Systematic errors in connectivity inferred from activity in strongly recurrent networks
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512053v1.full.pdf
Description
Submitted version
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19.28 MB
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Adobe PDF
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Author(s)
Das, Abhranil
Fiete, Ila R
Date Issued
2020
Journal
Nature Neuroscience
Publisher
Springer Science and Business Media LLC
Version
Original manuscript
Abstract
© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc. Understanding the mechanisms of neural computation and learning will require knowledge of the underlying circuitry. Because it is difficult to directly measure the wiring diagrams of neural circuits, there has long been an interest in estimating them algorithmically from multicell activity recordings. We show that even sophisticated methods, applied to unlimited data from every cell in the circuit, are biased toward inferring connections between unconnected but highly correlated neurons. This failure to ‘explain away’ connections occurs when there is a mismatch between the true network dynamics and the model used for inference, which is inevitable when modeling the real world. Thus, causal inference suffers when variables are highly correlated, and activity-based estimates of connectivity should be treated with special caution in strongly connected networks. Finally, performing inference on the activity of circuits pushed far out of equilibrium by a simple low-dimensional suppressive drive might ameliorate inference bias.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1038/S41593-020-0699-2
