Cargando…
Specific excitatory connectivity for feature integration in mouse primary visual cortex
Local excitatory connections in mouse primary visual cortex (V1) are stronger and more prevalent between neurons that share similar functional response features. However, the details of how functional rules for local connectivity shape neuronal responses in V1 remain unknown. We hypothesised that co...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5746254/ https://www.ncbi.nlm.nih.gov/pubmed/29240769 http://dx.doi.org/10.1371/journal.pcbi.1005888 |
_version_ | 1783289068123062272 |
---|---|
author | Muir, Dylan R. Molina-Luna, Patricia Roth, Morgane M. Helmchen, Fritjof Kampa, Björn M. |
author_facet | Muir, Dylan R. Molina-Luna, Patricia Roth, Morgane M. Helmchen, Fritjof Kampa, Björn M. |
author_sort | Muir, Dylan R. |
collection | PubMed |
description | Local excitatory connections in mouse primary visual cortex (V1) are stronger and more prevalent between neurons that share similar functional response features. However, the details of how functional rules for local connectivity shape neuronal responses in V1 remain unknown. We hypothesised that complex responses to visual stimuli may arise as a consequence of rules for selective excitatory connectivity within the local network in the superficial layers of mouse V1. In mouse V1 many neurons respond to overlapping grating stimuli (plaid stimuli) with highly selective and facilitatory responses, which are not simply predicted by responses to single gratings presented alone. This complexity is surprising, since excitatory neurons in V1 are considered to be mainly tuned to single preferred orientations. Here we examined the consequences for visual processing of two alternative connectivity schemes: in the first case, local connections are aligned with visual properties inherited from feedforward input (a ‘like-to-like’ scheme specifically connecting neurons that share similar preferred orientations); in the second case, local connections group neurons into excitatory subnetworks that combine and amplify multiple feedforward visual properties (a ‘feature binding’ scheme). By comparing predictions from large scale computational models with in vivo recordings of visual representations in mouse V1, we found that responses to plaid stimuli were best explained by assuming feature binding connectivity. Unlike under the like-to-like scheme, selective amplification within feature-binding excitatory subnetworks replicated experimentally observed facilitatory responses to plaid stimuli; explained selective plaid responses not predicted by grating selectivity; and was consistent with broad anatomical selectivity observed in mouse V1. Our results show that visual feature binding can occur through local recurrent mechanisms without requiring feedforward convergence, and that such a mechanism is consistent with visual responses and cortical anatomy in mouse V1. |
format | Online Article Text |
id | pubmed-5746254 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-57462542018-01-10 Specific excitatory connectivity for feature integration in mouse primary visual cortex Muir, Dylan R. Molina-Luna, Patricia Roth, Morgane M. Helmchen, Fritjof Kampa, Björn M. PLoS Comput Biol Research Article Local excitatory connections in mouse primary visual cortex (V1) are stronger and more prevalent between neurons that share similar functional response features. However, the details of how functional rules for local connectivity shape neuronal responses in V1 remain unknown. We hypothesised that complex responses to visual stimuli may arise as a consequence of rules for selective excitatory connectivity within the local network in the superficial layers of mouse V1. In mouse V1 many neurons respond to overlapping grating stimuli (plaid stimuli) with highly selective and facilitatory responses, which are not simply predicted by responses to single gratings presented alone. This complexity is surprising, since excitatory neurons in V1 are considered to be mainly tuned to single preferred orientations. Here we examined the consequences for visual processing of two alternative connectivity schemes: in the first case, local connections are aligned with visual properties inherited from feedforward input (a ‘like-to-like’ scheme specifically connecting neurons that share similar preferred orientations); in the second case, local connections group neurons into excitatory subnetworks that combine and amplify multiple feedforward visual properties (a ‘feature binding’ scheme). By comparing predictions from large scale computational models with in vivo recordings of visual representations in mouse V1, we found that responses to plaid stimuli were best explained by assuming feature binding connectivity. Unlike under the like-to-like scheme, selective amplification within feature-binding excitatory subnetworks replicated experimentally observed facilitatory responses to plaid stimuli; explained selective plaid responses not predicted by grating selectivity; and was consistent with broad anatomical selectivity observed in mouse V1. Our results show that visual feature binding can occur through local recurrent mechanisms without requiring feedforward convergence, and that such a mechanism is consistent with visual responses and cortical anatomy in mouse V1. Public Library of Science 2017-12-14 /pmc/articles/PMC5746254/ /pubmed/29240769 http://dx.doi.org/10.1371/journal.pcbi.1005888 Text en © 2017 Muir et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Muir, Dylan R. Molina-Luna, Patricia Roth, Morgane M. Helmchen, Fritjof Kampa, Björn M. Specific excitatory connectivity for feature integration in mouse primary visual cortex |
title | Specific excitatory connectivity for feature integration in mouse primary visual cortex |
title_full | Specific excitatory connectivity for feature integration in mouse primary visual cortex |
title_fullStr | Specific excitatory connectivity for feature integration in mouse primary visual cortex |
title_full_unstemmed | Specific excitatory connectivity for feature integration in mouse primary visual cortex |
title_short | Specific excitatory connectivity for feature integration in mouse primary visual cortex |
title_sort | specific excitatory connectivity for feature integration in mouse primary visual cortex |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5746254/ https://www.ncbi.nlm.nih.gov/pubmed/29240769 http://dx.doi.org/10.1371/journal.pcbi.1005888 |
work_keys_str_mv | AT muirdylanr specificexcitatoryconnectivityforfeatureintegrationinmouseprimaryvisualcortex AT molinalunapatricia specificexcitatoryconnectivityforfeatureintegrationinmouseprimaryvisualcortex AT rothmorganem specificexcitatoryconnectivityforfeatureintegrationinmouseprimaryvisualcortex AT helmchenfritjof specificexcitatoryconnectivityforfeatureintegrationinmouseprimaryvisualcortex AT kampabjornm specificexcitatoryconnectivityforfeatureintegrationinmouseprimaryvisualcortex |