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Systematic errors in connectivity inferred from activity in strongly coupled recurrent circuits

Abhranil Das, View ORCID ProfileIla R. Fiete
doi: https://doi.org/10.1101/512053
Abhranil Das
1Department of Physics, The University of Texas, Austin TX 78712
2Center for Learning and Memory, The University of Texas, Austin TX 78712
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Ila R. Fiete
1Department of Physics, The University of Texas, Austin TX 78712
2Center for Learning and Memory, The University of Texas, Austin TX 78712
3Department of Brain and Cognitive Sciences, MIT, Cambridge MA 02139
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Abstract

Understanding the mechanisms of neural computation and learning will require knowledge of the underlying circuitry. Because it is slow, expensive, or often infeasible to directly measure the wiring diagrams of neural microcircuits, there has long been an interest in estimating them from neural recordings. We show that even sophisticated inference algorithms, applied to large volumes of data from every node in the circuit, are biased toward inferring connections between unconnected but strongly correlated neurons, a situation that is common in strongly recurrent circuits. This e ect, representing a failure to fully “explain away” non-existent connections when correlations are strong, occurs when there is a mismatch between the true network dynamics and the generative model assumed for inference, an inevitable situation when we model the real world. Thus, effective connectivity estimates should be treated with especial caution in strongly connected networks when attempting to infer the mechanistic basis of circuit activity. Finally, we show that activity states of networks injected with strong noise or grossly perturbed away from equilibrium may be a promising way to alleviate the problems of bias error.

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  • ↵* fiete{at}mit.edu

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 04, 2019.
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Systematic errors in connectivity inferred from activity in strongly coupled recurrent circuits
Abhranil Das, Ila R. Fiete
bioRxiv 512053; doi: https://doi.org/10.1101/512053
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Systematic errors in connectivity inferred from activity in strongly coupled recurrent circuits
Abhranil Das, Ila R. Fiete
bioRxiv 512053; doi: https://doi.org/10.1101/512053

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