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Open AccessJournal ArticleDOI

Direction selectivity of neurons in the visual cortex is non‐linear and lamina‐dependent

TLDR
Differences in orientation tuning for non‐preferred vs. preferred directions were smallest in layer 4 and largest in layer 6, consistent with a non‐linear process of intra‐cortical inhibition that enhances DS by selective suppression of neuronal firing for non-preferred directions of stimulus motion in a lamina‐dependent manner.
Abstract
Neurons in the visual cortex are generally selective to direction of movement of a stimulus. Although models of this direction selectivity (DS) assume linearity, experimental data show stronger degrees of DS than those predicted by linear models. Our current study was intended to determine the degree of non-linearity of the DS mechanism for cells within different laminae of the cat's primary visual cortex. To do this, we analysed cells in our database by using neurophysiological and histological approaches to quantify non-linear components of DS in four principal cortical laminae (layers 2/3, 4, 5, and 6). We used a DS index (DSI) to quantify degrees of DS in our sample. Our results showed laminar differences. In layer 4, the main thalamic input region, most neurons were of the simple type and showed high DSI values. For complex cells in layer 4, there was a broad distribution of DSI values. Similar features were observed in layer 2/3, but complex cells were dominant. In deeper layers (5 and 6), DSI value distributions were characterized by clear peaks at high values. Independently of specific lamina, high DSI values were accompanied by narrow orientation tuning widths. Differences in orientation tuning for non-preferred vs. preferred directions were smallest in layer 4 and largest in layer 6. These results are consistent with a non-linear process of intra-cortical inhibition that enhances DS by selective suppression of neuronal firing for non-preferred directions of stimulus motion in a lamina-dependent manner. Other potential mechanisms are also considered.

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Posted ContentDOI

Hierarchical temporal prediction captures motion processing from retina to higher visual cortex

TL;DR: It is shown that hierarchical application of temporal prediction - representing features that efficiently predict future sensory input from past sensory input - can explain how neuronal tuning properties, particularly those relating to motion, change from retina to higher visual cortex.
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.
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.
Journal ArticleDOI

Morphology and intracortical projections of functionally characterised neurones in the cat visual cortex

TL;DR: The neuronal structure and connectivity underlying receptive field organisation of cells in the cat visual cortex have been investigated using a micropipette filled with a histochemical marker to visualise the dendritic and axonal arborisations of functionally identified neurones.
Journal ArticleDOI

A simple white noise analysis of neuronal light responses.

TL;DR: A white noise technique is presented for estimating the response properties of spiking visual system neurons that provides a complete and easily interpretable model of light responses even for neurons that display a common form of response nonlinearity that precludes classical linear systems analysis.
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