TY - JOUR
T1 - Dynamical latent state computation in the male macaque posterior parietal cortex
AU - Lakshminarasimhan, Kaushik J.
AU - Avila, Eric
AU - Pitkow, Xaq
AU - Angelaki, Dora E.
N1 - Funding Information:
The authors express their deepest gratitude to Roozbeh Kiani for performing the Utah array implantations, Erin Neyhart for assisting with the data collection, Karen Wood and Rebecca Meyer for assisting with spike-sorting, and Jing Lin and Jian Chen for their help in programming the stimulus. This work was supported by NIH grant 1R01 DC004260 and 1R01 NS127122 to D.E.A., NSF NeuroNex 1707400 and NSF CAREER IOS-1552868 to X.P, NIH CRCNS 1R01 NS120407-01, 1U19 NS118246, and Simons Collaboration on the Global Brain, grant no. 324143 to X.P. and D.E.A. K.J.L. was supported by the NSF NeuroNex Award DBI-1707398 and the Gatsby Charitable Foundation GAT3780.
Publisher Copyright:
© 2023, The Author(s).
PY - 2023/12
Y1 - 2023/12
N2 - Success in many real-world tasks depends on our ability to dynamically track hidden states of the world. We hypothesized that neural populations estimate these states by processing sensory history through recurrent interactions which reflect the internal model of the world. To test this, 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 task demands shape the neural interactions in PPC, leading them to embody a world model that consolidates information and tracks task-relevant hidden states.
AB - Success in many real-world tasks depends on our ability to dynamically track hidden states of the world. We hypothesized that neural populations estimate these states by processing sensory history through recurrent interactions which reflect the internal model of the world. To test this, 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 task demands shape the neural interactions in PPC, leading them to embody a world model that consolidates information and tracks task-relevant hidden states.
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U2 - 10.1038/s41467-023-37400-4
DO - 10.1038/s41467-023-37400-4
M3 - Article
C2 - 37005470
AN - SCOPUS:85151330632
SN - 2041-1723
VL - 14
SP - 1832
JO - Nature communications
JF - Nature communications
IS - 1
M1 - 1832
ER -