Computational Maps in the Visual Cortex by Risto Miikkulainen, James A. Bednar, Yoonsuck Choe, Joseph

By Risto Miikkulainen, James A. Bednar, Yoonsuck Choe, Joseph Sirosh

This booklet provides a unified computational method of knowing the constitution, improvement, and serve as of the visible cortex. It stories the present theories of the visible cortex and the organic info on which they're established, and offers a close research of the laterally attached self-organizing map version and effects bought to this point. including the software program package deal Topographica, it serves as a complete origin for destiny learn in computational neuroscience of the visible cortex.

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Extra info for Computational Maps in the Visual Cortex

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4. They may mediate competition and synchronization over large distances of cortex (Taylor and Alavi 1996; Usher, Stemmler, and Niebur 1996; Wang 1996). 5. They may selectively enhance and suppress responses to implement attention and control (Taylor and Alavi 1996). 2 Lateral Connections 27 Representing and Associating Information 1. Lateral connections may store information that allows decorrelating visual input and filtering out known statistical redundancies in the cortical representations (Barlow and F¨oldi´ak 1989; Dong 1996; Ghahramani and Hinton 1998; Sirosh, Miikkulainen, and Bednar 1996a).

Have no preferred orientation. Starting in V1, most cells in primates have orientation-selective RFs instead. The V1 RFs can be classified into a few basic spatial types, of which the two most common are shown above: (c) A two-lobe arrangement, favoring a 45◦ edge with dark in the upper left and light in the lower right, and (d) a three-lobe pattern, favoring a 135◦ white line against a dark background. Both types of RF are often represented with Gabor functions (Daugman 1980; Jones and Palmer 1987).

However, the specific biological and psychophysical evidence and prior modeling work for each individual experiment is reviewed in the individual chapters throughout the book. Part II focuses on mechanisms of input-driven self-organization. The basic architecture of the LISSOM computational map model of V1 is presented, and demonstrated to develop a map organization and patchy lateral connections based on regularities in the visual input. The same self-organization processes are shown to account for plasticity of the adult cortex, and give rise to psychophysical phenomena such as the tilt aftereffect.

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