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Dynamical Latent State Computation in the Posterior Parietal Cortex

View ORCID ProfileKaushik J Lakshminarasimhan, Eric Avila, View ORCID ProfileXaq Pitkow, Dora E Angelaki
doi: https://doi.org/10.1101/2022.01.12.476065
Kaushik J Lakshminarasimhan
1Center for Theoretical Neuroscience, Columbia University, New York
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  • For correspondence: jl5649@columbia.edu
Eric Avila
2Center for Neural Science, New York University, New York
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Xaq Pitkow
3Department of Neuroscience, Baylor College of Medicine, Houston
4Electrical & Computer Engineering, Rice University, Houston
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Dora E Angelaki
2Center for Neural Science, New York University, New York
5Department of Mechanical and Aerospace Engineering, New York University, New York
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Summary

Success in many real-world tasks depends on our ability to dynamically track hidden states of the world. To understand the underlying neural computations, we recorded brain activity in posterior parietal cortex (PPC) of monkeys navigating by optic flow to a hidden target location within a virtual environment, without explicit position cues. In addition to sequential neural dynamics and strong interneuronal interactions, we found that the hidden state – monkey’s displacement from the goal – was encoded in single neurons, and could be dynamically decoded from population activity. The decoded estimates predicted navigation performance on individual trials. Task manipulations that perturbed the world model induced substantial changes in neural interactions, and modified the neural representation of the hidden state, while representations of sensory and motor variables remained stable. The findings were recapitulated by a task-optimized recurrent neural network model, suggesting that neural interactions in PPC embody the world model to consolidate information and track task-relevant hidden states.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
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 12, 2022.
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Dynamical Latent State Computation in the Posterior Parietal Cortex
Kaushik J Lakshminarasimhan, Eric Avila, Xaq Pitkow, Dora E Angelaki
bioRxiv 2022.01.12.476065; doi: https://doi.org/10.1101/2022.01.12.476065
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Dynamical Latent State Computation in the Posterior Parietal Cortex
Kaushik J Lakshminarasimhan, Eric Avila, Xaq Pitkow, Dora E Angelaki
bioRxiv 2022.01.12.476065; doi: https://doi.org/10.1101/2022.01.12.476065

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