate','FontSize',20)
  300. end
  301. save('Fig4D.mat','dM');
  302. toc
  305. %% Figure 5
  306. totalTime=120;delT=0.01;Rec_length=round(totalTime/delT);time=delT*(1:Rec_length); %time related variables in ms
  307. histE=zeros(1,Rec_length);
  308. rE=50;rI=rE;gEmax=0.0103; gImax=gEmax;
  310. %----parameters to vary
  311. sweeps=1000;
  312. pE=1;% steady-state excitatory probability
  313. delE=5;delI=5; % relative delay (onset);example: if delE=5, delI=7 then inhibition occurs 2 ms after excitation
  314. rmpE=round(20);rmpI=rmpE; %ramp duration in ms
  315. nE=250;nI=nE;
  317. [pEt,pIt]=ramp(delE,delI,rmpE,rmpI,totalTime);
  318. pEt=pE*smooth(pEt,500);pEt=pEt';
  319. pIt=pE*smooth(pIt,500);pIt=pIt';
  320. pNt=pEt.*(1-pIt);
  322. tic
  323. for sw=1:sweeps
  324. gE=gEmax*genPSPtrain(pEt,rE,nE,totalTime,delT);
  325. gI=gImax*genPSPtrain(pEt.*pIt,rI,nI,totalTime,delT);
  326. v=LIF(gE,gI,1,Rec_length,delT,1);
  327. histE(find(v>=0))= histE(find(v>=0))+1;
  328. end
  329. figure(1);clf;hold on;subplot(2,1,1);hold on;plot(time,pEt,'b','lineWidth',2);plot(time,pIt,'--r','lineWidth',3);plot(time,pNt,'k','lineWidth',2);axis([0 120 0 1]);
  330. xlabel('time (ms)','FontSize',20);ylabel('Prob','FontSize',20);lgd=legend('pE','pI','pNet');lgd.Title.FontSize = 14;
  331. subplot(2,1,2);stem(time,histE,'k','Marker','none');xlabel('time (ms)','FontSize',20);ylabel('counts','FontSize',20);xlim([0 120]);
  332. h=figure(1);savefig(h,'fig5');
  333. toc
  336. %% common functions
  337. function [gdIn]=genPSPtrain(probIn,r,n,totalTime,delT)
  338. Rec_length=round(totalTime/delT);
  339. ptau=2.0;impLength=round(25/delT);t=(1:impLength);
  340. uPSP=(delT*t/ptau).*exp(1-t*delT/ptau);
  341. gdIn=zeros(1,numel(probIn));gdI=gdIn;gImpE=zeros(1,round(25/delT));
  343. %train=binornd(nE,tR*delT*0.001*probIn);
  344. train=poissrnd(n*r*delT*0.001*probIn);
  346. gdIn = conv(train,uPSP);
  347. gdIn(Rec_length+1:numel(gdIn))=[];
  348. end
  350. function [envE,envI]=ramp(delE,delI,rmpE,rmpI,totalTime)
  351. delT=0.01;rampDur=rmpE;del=round(delE/delT);
  352. Rec_length=round(totalTime/delT);envE=zeros(1,Rec_length);
  353. dur=round((totalTime-2*rampDur-2*delE)/delT);rLength=round(rampDur/delT);m=1/rLength;xEnd = rLength+del+1+dur-100;
  354. envE(del:rLength+(del))=(1/rLength)*(del:del+rLength)-(1/rLength)*del;
  355. envE(rLength+del+1:xEnd)=1;
  356. envE(xEnd:rLength+xEnd-1)=-m*(xEnd:rLength+xEnd-1)+m*(xEnd+rLength);
  358. delT=0.01;rampDur=rmpI;del=round(delI/delT);diffDel=(round(delE/delT)-round(delI/delT));
  359. Rec_length=round(totalTime/delT);envI=zeros(1,Rec_length);
  360. dur=round((totalTime-2*rampDur-2*delI)/delT)-diffDel;rLength=round(rampDur/delT);m=1/rLength;
  361. xEnd = rLength+del+1+dur-100-diffDel;
  362. envI(del:rLength+(del))=(1/rLength)*(del:del+rLength)-(1/rLength)*del;
