Inferring circuit mechanisms from sparse neural recording and global perturbation in grid cells
- University of California, Berkeley, United States
- The University of Texas at Austin, United States
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.
Data availability
The Matlab source code to run all the simulations described in this work has been provided as Source Code File 1.
Article and author information
Author details
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.
Copyright
© 2018, Widloski & Fiete
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.
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Inferring circuit mechanisms from sparse neural recording and global perturbation in grid cells
eLife 7:e33503.
https://doi.org/10.7554/eLife.33503
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