Preserved neural dynamics across animals performing similar behaviour

Mostafa Safaie, Joanna C. Chang, Junchol Park, Lee E. Miller, Joshua T. Dudman, Matthew G. Perich*, Juan A. Gallego*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

Animals of the same species exhibit similar behaviours that are advantageously adapted to their body and environment. These behaviours are shaped at the species level by selection pressures over evolutionary timescales. Yet, it remains unclear how these common behavioural adaptations emerge from the idiosyncratic neural circuitry of each individual. The overall organization of neural circuits is preserved across individuals1 because of their common evolutionarily specified developmental programme2–4. Such organization at the circuit level may constrain neural activity5–8, leading to low-dimensional latent dynamics across the neural population9–11. Accordingly, here we suggested that the shared circuit-level constraints within a species would lead to suitably preserved latent dynamics across individuals. We analysed recordings of neural populations from monkey and mouse motor cortex to demonstrate that neural dynamics in individuals from the same species are surprisingly preserved when they perform similar behaviour. Neural population dynamics were also preserved when animals consciously planned future movements without overt behaviour12 and enabled the decoding of planned and ongoing movement across different individuals. Furthermore, we found that preserved neural dynamics extend beyond cortical regions to the dorsal striatum, an evolutionarily older structure13,14. Finally, we used neural network models to demonstrate that behavioural similarity is necessary but not sufficient for this preservation. We posit that these emergent dynamics result from evolutionary constraints on brain development and thus reflect fundamental properties of the neural basis of behaviour.

Original languageEnglish (US)
Pages (from-to)765-771
Number of pages7
JournalNature
Volume623
Issue number7988
DOIs
StatePublished - Nov 23 2023

Funding

We thank K. Mitchell for suggesting the concept of the genome encoding a \u2018generative model\u2019 and other helpful comments regarding this research. We also thank L. Li for his contribution to the decoder analysis, F. H. Taschbach for suggesting further controls for the decoding analysis and C. Massumoto for the monkey and mouse illustrations. This work was supported in part by: grant no. H2020-MSCA-IF-2020-101025630 from the Commission of the European Union (M.S.), grant no. 108908/Z/15/Z from the Wellcome Trust (J.C.C.), grant nos NS053603 and NS074044 from the NIH National Institute of Neurological Disorders and Stroke (L.E.M.), grant (chercheurs-boursiers en intelligence artificielle) from the Fonds de recherche du Quebec Sant\u00E9 (M.G.P.), grant no. EP/T020970/1 from the UKRI Engineering and Physical Sciences Research Council (J.A.G.) and grant no. ERC-2020-StG-949660 from the European Research Council (J.A.G.).

ASJC Scopus subject areas

  • General

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