Computational Maps in the Visual Cortex
Springer Science & Business Media, Aug 9, 2005 - Science - 538 pages
Biological structures can be seen as collections of special devices coordinated by a matrix of organization. Devices are dif?cult to evolve and are meticulously conserved through the eons. Organization is a ?uid medium capable of rapid adaptation. The brain carries organizational ?uidity to the extreme. In its context, typical devices are ion channels, transmitters and receptors, signaling pathways, whole individual neurons or speci?c circuit patterns. The border line between what is to be called device and what a feat of organization is ?owing, given that in time organized s- systems solidify into devices. In spite of the neurosciences’ traditional concentration on devices, their aiming point on the horizon must be to understand the principles by which the nervous system ties vast arrays of internal and external variables into one coherent purposeful functional whole — to understand the brain’s mechanism of organization. For that purpose a crucial methodology is in silico experimentation. Computer simulation is a convenient tool for testing functional ideas, a sharp weapon for d- tinguishing those that work from those that don’t. To be sure, many alternatives can only be decided by direct experiment on the substrate, not by modeling. However, if a functional idea can be debunked as ?awed once tried in silico it would be a waste to make it the subject of a decade of experimentation or discussion. The venture of understanding the function and organization of the visual system illustrates this danger.
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Computational Foundations 39
A Computational Map Model of V1
Development of Maps and Connections 85
The Tilt Aftereffect
A Hierarchical Model 175
B Reduced LISSOM Simulation Specifications
PGLISSOM Simulation Specifications 435
F Visual Coding Simulation Specifications
G Calculating Feature Maps
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adaptation afferent and lateral afferent connections afferent weights animals behavior biological cells chapter computational models connection patterns contour integration correlations cortical cortical column develop direction effect Equation excitatory connections experimental experiments face preferences facelike Figure function Gaussian GMAP Hebbian learning illusory contours inhibition inhibitory connections initial input patterns iterations lateral connections lateral excitatory lateral inhibition lateral inhibitory lateral interactions learning lesion LISSOM model match measured Miikkulainen natural images neural neurons newborns normalization ocular dominance organization orientation map orientation preferences parameters patches perceptual grouping PGLISSOM plotted postnatal predictions primary visual cortex receptive fields represent representations retinal waves retinotopic map scale schematic scotoma selectivity self-organizing map self-organizing process shown shows similar simulations SMAP sparse coding spatial specific spiking stimuli structure synaptic synchronization temporal coding threshold tilt aftereffect tion V1 response vector visual input visual system