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Elife. 2018 Jul 9;7. pii: e33503. doi: 10.7554/eLife.33503.

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

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Department of Psychology, The University of California, Berkeley, United States.
Department of Physics, The University of Texas, Austin, United States.
Center for Learning and Memory, The University of Texas, Austin, United States.


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 the distribution of relative phase shifts (DRPS) 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.


attractor dynamics; circuit perturbation; grid cells; neuroscience; none; recurrent networks

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