Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies

Reimann, MW, Riihimäki, H, Smith, JP ORCID: https://orcid.org/0000-0002-4209-1604, Lazovskis, J, Pokorny, C and Levi, R, 2022. Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies. PLOS ONE, 17 (1): e0261702.. ISSN 1932-6203

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Abstract

In motor-related brain regions, movement intention has been successfully decoded from in-vivo spike train by isolating a lower-dimension manifold that the high-dimensional spiking activity is constrained to. The mechanism enforcing this constraint remains unclear, although it has been hypothesized to be implemented by the connectivity of the sampled neurons. We test this idea and explore the interactions between local synaptic connectivity and its ability to encode information in a lower dimensional manifold through simulations of a detailed microcircuit model with realistic sources of noise. We confirm that even in isolation such a model can encode the identity of different stimuli in a lower-dimensional space. We then demonstrate that the reliability of the encoding depends on the connectivity between the sampled neurons by specifically sampling populations whose connectivity maximizes certain topological metrics. Finally, we developed an alternative method for determining stimulus identity from the activity of neurons by combining their spike trains with their recurrent connectivity. We found that this method performs better for sampled groups of neurons that perform worse under the classical approach, predicting the possibility of two separate encoding strategies in a single microcircuit.

Item Type:	Journal article
Publication Title:	PLOS ONE
Creators:	Reimann, M.W., Riihimäki, H., Smith, J.P., Lazovskis, J., Pokorny, C. and Levi, R.
Publisher:	Public Library of Science (PLoS)
Date:	2022
Volume:	17
Number:	1
ISSN:	1932-6203
Identifiers:	Number Type 10.1371/journal.pone.0261702 DOI 1508717 Other
Rights:	© 2022 Reimann et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the originalauthor and source are credited.
Divisions:	Schools > School of Science and Technology
Record created by:	Jeremy Silvester
Date Added:	14 Jan 2022 11:17
Last Modified:	25 Jan 2022 14:10
URI:	https://irep.ntu.ac.uk/id/eprint/45333

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