KodaliVilkhuJolly - Revision history

imported>Student221: /* Source Code */

2019-12-13T21:12:37Z

Source Code

← Older revision		Revision as of 21:12, 13 December 2019
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	== Source Code ==		== Source Code ==

	Zipped source code can be found:		Zipped source code can be found here:
	[[Media:source_code.zip]]		[[Media:source_code.zip]]

	It contains '''multipleTrials.m''' and '''genPatterns.m'''.		It contains '''multipleTrials.m''' and '''genPatterns.m'''.

imported>Student221: /* Source Code */

2019-12-13T18:38:28Z

Source Code

← Older revision		Revision as of 18:38, 13 December 2019
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	== Source Code ==		== Source Code ==

	~~=== multipleTrials.m ===~~		Zipped source code can be found:
	[[Media:multipleTrials.m]]		[[Media:source_code.zip]]

			It contains '''multipleTrials.m''' and '''genPatterns.m'''.

imported>Student221: /* multipleTrials.m */

2019-12-13T18:35:01Z

multipleTrials.m

← Older revision		Revision as of 18:35, 13 December 2019
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	=== multipleTrials.m ===		=== multipleTrials.m ===
	~~%% Multiple Trials~~		[[Media:multipleTrials.m]]
	~~clear~~
	~~close all~~
	~~clc~~
	%%
	~~ieInit~~
	~~nTrials = 10;~~

	~~%% The slanted bar scene~~
	~~scene = sceneCreate('slanted edge');~~
	~~% sceneWindow(scene);~~

	~~%% A standard human eye ball~~
	~~oi = oiCreate;~~
	~~oi = oiCompute(oi,scene);~~
	~~% oiWindow(oi);~~

	~~%% Rectangular mosaic - only one cone type~~

	~~nEyeMovements = 100;~~
	~~cones = coneMosaic;~~
	~~cones.spatialDensity =~~ [~~0,1,0,0]; % Only L cones~~
	~~cones.integrationTime = 0.001; % One ms integration time~~

	~~% Create patterns (uncomment appropriate line below) %%%%%%~~
	~~%%% Randomize eye movements~~
	~~% cones.emGenSequence(nEyeMovements,'rSeed',~~[~~],'nTrials',nTrials);~~

	~~%%% Custom eye movements~~
	~~%%% (choose 'horizontal','vertical','positive','negative')~~
	~~cones.emPositions = genPattern(nTrials,nEyeMovements,'horizontal');~~


	~~absorptions = cones.compute(oi);~~
	~~cones.window;~~

	~~% absorptions = cones.absorptions;~~

	~~mtf_sum = zeros(24,1);~~

	~~%% Loop through and Create/plot mtf for each trial~~
	~~figure~~
	~~hold on~~
	~~xlabel('Spatial Frequency (cycles/deg)');~~
	~~ylabel('Contrast Reduction (SFR)');~~
	~~grid on;~~
	~~mmPerDeg = 0.2852;~~
	~~for n = 1:nTrials~~
	~~edgeImage = squeeze(absorptions(n,:,:,1));~~

	~~rect = ISOFindSlantedBar(edgeImage);~~

	~~edgeImage = mean(squeeze(absorptions(n,:,:,~~:~~)),3);~~
	~~edgeImage = imcrop(edgeImage,rect);~~

	~~deltaX = cones~~.~~patternSampleSize(1)*1e3;~~
	~~mtf = ISO12233(edgeImage,deltaX,[~~]~~,'none');~~
	~~cpd = mtf.freq*mmPerDeg;~~
	~~plot(cpd,mtf.mtf,'Linewidth',2);~~
	~~end~~

	~~xlim([0 50~~])
	~~title('Negative Slope Pattern (L only)')~~

imported>Student221: /* Source Code */

2019-12-13T18:32:48Z

Source Code

← Older revision		Revision as of 18:32, 13 December 2019
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	=== multipleTrials.m ===		=== multipleTrials.m ===
	%% Multiple Trials		%% Multiple Trials

	clear		clear

	close all		close all

	clc		clc

	%%		%%

	ieInit		ieInit

	nTrials = 10;		nTrials = 10;



