Inferring circuit mechanisms from sparse neural recording and global perturbation in grid cells

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Abstract

A goal of systems neuroscience is to discover the circuit mechanisms underlying brain function. Despite experimental advances that enable circuit-wide neural recording, the problem remains open in part because solving the 'inverse problem' of inferring circuity and mechanism by merely observing activity is hard. In the grid cell system, we show through modeling that a technique based on global circuit perturbation and examination of a novel theoretical object called distribution of relative phase shifts (DRPS) the could reveal the mechanisms of a cortical circuit at unprecedented detail using extremely sparse neural recordings. We establish feasibility, showing that the method can discriminate between recurrent versus feedforward mechanisms and amongst various recurrent mechanisms using recordings from a handful of cells. The proposed strategy demonstrates that sparse recording coupled with simple perturbation can reveal more about circuit mechanism than can full knowledge of network activity or the synaptic connectivity matrix.

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The Matlab source code to run all the simulations described in this work has been provided as Source Code File 1.

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John Widloski

Department of Psychology, University of California, Berkeley, Berkeley, United States

For correspondence
johnwidloski@berkeley.edu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-4236-8957
Ila R Fiete

Center for Learning and Memory, The University of Texas at Austin, Austin, United States

For correspondence
fiete@mail.clm.utexas.edu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-4738-2539

Funding

Human Frontier Science Program (HFSP-RGP0062/2014)

Ila R Fiete

National Science Foundation (NSF-CRCNS- IIS-1311213)

Ila R Fiete

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

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This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.