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Do we know what the early visual system does

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TLDR
Research is progressing with the goals of defining a single “standard model” for each stage of the visual pathway and testing the predictive power of these models on the responses to movies of natural scenes, which would be an invaluable guide for understanding the underlying biophysical and anatomical mechanisms and relating neural responses to visual perception.
Abstract
We can claim that we know what the visual system does once we can predict neural responses to arbitrary stimuli, including those seen in nature. In the early visual system, models based on one or more linear receptive fields hold promise to achieve this goal as long as the models include nonlinear mechanisms that control responsiveness, based on stimulus context and history, and take into account the nonlinearity of spike generation. These linear and nonlinear mechanisms might be the only essential determinants of the response, or alternatively, there may be additional fundamental determinants yet to be identified. Research is progressing with the goals of defining a single "standard model" for each stage of the visual pathway and testing the predictive power of these models on the responses to movies of natural scenes. These predictive models represent, at a given stage of the visual pathway, a compact description of visual computation. They would be an invaluable guide for understanding the underlying biophysical and anatomical mechanisms and relating neural responses to visual perception.

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Whatever next? Predictive brains, situated agents, and the future of cognitive science

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Performance-optimized hierarchical models predict neural responses in higher visual cortex

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Normalization as a canonical neural computation.

TL;DR: Normalization was developed to explain responses in the primary visual cortex and is now thought to operate throughout the visual system, and in many other sensory modalities and brain regions, suggesting that it serves as a canonical neural computation.
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How Does the Brain Solve Visual Object Recognition

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Using goal-driven deep learning models to understand sensory cortex

TL;DR: It is outlined how the goal-driven HCNN approach can be used to delve even more deeply into understanding the development and organization of sensory cortical processing.
References
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Journal ArticleDOI

Receptive fields, binocular interaction and functional architecture in the cat's visual cortex

TL;DR: This method is used to examine receptive fields of a more complex type and to make additional observations on binocular interaction and this approach is necessary in order to understand the behaviour of individual cells, but it fails to deal with the problem of the relationship of one cell to its neighbours.
Journal ArticleDOI

Receptive fields of single neurones in the cat's striate cortex

TL;DR: The present investigation, made in acute preparations, includes a study of receptive fields of cells in the cat's striate cortex, which resembled retinal ganglion-cell receptive fields, but the shape and arrangement of excitatory and inhibitory areas differed strikingly from the concentric pattern found in retinalganglion cells.
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The Cognitive Neurosciences

TL;DR: The fourth edition of The Cognitive Neurosciences continues to chart new directions in the study of the biologic underpinnings of complex cognition -the relationship between the structural and physiological mechanisms of the nervous system and the psychological reality of the mind as discussed by the authors.
Journal ArticleDOI

Spatiotemporal energy models for the perception of motion

TL;DR: In this article, the first stage consists of linear filters that are oriented in space-time and tuned in spatial frequency, and the outputs of quadrature pairs of such filters are squared and summed to give a measure of motion energy.
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The synaptic organization of the brain

TL;DR: Introduction to synaptic circuits, Gordon M.Shepherd and Christof Koch membrane properties and neurotransmitter actions, David A.Brown and Anthony M.Brown.
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