“mimicking cortical responses in the visual cortex” presentation 27 may 2004 florie daniels...
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“Mimicking cortical responsesin the visual cortex”
Presentation
27 May 2004
Florie DanielsLotte Verbunt
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Introduction
Visual system is most important and well-known
One cell interactions between cells
Voltage sensitive dyes (Grinvald & Fitzpatrick)
Orientation preference
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Introduction
Mapping respons dependent of stimulus in colour
500 m
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Introduction
Spinning pinwheels
Goal: Reproducing the previous image and this movie in Mathematica
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Contents
Biological backgroundModelling of simple cells and hypercolumns
of the cortex in MathematicaClusters: orientation of the hypercolumns in
relation to each otherTest-imagesColourmappingMovies Conclusions and suggestions
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The optical pathway
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The primary visual cortex
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Hypercolumns
Processing a single 'pixel' in the visual
field
Orientation sensitivity
Scaling (sizes of receptive fields)
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Receptive fields (RF)
Part of the visual field in which a stimulus will
elicit a respons
Small RF high resolution
Large RF blurred picture
Size RF = scale ()
fovea
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The receptive field sensitivity profiles of simple cells
First order Gaussian derivative:
Second order Gaussian derivative:
φ = 0 and φ = π/2
φ = 0 and φ = π/2
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The kernels for the hypercolumns
1st order
:14
1st order
:41
2nd order
:14
2nd order
:41
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Clusters
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Test-image: ramp
1st order
:14
2nd order
:14
1st order
:41
2nd order
:41
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Test-image: line
1st order
:14
2nd order
:14
1st order
:41
2nd order
:41
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Test-image: circle
1st order
:14
2nd order
:14
1st order
:41
2nd order
:41
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Test-image: mr64
1st order
:14
2nd order
:14
1st order
:41
2nd order
:41
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Colourmapping
The colourmapping
depends on:
angle colour
saturation 1
greyvalue brightness
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Kernels in colour
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mr64 in colour
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Rotating bar (wide) in colour
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Movie of a rotating line
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Experiment vs model
Which part of cortex????
Rotation clustering
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Rotating bar (wide)
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Rotating bar (narrow)
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Translating bar (wide)
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Translating bar (narrow)
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Conclusions
Goal not completely achieved, because of complexity and lack of time
σ increasing from the inside to the outside wide lines
σ increasing from the outside to the inside narrow lines
Second order Gaussian kernels are better line detectors than first order Gaussian kernels
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Suggestions for further research
Mathematica:
Colour movie
Clustering
Experimental research:
Vary the stimuli
Tip:
Beware of mistakes in the x and y direction caused by plotting with
different plotting commands
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Questions???