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Systematic Integration of Structural and Functional Data into Multi-Scale Models of Mouse Primary Visual Cortex

Posted: 9 Jul 2019 Publication Status: Review Complete

See all articles by Yazan N. Billeh

Yazan N. Billeh

Allen Institute for Brain Science

Binghuang Cai

Allen Institute for Brain Science

Sergey L. Gratiy

Allen Institute for Brain Science

Kael Dai

Allen Institute for Brain Science

Ramakrishnan Iyer

Allen Institute for Brain Science

Nathan W. Gouwens

Allen Institute for Brain Science

Reza Abbasi-Asl

Allen Institute for Brain Science

Xiaoxuan Jia

Allen Institute for Brain Science

Joshua H. Siegle

Allen Institute for Brain Science

Shawn R. Olsen

Allen Institute for Brain Science

Christof Koch

Allen Institute for Brain Science

Stefan Mihalas

Allen Institute for Brain Science

Anton Arkhipov

Allen Institute for Brain Science

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Abstract

Structural rules underlying functional properties of cortical circuits are poorly understood. To explore these rules systematically, we integrated information from extensive literature curation and large-scale experimental surveys into data-driven, biologically realistic models of the mouse primary visual cortex. The models were constructed at two levels of granularity, using either biophysically-detailed or point-neuron models, with identical network connectivity. Both models were compared to each other and to experimental recordings of neural activity during presentation of visual stimuli to awake mice. Three specific predictions emerge from model construction and simulations: about connectivity between excitatory and parvalbumin-negative inhibitory neurons, functional specialization of connections between excitatory neurons, and the impact of the cortical retinotopic map on structure-function relationships. Finally, despite their vastly different neuronal levels of granularity, both models perform similarly at the level of firing rate distributions. All data and models are freely available as a resource for the community.

Keywords: visual cortex, Modeling, Direction selectivity, Biologically realistic simulations, Cortical circuits

Suggested Citation

Billeh, Yazan N. and Cai, Binghuang and Gratiy, Sergey L. and Dai, Kael and Iyer, Ramakrishnan and Gouwens, Nathan W. and Abbasi-Asl, Reza and Jia, Xiaoxuan and Siegle, Joshua H. and Olsen, Shawn R. and Koch, Christof and Mihalas, Stefan and Arkhipov, Anton, Systematic Integration of Structural and Functional Data into Multi-Scale Models of Mouse Primary Visual Cortex (July 8, 2019). Available at SSRN: https://ssrn.com/abstract=3416643 or http://dx.doi.org/10.2139/ssrn.3416643
This is a paper under consideration at Cell Press and has not been peer-reviewed.

Yazan N. Billeh (Contact Author)

Allen Institute for Brain Science ( email )

615 Westlake Ave N
Seattle, WA 98109
United States

Binghuang Cai

Allen Institute for Brain Science

615 Westlake Ave N
Seattle, WA 98109
United States

Sergey L. Gratiy

Allen Institute for Brain Science

615 Westlake Ave N
Seattle, WA 98109
United States

Kael Dai

Allen Institute for Brain Science

615 Westlake Ave N
Seattle, WA 98109
United States

Ramakrishnan Iyer

Allen Institute for Brain Science

615 Westlake Ave N
Seattle, WA 98109
United States

Nathan W. Gouwens

Allen Institute for Brain Science

615 Westlake Ave N
Seattle, WA 98109
United States

Reza Abbasi-Asl

Allen Institute for Brain Science

615 Westlake Ave N
Seattle, WA 98109
United States

Xiaoxuan Jia

Allen Institute for Brain Science

615 Westlake Ave N
Seattle, WA 98109
United States

Joshua H. Siegle

Allen Institute for Brain Science

615 Westlake Ave N
Seattle, WA 98109
United States

Shawn R. Olsen

Allen Institute for Brain Science

615 Westlake Ave N
Seattle, WA 98109
United States

Christof Koch

Allen Institute for Brain Science

615 Westlake Ave N
Seattle, WA 98109
United States

Stefan Mihalas

Allen Institute for Brain Science

615 Westlake Ave N
Seattle, WA 98109
United States

Anton Arkhipov

Allen Institute for Brain Science ( email )

615 Westlake Ave N
Seattle, WA 98109
United States

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