Neural Circuits Reveal Flexible Perceptual Inference Mechanisms
Schwarcz et al. (published December 1, 2025) introduce a change-detection task and show mice rapidly adapt decisions to latent context shifts, often on the first trial without explicit feedback. By deriving the Bayes-optimal policy for a partially observable Markov decision process, they show rapid adaptation arises from sequential belief-state updates. Reinforcement-learning-trained recurrent networks achieve near-optimal performance, mirroring Bayesian-like internal dynamics and flexible inference.
Key Points
- 1Demonstrate mice show first-trial adaptation to latent context shifts without reward feedback
- 2Show Bayesian belief-state updates under a POMDP account for rapid context-dependent adaptation
- 3Indicate recurrent neural networks trained with reinforcement learning implement Bayesian-like inference dynamics
Scoring Rationale
Strong experimental and computational evidence supports flexible inference, but findings are specific to mouse behavior and controlled tasks.
Sources
Public references used for this report.
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