	%% The slanted bar scene		%% The slanted bar scene

	scene = sceneCreate('slanted edge');		scene = sceneCreate('slanted edge');

	% sceneWindow(scene);		% sceneWindow(scene);


	%% A standard human eye ball		%% A standard human eye ball

	oi = oiCreate;		oi = oiCreate;

	oi = oiCompute(oi,scene);		oi = oiCompute(oi,scene);

	% oiWindow(oi);		% oiWindow(oi);


	%% Rectangular mosaic - only one cone type		%% Rectangular mosaic - only one cone type


	nEyeMovements = 100;		nEyeMovements = 100;

	cones = coneMosaic;		cones = coneMosaic;

	cones.spatialDensity = [0,1,0,0]; % Only L cones		cones.spatialDensity = [0,1,0,0]; % Only L cones

	cones.integrationTime = 0.001; % One ms integration time		cones.integrationTime = 0.001; % One ms integration time


	% Create patterns (uncomment appropriate line below) %%%%%%		% Create patterns (uncomment appropriate line below) %%%%%%

	%%% Randomize eye movements		%%% Randomize eye movements

	% cones.emGenSequence(nEyeMovements,'rSeed',[],'nTrials',nTrials);		% cones.emGenSequence(nEyeMovements,'rSeed',[],'nTrials',nTrials);


	%%% Custom eye movements		%%% Custom eye movements

	%%% (choose 'horizontal','vertical','positive','negative')		%%% (choose 'horizontal','vertical','positive','negative')

	cones.emPositions = genPattern(nTrials,nEyeMovements,'horizontal');		cones.emPositions = genPattern(nTrials,nEyeMovements,'horizontal');



	absorptions = cones.compute(oi);		absorptions = cones.compute(oi);

	cones.window;		cones.window;


	% absorptions = cones.absorptions;		% absorptions = cones.absorptions;


	mtf_sum = zeros(24,1);		mtf_sum = zeros(24,1);


	%% Loop through and Create/plot mtf for each trial		%% Loop through and Create/plot mtf for each trial

	figure		figure

	hold on		hold on

	xlabel('Spatial Frequency (cycles/deg)');		xlabel('Spatial Frequency (cycles/deg)');

	ylabel('Contrast Reduction (SFR)');		ylabel('Contrast Reduction (SFR)');

	grid on;		grid on;

	mmPerDeg = 0.2852;		mmPerDeg = 0.2852;

	for n = 1:nTrials		for n = 1:nTrials

	edgeImage = squeeze(absorptions(n,:,:,1));		edgeImage = squeeze(absorptions(n,:,:,1));


	rect = ISOFindSlantedBar(edgeImage);		rect = ISOFindSlantedBar(edgeImage);


	edgeImage = mean(squeeze(absorptions(n,:,:,:)),3);		edgeImage = mean(squeeze(absorptions(n,:,:,:)),3);

	edgeImage = imcrop(edgeImage,rect);		edgeImage = imcrop(edgeImage,rect);


	deltaX = cones.patternSampleSize(1)*1e3;		deltaX = cones.patternSampleSize(1)*1e3;

	mtf = ISO12233(edgeImage,deltaX,[],'none');		mtf = ISO12233(edgeImage,deltaX,[],'none');

	cpd = mtf.freq*mmPerDeg;		cpd = mtf.freq*mmPerDeg;

	plot(cpd,mtf.mtf,'Linewidth',2);		plot(cpd,mtf.mtf,'Linewidth',2);

	end		end


	xlim([0 50])		xlim([0 50])

	title('Negative Slope Pattern (L only)')		title('Negative Slope Pattern (L only)')

imported>Student221: /* Source Code */

2019-12-13T18:32:30Z

Source Code

← Older revision		Revision as of 18:32, 13 December 2019
Line 194:		Line 194:

