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Specific excitatory connectivity for feature integration ... fileRESEARCH ARTICLE Specific excitatory connectivity for feature integration in mouse primary visual cortex Dylan R. Muir1,2*

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Zurich Open Repository andArchiveUniversity of ZurichMain LibraryStrickhofstrasse 39CH-8057 Zurichwww.zora.uzh.ch

Year: 2017

Specific excitatory connectivity for feature integration in mouse primaryvisual cortex

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

Abstract: Local excitatory connections in mouse primary visual cortex (V1) are stronger and moreprevalent between neurons that share similar functional response features. However, the details of howfunctional rules for local connectivity shape neuronal responses in V1 remain unknown. We hypothe-sised that complex responses to visual stimuli may arise as a consequence of rules for selective excitatoryconnectivity within the local network in the superficial layers of mouse V1. In mouse V1 many neuronsrespond 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 sur-prising, 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 thefirst 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 secondcase, local connections group neurons into excitatory subnetworks that combine and amplify multiplefeedforward visual properties (a feature binding scheme). By comparing predictions from large scalecomputational models with in vivo recordings of visual representations in mouse V1, we found that re-sponses to plaid stimuli were best explained by assuming feature binding connectivity. Unlike underthe like-to-like scheme, selective amplification within feature-binding excitatory subnetworks replicatedexperimentally observed facilitatory responses to plaid stimuli; explained selective plaid responses notpredicted by grating selectivity; and was consistent with broad anatomical selectivity observed in mouseV1. Our results show that visual feature binding can occur through local recurrent mechanisms withoutrequiring feedforward convergence, and that such a mechanism is consistent with visual responses andcortical anatomy in mouse V1.

DOI: https://doi.org/10.1371/journal.pcbi.1005888

Posted at the Zurich Open Repository and Archive, University of ZurichZORA URL: https://doi.org/10.5167/uzh-149563Journal ArticlePublished Version

The following work is licensed under a Creative Commons: Attribution 4.0 International (CC BY 4.0)License.

Originally published at:

https://doi.org/10.1371/journal.pcbi.1005888https://doi.org/10.5167/uzh-149563http://creativecommons.org/licenses/by/4.0/http://creativecommons.org/licenses/by/4.0/

Muir, Dylan R; Molina-Luna, Patricia; Roth, Morgane M; Helmchen, Fritjof; Kampa, Bjrn M (2017).Specific excitatory connectivity for feature integration in mouse primary visual cortex. PLoS Computa-tional Biology, 13(12):e1005888.DOI: https://doi.org/10.1371/journal.pcbi.1005888

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https://doi.org/10.1371/journal.pcbi.1005888

RESEARCH ARTICLE

Specific excitatory connectivity for feature

integration in mouse primary visual cortex

Dylan R. Muir1,2*, Patricia Molina-Luna2, Morgane M. Roth1,2, Fritjof Helmchen2, BjornM. Kampa2,3,4

1 Biozentrum, University of Basel, Basel, Switzerland, 2 Laboratory of Neural Circuit Dynamics, Brain

Research Institute, University of Zurich, Zurich, Switzerland, 3 Department of Neurophysiology, Institute of

Biology 2, RWTH Aachen University, Aachen, Germany, 4 JARA-BRAIN, Aachen, Germany

* [email protected]

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 conse-

quence 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 ex-

citatory 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 sti-

muli 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.

Author summary

The brain is a highly complex structure, with abundant connectivity between nearby neu-

rons in the neocortex, the outermost and evolutionarily most recent part of the brain.

PLOS Computational Biology | https://doi.org/10.1371/journal.pcbi.1005888 December 14, 2017 1 / 33

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OPENACCESS

Citation: Muir DR, Molina-Luna P, Roth MM,

Helmchen F, Kampa BM (2017) Specific excitatory

connectivity for feature integration in mouse

primary visual cortex. PLoS Comput Biol 13(12):

e1005888. https://doi.org/10.1371/journal.

pcbi.1005888

Editor: Udo A. Ernst, University of Bremen,

GERMANY

Received: January 31, 2017

Accepted: November 23, 2017

Published: December 14, 2017

Copyright: 2017 Muir et al. This is an openaccess article distributed under the terms of the

Creative Commons Attribution License, which

permits unrestricted use, distribution, and

reproduction in any medium, provided the original

author and source are credited.

Data Availability Statement: Data and scripts to

generate all figure panels showing new

experimental data are available from figshare,

DOI:10.6084/m9.figshare.c.3517551.v1, URL:

https://doi.org/10.6084/m9.figshare.c.3517551.v1.

Funding: This work was supported by the Velux

Stiftung (grant number 787 to DRM); the Novartis

Foundation (grants to DRM and BMK); the Swiss

National Science Foundation (grant number 31

120480 to BMK); the European Commission FP7

program (grant BrainScales 269921 to FH and

https://doi.org/10.1371/journal.pcbi.1005888http://crossmark.crossref.org/dialog/?doi=10.1371/journal.pcbi.1005888&domain=pdf&date_stamp=2017-12-28http://crossmark.crossref.org/dialog/?doi=10.1371/journal.pcbi.1005888&domain=pdf&date_stamp=2017-12-28http://crossmark.crossref.org/dialog/?doi=10.1371/journal.pcbi.1005888&domain=pdf&date_stamp=2017-12-28http://crossmark.crossref.org/dialog/?doi=10.1371/journal.pcbi.1005888&domain=pdf&date_stamp=2017-12-28http://crossmark.crossref.org/dialog/?doi=10.1371/journal.pcbi.1005888&domain=pdf&date_stamp=2017-12-28http://crossmark.crossref.org/dialog/?doi=10.1371/journal.pcbi.1005888&domain=pdf&date_stamp=2017-12-28https://doi.org/10.1371/journal.pcbi.1005888https://doi.org/10.1371/journal.pcbi.1005888http://creativecommons.org/licenses/by/4.0/https://doi.org/10.6084/m9.figshare.c.3517551.v1https://doi.org/10.6084/m9.figshare.c.3517551.v1

Although the network architecture of the neocortex can appear disordered, connections

between neurons seem to follow certain rules. These rules most likely determine how

information flows through the neural circuits of the brain, but the relationship between

particular connectivity rules and the function of the cortical network is not known. We

built models of visual cortex in the mouse, assuming distinct rules for connectivity, and

examined how the various rules changed the way the models responded to visual stimuli.

We also recorded responses to visual stimuli of populations of neurons in anesthetized

mice, and compared these responses with our model predictions. We found that connec-

tions in neocortex probably follow a connectivity rule that groups together neurons that

differ in simple visual properties, to build more complex representations of visual stimuli.

This finding is surprising because primary visual cortex is assumed to support mainly sim-

ple visual representations. W

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