ArticleScience advances2024
Inductive biases of neural network modularity in spatial navigation.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Neural and computational correlates of strategic aborting and long-run policy optimization in the dorsolateral prefrontal cortex.Nature communications · 2026Article
- Belief embodiment through eye movements facilitates memory-guided navigation.Nature communications · 2025Article
- Fragmentation and multithreading of experience in the default-mode network.Nature communications · 2025Article
- Diverging network architecture of theArXiv · 2025Article
- Spatial memory deficits in Parkinson's disease: neural mechanisms and assessment.American journal of neurodegenerative disease · 2025Review
- Emergence of belief-like representations through reinforcement learning.PLoS computational biology · 2023Article
- Context-invariant beliefs are supported by dynamic reconfiguration of single unit functional connectivity in prefrontal cortex.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
The brain may have evolved a modular architecture for daily tasks, with circuits featuring functionally specialized modules that match the task structure. We hypothesize that this architecture enables better learning and generalization than architectures with less specialized modules. To test this, we trained reinforcement learning agents with various neural architectures on a naturalistic navigation task. We found that the modular agent, with an architecture that segregates computations of state representation, value, and action into specialized modules, achieved better learning and generalization. Its learned state representation combines prediction and observation, weighted by their relative uncertainty, akin to recursive Bayesian estimation. This agent's behavior also resembles macaques' behavior more closely. Our results shed light on the possible rationale for the brain's modularity and suggest that artificial systems can use this insight from neuroscience to improve learning and generalization in natural tasks.
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