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103 lines
2.9 KiB
Matlab
103 lines
2.9 KiB
Matlab
function [y,s,r]=test_attn_featural_tuning(figoffset)
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if nargin<1 || isempty(figoffset), figoffset=0; end
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iterations=10;
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offTime=20;
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plotIntermediate=1;
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%DEFINE V1 PREDICTION NEURON RECEPTIVE FIELDS
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wScaleLat=0.5;
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wScaleV2=0.35;
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phases=[0,180]; %even only
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%phases=[90,270]; %odd only
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%phases=[0,180,90,270] %even and odd
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texture=0;
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lateral=0;
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[w,v,v1Masks,lgnMasks]=filter_definitions_V1_simple_diffGauss([],[],1, [0,0],phases);
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if texture
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[w,v,v1Masks]=filter_definitions_V1_simple_diffGauss(w,v,1, [v1Masks,0],phases);
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phases=[phases;phases];
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end
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fbMasks=8;
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for i=1:fbMasks
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for j=1:v1Masks
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mask=gauss2D(25,0,1,0);
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dist=min([rem(abs(j-i),8),rem(abs(j-(i-8)),8),rem(abs(j-(i+8)),8),rem(abs(j-(i+16)),8)]);
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scale=exp(-(dist^2)./(2*0.5^2));
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%top-down, attentional, weights to V1
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w{j,lgnMasks+i}=scale.*mask;
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v{j,lgnMasks+i}=scale.*mask;
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end
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end
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if lateral
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[w,v]=filter_definitions_V1_recurrent(w,v,0.5, [0,lgnMasks+fbMasks],phases);
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end
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%=TUNING CURVE: ONE STIMULUS IN RF======================================================
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grating_wavel=4;
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grating_angles=[0:22.5:180];
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patch_diam=2;
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phase=180;
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nodePref=5;
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attnCoords=[18,18]
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i=0;
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for ga=grating_angles
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i=i+1;
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%DEFINE STIMULI AND ATTENTIONAL STATES
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I=gabor2D(patch_diam,ga,grating_wavel,phase,1,35);
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%I=image_circular_grating(patch_diam,20,grating_wavel,ga,phase,0.5);
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[a,b]=size(I);
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coordsPref=[ceil(a/2),ceil(b/2)];
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nTrials=2;
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%attend away
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Amasks{1}=[]; Acoords{1}=[];
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%attend to stimulus features
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Amasks{2}=rem(i-1,8)+1; Acoords{2}=attnCoords;
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X=define_input_with_cutoff_and_attn(I,nTrials,fbMasks,Amasks,Acoords,offTime,iterations);
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%SIMULATE MODEL
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if plotIntermediate, figoffset=figoffset+1; figure(figoffset), clf, end
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for trial=1:nTrials
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%iterate model
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[y,E,R,ytrace]=dim_activation_conv_recurrent(w,X{trial},[],iterations,v);
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%plot and record results
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if offTime<iterations,
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for j=1:length(y),
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y{j}=ytrace{j}(:,:,offTime);
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end
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end
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if plotIntermediate
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maxsubplot(2,nTrials,trial), plot_cropped_image(X{trial}{1}(:,:,1)-X{trial}{2}(:,:,1),0,[-0.7,0.7]);
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maxsubplot(2,nTrials,trial+nTrials), plot_cropped_image(y{nodePref},0,[0,0.25]);
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end
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response=cat(2,ytrace{nodePref}(coordsPref(1),coordsPref(2),:));
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resp(i,trial)=mean(response);
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end
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end
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%PLOT RESULTS
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figoffset=figoffset+1; figure(figoffset), clf
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%plot(resp,'LineWidth',4);
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plot(resp(:,1),'b-s','LineWidth',6,'MarkerFaceColor','w','MarkerSize',16); hold on
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plot(resp(:,2),'r-o','LineWidth',6,'MarkerFaceColor','w','MarkerSize',16);
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ax=axis;
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axis([1,9,0,ax(4)])
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set(gca,'XTick',[1:2:9],'XTickLabel',[-90:45:90],'FontSize',24);
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ylabel('Response')
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xlabel('Orientation (degrees)')
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set(gca,'YTick',[]);
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drawnow;
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set(gcf,'PaperPosition',[0,0,15,12])
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if plotIntermediate
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legend(['unattended';'attended '])
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else
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print -depsc2 attn_featural_tuning_dim.eps
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end
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