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 ebook offers a unified computational method of realizing the constitution, improvement, and serve as of the visible cortex. It stories the present theories of the visible cortex and the organic facts on which they're established, and offers a close research of the laterally attached self-organizing map version and effects received up to now. including the software program package deal Topographica, it serves as a accomplished starting place for destiny study in computational neuroscience of the visible cortex.

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Additional resources for Computational Maps in the Visual Cortex

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Hierarchical organization of feature preferences in the macaque. The images illustrate orientation and ocular dominance patches in a 4 mm × 3 mm area of the cortical surface in the macaque monkey, measured through optical imaging. 4a. (b) The same cells are colored in gray scale from white to black according to how strongly they prefer input from the left vs. the right eye. Each neuron is sensitive to a combination of feature values, in this case a line of a particular orientation in the left or the right eye at a particular location on the visual field.

For self-organization, lateral connections, genetic vs. environmentally driven development, and temporal coding. The computational foundations of LISSOM, such as the neuron models, synchronization, learning, and self-organizing maps, are also discussed. 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.

1 Visual System Organization 19 Fig. 3. Measuring cortical maps. Optical imaging techniques allow neuronal preferences to be measured for large numbers of neurons at once (Blasdel and Salama 1986). In such experiments, part of the skull of a laboratory animal is removed by surgery, exposing the surface of the visual cortex. Visual patterns are then presented to the eyes, and a video camera records either light absorbed by the cortex or light given off by voltage-sensitive fluorescent chemicals that have been applied to it.

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