  363. envI(rLength+del+1:xEnd)=1;
  364. envI(xEnd:rLength+xEnd-1)=-m*(xEnd:rLength+xEnd-1)+m*(xEnd+rLength);
  366. end
  368. function [pEt,pIt,rI,vE]=go(kn,kq,pE,pEtoI,rE,nE,gMode,gEmax,gImax,delT,Rec_length)
  369. %--constants,vectors, matrices-------
  370. nI=kn*nE; %number of inhibitory cells
  371. vE=zeros(1,Rec_length); vI=vE;%voltage traces
  372. spkI=zeros(1,Rec_length);
  373. totalTime=delT*Rec_length;
  374. %---synaptic parameters-------
  375. gE=zeros(1,Rec_length);gI=kq*gE; %synaptic conductances
  376. pEt=zeros(1,Rec_length); pIt=pEt;%probability traces
  377. %-----make template synaptic current; alpha function------
  378. ptau=2;t=(1:Rec_length); uPSP=1*(delT*t/ptau).*exp(1-t*delT/ptau);
  380. %------make a bank of inhibitory inputs to E cells---
  381. %simulate the activities of I cells and store in matrices
  382. numWaves=3*nI; %3x as many as needed; goes slower if increases
  383. spkIbank=zeros(numWaves,Rec_length); %matrices to store spikes
  384. for nn=1:numWaves
  385. train=poissrnd(pEtoI*rE*nE*delT*0.001,[1 Rec_length]); %when pE=1
  386. gE=gEmax*conv(train,uPSP);gE(Rec_length+1:numel(gE))=[];
  387. vI=LIF(gE,0*gI,gMode,Rec_length,delT,1); %with (1) or without (0) spikes
  388. spkIbank(nn,(find(vI>=0)))=spkIbank(nn,(find(vI>=0)))+1;
  389. end
  390. rI=1000*sum(sum(spkIbank(:,:)))/(numWaves*totalTime); %average firing rate of inhibitory cells
  391. %-------calculate E and I inputs to reference cell-----------
  392. gI=gI*0;
  393. lambda = rE*nE*delT*0.001;
  394. train=poissrnd(pE*lambda,[1 Rec_length]);
  395. cPSP=conv(train,uPSP);cPSP(Rec_length+1:numel(cPSP))=[];
  396. pEt=cPSP/(sum(uPSP)*lambda);% calculate excitatory probability time course
  397. gE=gEmax*cPSP;
  398. Ilist=randperm(numWaves,nI); %pick random set of nI inhibitory inputs from bank
  399. for ii=1:numel(Ilist)
  400. spkI=spkI+spkIbank(Ilist(ii),:);
  401. end
  402. cPSP=conv(spkI,uPSP);cPSP(Rec_length+1:numel(cPSP))=[];
  403. pIt=cPSP/(sum(uPSP)*lambda);
  404. gI=gImax*cPSP;%conv(spkI,kq*uPSP);gI(Rec_length+1:numel(gI))=[];
  405. vE=LIF(gE,pE*gI,gMode,Rec_length,delT,1);
  406. %nEspks=sum(vE>-10); %count number of evoked spikes
  407. end
  409. function [prbE,prbEstd,meanE,stdE,prbI,prbIstd,meanI,stdI,meanF,stdF]=goB(kval,kq,prbE,pIX, tR,gEmax,gImax,gMode,sweeps,arStart,arEnd)
  410. nE=250;nI=nE;delT=0.01;totalTime=1000;Rec_length=round(totalTime/delT); IRh=0.2;q=0.0557;tau=10;
  411. lambda=nE*tR*delT*0.001; %used for Poisson train
  412. vE=zeros(1,Rec_length);vI=vE;freqN=zeros(1,Rec_length);
  413. gE=zeros(1,Rec_length);gI=gE;
  414. ptau=2;t=(1:Rec_length); uPSP=(delT*t/ptau).*exp(1-t*delT/ptau); imp=exp(-t/500);imp=imp/sum(imp);
  415. knI=round(kval*nI);
  416. kswps=3*knI; spkIbank=zeros(kswps,Rec_length);
