By Risto Miikkulainen, James A. Bednar, Yoonsuck Choe, Joseph Sirosh
This publication provides a unified computational method of knowing the constitution, improvement, and serve as of the visible cortex. It reports the present theories of the visible cortex and the organic info on which they're established, and offers a close research of the laterally hooked up self-organizing map version and effects acquired up to now. including the software program package deal Topographica, it serves as a finished origin for destiny learn in computational neuroscience of the visible cortex.
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Additional resources for Computational Maps in the Visual Cortex
Areas of the retina where light spots excite this neuron are plotted in white (ON areas), areas where dark spots excite it are plotted in black (OFF areas), and areas with little effect are plotted in medium gray. e. their ON and OFF areas cover a small speciﬁc portion of the retina. (a) ON cells are found in the retina and LGN, and prefer light areas surrounded by dark. (b) OFF cells have the opposite preferences, responding most strongly to a dark area surrounded by light. e. have no preferred orientation.
Pattern generation is ubiquitous in nature, and could also be utilized in engineering of complex artiﬁcial systems in general. 3 Perceptual Grouping In addition to understanding how the observed structures in the visual cortex emerge, it is important to understand what role they play in visual processing. Because LISSOM is a functional computational model, it can be tested in simulated neurobiological and psychophysical experiments. It is therefore ideal for testing hypotheses about the functional phenomena that arise from the self-organized structures.
In addition to providing computational support for the above three hypotheses, the LISSOM framework constitutes a general computational theory of representation and learning in the visual cortex. The learning mechanisms extract correlations in the input that allow representing visual information efﬁciently in a sparse, redundancyreduced code. Such representations are separable and generalizable, and serve as an effective foundation for later stages of visual processing, such as pattern recognition.