Evidence map›Paper›PMID 29240769›Full record

ArticlePLoS computational biology2017

Specific excitatory connectivity for feature integration in mouse primary visual cortex.

Dylan R Muir, Patricia Molina-Luna, Morgane M Roth, Fritjof Helmchen, Björn M Kampa

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Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Dylan R MuirBiozentrum, University of Basel, Basel, Switzerland.ORCID 0000-0003-3856-826X
Patricia Molina-LunaLaboratory of Neural Circuit Dynamics, Brain Research Institute, University of Zurich, Zurich, Switzerland.
Morgane M RothBiozentrum, University of Basel, Basel, Switzerland.
Fritjof HelmchenLaboratory of Neural Circuit Dynamics, Brain Research Institute, University of Zurich, Zurich, Switzerland.ORCID 0000-0002-8867-9569
Björn M KampaLaboratory of Neural Circuit Dynamics, Brain Research Institute, University of Zurich, Zurich, Switzerland.ORCID 0000-0002-4343-2634

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Models, NeurologicalAnimalsCalcium SignalingComputational BiologyFemaleMaleMiceMice, Inbred C57BLNerve NetNeuronsOrientationPhotic StimulationSynapsesVisual CortexVisual PathwaysVisual Perception

Identifiers

PMID29240769
PMCPMC5746254

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.