ArticleNature communications2023
Dynamical latent state computation in the male macaque posterior parietal cortex.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 17 citations in OpenAlex.
- Neural and computational correlates of strategic aborting and long-run policy optimization in the dorsolateral prefrontal cortex.Nature communications · 2026Article
- Partitioning Neural Co-Variability.ArXiv · 2026Article
- Belief embodiment through eye movements facilitates memory-guided navigation.Nature communications · 2025Article
- Internal models in active self-motion estimation: role of inertial sensory cues.Journal of neurophysiology · 2025Article
- Large-scale foundation models and generative AI for BigData neuroscience.Neuroscience research · 2025Review
- Article
- A corticostriatal learning mechanism linking excess striatal dopamine and auditory hallucinations.bioRxiv : the preprint server for biology · 2025Article
- Monkeys engage in visual simulation to solve complex problems.Current biology : CB · 2024Article
- Inductive biases of neural network modularity in spatial navigation.Science advances · 2024Article
- Context-invariant beliefs are supported by dynamic reconfiguration of single unit functional connectivity in prefrontal cortex of male macaques.Nature communications · 2024Article
- Specific connectivity optimizes learning in thalamocortical loops.Cell reports · 2024Article
- Context-invariant beliefs are supported by dynamic reconfiguration of single unit functional connectivity in prefrontal cortex.bioRxiv : the preprint server for biology · 2023Article
- Computational cross-species views of the hippocampal formation.Hippocampus · 2023Review
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 1 country.
Funding
Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.