	== Source Code ==		== Source Code ==

			=== multipleTrials.m ===
			%% Multiple Trials

			clear

			close all

			clc

			%%

			ieInit

			nTrials = 10;



			%% The slanted bar scene

			scene = sceneCreate('slanted edge');

			% sceneWindow(scene);


			%% A standard human eye ball

			oi = oiCreate;

			oi = oiCompute(oi,scene);

			% oiWindow(oi);


			%% Rectangular mosaic - only one cone type


			nEyeMovements = 100;

			cones = coneMosaic;

			cones.spatialDensity = [0,1,0,0]; % Only L cones

			cones.integrationTime = 0.001; % One ms integration time


			% Create patterns (uncomment appropriate line below) %%%%%%

			%%% Randomize eye movements

			% cones.emGenSequence(nEyeMovements,'rSeed',[],'nTrials',nTrials);


			%%% Custom eye movements

			%%% (choose 'horizontal','vertical','positive','negative')

			cones.emPositions = genPattern(nTrials,nEyeMovements,'horizontal');



			absorptions = cones.compute(oi);

			cones.window;


			% absorptions = cones.absorptions;


			mtf_sum = zeros(24,1);


			%% Loop through and Create/plot mtf for each trial

			figure

			hold on

			xlabel('Spatial Frequency (cycles/deg)');

			ylabel('Contrast Reduction (SFR)');

			grid on;

			mmPerDeg = 0.2852;

			for n = 1:nTrials

			edgeImage = squeeze(absorptions(n,:,:,1));


			rect = ISOFindSlantedBar(edgeImage);


			edgeImage = mean(squeeze(absorptions(n,:,:,:)),3);

			edgeImage = imcrop(edgeImage,rect);


			deltaX = cones.patternSampleSize(1)*1e3;

			mtf = ISO12233(edgeImage,deltaX,[],'none');

			cpd = mtf.freq*mmPerDeg;

			plot(cpd,mtf.mtf,'Linewidth',2);

			end


			xlim([0 50])

			title('Negative Slope Pattern (L only)')

imported>Student221 at 20:33, 12 December 2019

2019-12-12T20:33:39Z

← Older revision		Revision as of 20:33, 12 December 2019
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	== Conclusions ==		== Conclusions ==

	~~- Describe what you learned~~.		Overall, the findings of this study helped provide insights into the relationship between photoreceptor responses due to eye movements and contrast sensitivity: less eye movement yields better contrast sensitivity when calculated as a function of photoreceptor absorptions. Additionally, by testing out custom eye movement patterns, we saw noticeable shifts in the SFR plots; therefore, when working on a retinal prosthetic it could be an interesting case study to experiment even with custom eye movement patterns that wouldn't necessarily be found in the biology.

	~~- What worked? What didn't? Why?~~		Finally, future steps include performing a similar study once a ganglion cell model has been incorporated into the ISETBio toolkit to observe how/if the observed trends change.

	~~- What should someone next year try?~~

	== References ==		== References ==

imported>Student221: /* Analysis */

2019-12-12T04:01:33Z

Analysis

← Older revision		Revision as of 04:01, 12 December 2019
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	== Analysis ==		== Analysis ==
	From the figures above, it is evident that different eye patterns led to different levels of absorption at the photoreceptors. However, due to the amount of variance between and within trials, it was difficult to conduct relevant quantitative analysis on the data. Nonetheless...		From the figures above, it is evident that different eye patterns led to different levels of absorption at the photoreceptors. However, due to the amount of variance between and within trials, it was difficult to conduct relevant quantitative analysis on the data. Nonetheless, we were able to observe noticeable shifts in the SFR curves with varying eye-movement patterns.

