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113 lines
3.2 KiB
Matlab
113 lines
3.2 KiB
Matlab
function [y,s,r]=test_attn_spatial_pair_tuning(figoffset)
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if nargin<1 || isempty(figoffset), figoffset=0; end
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iterations=25;
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offTime=25;
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plotIntermediate=1;
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%DEFINE V1 PREDICTION NEURON RECEPTIVE FIELDS
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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=1;
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for i=1:fbMasks
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for j=1:v1Masks
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mask=gauss2D(3,0,1,0);
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%top-down, attentional, weights to V1
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w{j,lgnMasks+i}=mask;
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v{j,lgnMasks+i}=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: TWO STIMULI IN RF=====================================================
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%coordsPref=[16,16];
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%I=zeros(35,35); I(17:21,19)=1; Ipoor=I;coordsPoor=[19,19];
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%nodePref=9;
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%nodePoor=13;
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%barlen=5;
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%bar=define_bar(barlen,1);
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%backgnd=0;contrast=1;
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%orientations=[-90:15:90];
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grating_wavel=4;
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orientations=[-90:15:90];
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patch_diam=2;
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phase=180;
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nodePref=1;
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coordsPref=[16,16];
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Ipoor=gabor2D(patch_diam,90,grating_wavel,phase,1,35,2,2);
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nodePoor=5;
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coordsPoor=[20,20];
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i=0;
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for orient=orientations
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i=i+1;
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%DEFINE STIMULI AND ATTENTIONAL STATES
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%I=draw_bar(Ipoor,coordsPref(1),coordsPref(2),bar,orient,contrast,backgnd);
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I=Ipoor+gabor2D(patch_diam,orient,grating_wavel,phase,1,35,-2,-2);
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nTrials=3;
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%attend away
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Amasks{1}=[]; Acoords{1}=[];
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%attend to poor stimulus
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Amasks{2}=1; Acoords{2}=coordsPoor;
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%attend to varying stimulus
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Amasks{3}=1; Acoords{3}=coordsPref;
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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(3,nTrials,trial), plot_cropped_image(X{trial}{1}(:,:,1)-X{trial}{2}(:,:,1),0,[-0.7,0.7]);
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maxsubplot(3,nTrials,trial+nTrials), plot_cropped_image(y{nodePref},0,[0,0.125]);
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maxsubplot(3,nTrials,trial+2*nTrials), plot_cropped_image(y{nodePoor},0,[0,0.125]);
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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(:,3),'r-o','LineWidth',6,'MarkerFaceColor','w','MarkerSize',16);hold on
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plot(resp(:,1),'b-s','LineWidth',6,'MarkerFaceColor','w','MarkerSize',16);
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plot(resp(:,2),'-x','Color',[0,0.7,0],'LineWidth',6,'MarkerFaceColor','w','MarkerSize',16);
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ax=axis;
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axis([1,13,0,ax(4)])
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set(gca,'XTick',[1:3:13],'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(['attend varying';'attend away ';'attend poor ';])
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else
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print -depsc2 attn_spatial_pair_tuning_dim.eps
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end
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