RT Journal Article
SR Electronic
T1 Dynamical Latent State Computation in the Posterior Parietal Cortex
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.01.12.476065
DO 10.1101/2022.01.12.476065
A1 Kaushik J Lakshminarasimhan
A1 Eric Avila
A1 Xaq Pitkow
A1 Dora E Angelaki
YR 2022
UL http://biorxiv.org/content/early/2022/01/12/2022.01.12.476065.abstract
AB 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 StatementThe authors have declared no competing interest.