			The key takeaway from the results presented above is that '''less eye movement yields better visual acuity'''. Looking at the plots above, it is obvious that the SFR curve for the system with no drift and no microsaccades (stationary gaze) is shifted further to the right compared to all the other curves that have some sort of movement present. This seems to contradict the Rucci study presented in the Background section. However, it is important to note that ISETBio generates the SFR curve based on the photoreceptor absorptions and not the ganglion cell outputs! Therefore, in some sense, the ISETBio toolkit is modeling the mechanical aspects of the human visual system and not the perceptual components -- so, mechanically it makes sense for more motion to yield lower contrast sensitivity (when you move a camera while it is capturing a scene it generally produces a blurrier image). Additionally, even the custom generated patterns followed the trend of less movement yielding higher contrast sensitivity: the custom vertical movements output is shifted rightward compared to the custom horizontal movement output, and one can watch the movement videos shown in Appendix I to observe that the horizontal movements lead to larger magnitude stimulus movements on the cone mosaics. So, while on the surface we seem to report findings opposing Rucci's findings, it could hold true that once the visual information passes through the photoreceptors to the ganglion cells there could be an inverse relationship in the context of perception (which is what Rucci would have observed). In fact, as a first-order check, it can be seen that no movement would cause perceptual problems due to perceptual fading from static images on the retina -- thus, immediately showing a limitation of the current ISETBio model which does not model the ganglion cells. This could be a great forward research direction for ISETBio to use the ganglion cell models Rucci shares in [3] to add ganglion cell models to ISETBio and then re-run this study.

			Finally, these observations still are very useful in the context of a retinal prosthesis that our team is interested in developing as a long-term research project. We discovered that maybe it is not overly critical to focus on perfectly matching FEMs in a prosthetic for the purposes of contrast sensitivity. It is worth noting that this might not be entirely true once the ganglion cells are modeled in ISETBio and those results are gathered, but regardless the ability to use this simulation framework to gauge the importance of eye movement encoding in a prosthetic is vital to the development of the envisioned retinal prosthetic.

	== Conclusions ==		== Conclusions ==

imported>Student221: /* Link to the mosaic video of custom vertical and horizontal movements */

2019-12-12T03:46:39Z

Link to the mosaic video of custom vertical and horizontal movements

← Older revision		Revision as of 03:46, 12 December 2019
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	===Link to the mosaic video of custom vertical and horizontal movements===		===Link to the mosaic video of custom vertical and horizontal movements===
	'''Custom vertical pattern''': https://www.youtube.com/watch?time_continue=1&v=x8gk1wPC6DU&feature=emb_logo		'''Custom vertical pattern''': https://www.youtube.com/watch?time_continue=1&v=x8gk1wPC6DU&feature=emb_logo

	'''Custom horizontal pattern''': https://www.youtube.com/watch?v=p0VfhDBI8-I&feature=emb_logo		'''Custom horizontal pattern''': https://www.youtube.com/watch?v=p0VfhDBI8-I&feature=emb_logo

imported>Student221: /* Appendix I */

2019-12-12T03:46:31Z

Appendix I

← Older revision		Revision as of 03:46, 12 December 2019
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	=== Link to perceptual fading optical illusion ===		=== Link to perceptual fading optical illusion ===
	http://www.123opticalillusions.com/pages/lilac_chaser.php		http://www.123opticalillusions.com/pages/lilac_chaser.php

			===Link to the mosaic video of custom vertical and horizontal movements===
			'''Custom vertical pattern''': https://www.youtube.com/watch?time_continue=1&v=x8gk1wPC6DU&feature=emb_logo
			'''Custom horizontal pattern''': https://www.youtube.com/watch?v=p0VfhDBI8-I&feature=emb_logo

	== Appendix II ==		== Appendix II ==

imported>Student221: /* Custom Positive Slope Pattern (L cones only) */

2019-12-12T03:44:04Z

Custom Positive Slope Pattern (L cones only)

← Older revision		Revision as of 03:44, 12 December 2019
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	=== Custom Positive Slope Pattern (L cones only) ===		=== Custom Positive Slope Pattern (L cones only) ===

	[[File:customPos_pattern.png\| 300px \| left]] [[File:customPos.png\| 300px \| left-center]] <br>		[[File:customPos_pattern.png\| 300px \| left]] [[File:customPos.png\| 300px \| left-center]] <br> <br>

	== Analysis ==		== Analysis ==