  417. nIspks=0;nMaxspks=0;
  418. for nn=1:kswps %rI
  419. train=poissrnd(nE*tR*delT*0.001*pIX,1,Rec_length);
  420. gE = gEmax*conv(train,uPSP);gE(Rec_length+1:numel(gE))=[];
  421. vI=LIF(gE,0*gI,1,Rec_length,delT,1) ;
  422. spkIbank(nn,(find(vI>=0)))=spkIbank(nn,(find(vI>=0)))+1;
  423. nMaxspks=nMaxspks+sum(vI(arStart:arEnd)>-10);
  424. if(sum(vI>-10))>0
  425. nIspks=nIspks+1;
  426. end
  427. end
  428. nMaxspks=nMaxspks/kswps;
  429. rI=nMaxspks/((arEnd-arStart)*delT/1000);
  430. ppI=nIspks/kswps;
  431. %-----make unitary PSP template--------
  432. ptau=2;t=(1:Rec_length);
  433. uPSP=(delT*t/ptau).*exp(1-t*delT/ptau);
  434. nEspks=0;nEspksPred=0;freqIavg=0;spkMatrix=zeros(4,sweeps);
  435. q=sum(gEmax*uPSP)*delT;
  436. fspks=zeros(1,sweeps);
  437. for sw=1:sweeps
  438. gIhisto=zeros(1,Rec_length);
  439. Ilist=randperm(kswps,knI);
  440. for nn=1:knI
  441. gIhisto=gIhisto+spkIbank(Ilist(nn),:);
  442. end
  443. cPSP=conv(gIhisto,kq*uPSP);cPSP(Rec_length+1:numel(cPSP))=[];
  444. pIt=cPSP/(sum(uPSP)*lambda);
  445. gI=gImax*cPSP;%conv(spkI,kq*uPSP);gI(Rec_length+1:numel(gI))=[];
  446. spkMatrix(3,sw)=mean(pIt(arStart:arEnd)); %mean I prob
  447. spkMatrix(4,sw)=mean(gI(arStart:arEnd)); %mean I current
  448. gI=gI*prbE;%conditioned on pE
  450. train=poissrnd(lambda*prbE,1,Rec_length);
  451. cPSP=conv(train,uPSP);cPSP(Rec_length+1:numel(cPSP))=[];
  452. pEt=cPSP/(sum(uPSP)*lambda);% calculate excitatory probability time course
  453. gE = gEmax*cPSP;%conv(train,uPSP);gE(Rec_length+1:numel(gE))=[];
  454. spkMatrix(1,sw)=mean(pEt(arStart:arEnd)); %mean E prob
  455. spkMatrix(2,sw)=mean(gE(arStart:arEnd)); %mean E current
  456. vE=LIF(gE,gI,1,Rec_length,delT,1);%figure(101);clf;hold on;plot(vE,'b');
  457. nEspks=sum(vE>-10);
  458. spkMatrix(5,sw)=1000*nEspks/totalTime; %firing rate in Hz
  459. end
  460. prbE=mean(spkMatrix(1,:));prbEstd=std(spkMatrix(1,:));
  461. meanE=mean(spkMatrix(2,:))/sweeps;stdE=std(spkMatrix(2,:));
  462. prbI=mean(spkMatrix(3,:));prbIstd=std(spkMatrix(3,:));
  463. meanI=mean(spkMatrix(4,:))/sweeps;stdI=std(spkMatrix(4,:));
  464. meanF=mean(spkMatrix(5,:));stdF=std(spkMatrix(5,:));
  465. end
  467. function v=LIF(gexc,ginh,gMode,Rec_length,delT,spkOn)
  468. tau=10;R=75;El=-70;vTh=-55; %LIF parameters
  469. v=zeros(1,Rec_length)+El;Isyn=0*v;
  470. i=2;
  471. while (i<=Rec_length)
  472. if gMode==2 %conductance mode
  473. Isyn(i)=gexc(i)*(v(i-1)-0)+ginh(i)*(v(i-1)+80);
  474. end
  475. if gMode==1 %current clamp mode
  476. Isyn(i)=-(gexc(i)-ginh(i));
  477. end
  478. delV=(-(v(i-1)-El)-R*(Isyn(i)))*(delT/tau);
  479. v(i)=v(i-1)+delV;
  480. if spkOn==1 %generate spikes?
  481. if (v(i)>vTh)
  482. v(i)=0;
  483. v(i+1)=El;
  484. i=i+1;
  485. end
  486. end
  487. i=i+1;
  488. end
  489. end

