“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
Introduction
Visual system is most important and well-known
One cell interactions between cells
Voltage sensitive dyes (Grinvald & Fitzpatrick)
Orientation preference
Introduction
Mapping respons dependent of stimulus in colour
500 m
Introduction
Spinning pinwheels
Goal: Reproducing the previous image and this movie in Mathematica
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
The optical pathway
The primary visual cortex
Hypercolumns
Processing a single 'pixel' in the visual
field
Orientation sensitivity
Scaling (sizes of receptive fields)
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
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
The kernels for the hypercolumns
1st order
:14
1st order
:41
2nd order
:14
2nd order
:41
Clusters
Test-image: ramp
1st order
:14
2nd order
:14
1st order
:41
2nd order
:41
Test-image: line
1st order
:14
2nd order
:14
1st order
:41
2nd order
:41
Test-image: circle
1st order
:14
2nd order
:14
1st order
:41
2nd order
:41
Test-image: mr64
1st order
:14
2nd order
:14
1st order
:41
2nd order
:41
Colourmapping
The colourmapping
depends on:
angle colour
saturation 1
greyvalue brightness
Kernels in colour
mr64 in colour
Rotating bar (wide) in colour
Movie of a rotating line
Experiment vs model
Which part of cortex????
Rotation clustering
Rotating bar (wide)
Rotating bar (narrow)
Translating bar (wide)
Translating bar (narrow)
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
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
Questions???