We show that nonlinear latent factors and structures in neural population activity can be modelled in a manner that allows for flexible dynamical inference, causally, non-causally and in the presence of missing neural observations. Further, the developed neural network model improves the prediction of neural activity, behaviour and latent neural structures.
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References
Vyas, S., Golub, M. D., Sussillo, D. & Shenoy, K. V. Computation through neural population dynamics. Annu. Rev. Neurosci. 43, 249–275 (2020). This review article overviews the dynamical modelling of neural population activity.
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This is a summary of: Abbaspourazad, H. et al. Dynamical flexible inference of nonlinear latent factors and structures in neural population activity. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-023-01106-1 (2023).
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A neural network that enables flexible nonlinear inference from neural population activity. Nat. Biomed. Eng 8, 9–10 (2024). https://doi.org/10.1038/s41551-023-01111-4
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DOI: https://doi.org/10.1038/s41551-023-01111-4