ScriptsPlos_sustained_final.m at commit 555de40, under MIT · at the source

Overview

Authors: Alex D Reyes1
ORCID iDs: Alex D Reyes
  1. Center for Neural Science, New York University, New York, New York, United States of America
Institutions: New York University (United States)
Journal: PLoS computational biology, volume 22, issue 3, article e1013958
Dates: received 16 May 2025; accepted 29 January 2026; published online 9 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pcbi.1013958 · PMID 41802014 · PMCID PMC12998957 · OpenAlex W7134237319
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), human (organism), computational (subfield)
Methods: Evoked potentials, Single-unit activity, calcium imaging
MeSH: Models, Neurological*, Nerve Net*, Neural Inhibition*, Neurons*, Action Potentials, Animals, Computational Biology, Computer Simulation, Humans (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute of Health Sciences (1 R01 MH129031-01)
Citations: not cited yet (Europe PMC); 79 references in the paper

Abstract

In sensory systems, stimuli are represented through the diverse firing responses and receptive fields of neurons. These features emerge from the interaction between excitatory (E) and inhibitory (I) neuron populations within the network. Changes in sensory inputs alter this balance, leading to shifts in firing patterns and the input-output properties of individual neurons and the network. Although these phenomena have been extensively investigated experimentally and theoretically, the principles governing how E and I inputs are integrated remain unclear. Here, probabilistic rules are derived to describe how neurons in feedforward inhibitory circuits combine these inputs to generate stimulus-evoked responses. This simple model is broadly applicable, capturing a wide range of response features that would otherwise require multiple separate models, and offers insights into the cellular and network mechanisms influencing the input-output properties of neurons, gain modulation, and the emergence of diverse temporal firing patterns.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

AlexDReyes/ReyesPlosCompBio2025

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 555de40e74a771eea36c941cdbc9558e08653bd3, 2 February 2026
Languages: MATLAB (2)
Size: 4 files, 2 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
4 files
At the source: github.com/AlexDReyes/ReyesPlosCompBio2025.git

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability

The source code and data used to produce the results and analyses presented in this manuscript are available on a Github repository at https://github.com/AlexDReyes/ReyesPlosCompBio2025.git.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 1 author, 9 MeSH terms, 1 funder, 77 references.

Cite

This paper

Reyes, A. D. (2026). Computing the effects of excitatory-inhibitory balance on neuronal input-output properties. PLoS computational biology, 22(3), e1013958. https://doi.org/10.1371/journal.pcbi.1013958

BibTeX

@article{reyes2026computing,
author = {Reyes, Alex D},
title = {{Computing the effects of excitatory-inhibitory balance on neuronal input-output properties}},
journal = {PLoS computational biology},
year = {2026},
month = mar,
volume = {22},
number = {3},
pages = {e1013958},
publisher = {PLOS},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1013958},
url = {https://doi.org/10.1371/journal.pcbi.1013958},
pmid = {41802014},
pmcid = {PMC12998957}
}

RIS

TY - JOUR
AU - Reyes, Alex D
TI - Computing the effects of excitatory-inhibitory balance on neuronal input-output properties
T2 - PLoS computational biology
J2 - PLoS Comput Biol
PY - 2026
DA - 2026/03/09
VL - 22
IS - 3
SP - e1013958
SN - 1553-734X
PB - PLOS
DO - 10.1371/journal.pcbi.1013958
UR - https://doi.org/10.1371/journal.pcbi.1013958
LA - en
ER -

CSL-JSON

{
"id": "10.1371/journal.pcbi.1013958",
"type": "article-journal",
"title": "Computing the effects of excitatory-inhibitory balance on neuronal input-output properties",
"container-title": "PLoS computational biology",
"author": [
{
"family": "Reyes",
"given": "Alex D"
}
],
"container-title-short": "PLoS Comput Biol",
"volume": "22",
"issue": "3",
"page": "e1013958",
"DOI": "10.1371/journal.pcbi.1013958",
"PMID": "41802014",
"PMCID": "PMC12998957",
"ISSN": "1553-734X",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pcbi.1013958",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
9
]
]
}
}

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