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Vision is a vastly parallel computational procedure, during which the retinal picture is remodeled over a chain of phases to be able to emphasize behaviorally proper info (such as item class and identification) and deemphasize different details (such as point of view and lighting). The approaches at the back of imaginative and prescient function via concurrent computation and message passing between neurons inside a visible region and among diverse components. The theoretical notion of "population code" encapsulates the concept visible content material is represented at every one level via the trend of job around the neighborhood inhabitants of neurons. knowing visible inhabitants codes eventually calls for multichannel dimension and multivariate research of job styles. during the last decade, the multivariate procedure has received major momentum in imaginative and prescient examine. useful imaging and mobilephone recording degree mind job in essentially other ways, yet they now use related theoretical options and mathematical instruments of their modeling and analyses.
With a spotlight at the ventral processing circulation inspiration to underlie item reputation, this booklet provides contemporary advances in our knowing of visible inhabitants codes, novel multivariate pattern-information research thoughts, and the beginnings of a unified standpoint for phone recording and useful imaging. It serves as an advent, assessment, and reference for scientists and scholars throughout disciplines who're attracted to human and primate imaginative and prescient and, extra typically, in knowing how the mind represents and approaches information.
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Additional resources for Visual Population Codes: Toward a Common Multivariate Framework for Cell Recording and Functional Imaging (Computational Neuroscience Series)
This will lead the system to act as a competitive learning mechanism in which different neurons will become selective to different patterns. The power of such a mechanism is illustrated by a study that looked at STDP-based learning in a model visual system that was stimulated by a set of images taken from the Caltech face database —a set of several hundred photographs of human faces seen on highly varied backgrounds (Masquelier and Thorpe, 2007). It was found that, even though there was no explicit instruction, the neurons at the top end of the visual pathway ended up selective for facelike features, simply because these were the features that occurred most frequently in the inputs.
However, there is a problem with such a view, which stems from the fact that the hit rate for finding such cells appears to be much higher than one would expect if that part of the brain was really using such an explicitly localist coding scheme. The critical issue is the number of different objects that the system needs to be able to encode. One widespread source of confusion concerning localist coding is the belief that it would require having one neuron to code every possible stimulus that can be identified.
Specifically, lesioned animals (and humans) were known to have severe problems in task shifting, for example, when performing visual discrimination tasks with reversals. We therefore explicitly tested this by training monkeys to perform a go/ no-go visual discrimination task for fruit juice reward, and then periodically reversing the rule. Thus, initially the monkey might be responding to a green “go” stimulus and withholding responses to a red “no-go” stimulus. However, we then reversed the contingency with the result that the monkey made one “mistake” and received a drop of saline after responding to the green stimulus.