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Fabrizio Musacchio
@pixeltracker@sigmoid.social  路  activity timestamp 5 days ago

馃 New preprint by Codol et al. (2025): Brain-like #NeuralDynamics for #behavioral control develop through #ReinforcementLearning. They show that only #RL, not #SupervisedLearning, yields neural activity geometries & dynamics matching monkey #MotorCortex recordings. RL-trained #RNNs operate at the edge of #chaos, reproduce adaptive reorganization under #visuomotor rotation, and require realistic limb #biomechanics to achieve brain-like control.

馃實 https://doi.org/10.1101/2024.10.04.616712

#CompNeuro #Neuroscience

Brain-like neural dynamics for behavioral control develop through reinforcement learning

Fig. 2: Neural networks trained with RL or SL achieved high performance in controlling the effector .
Fig. 2: Neural networks trained with RL or SL achieved high performance in controlling the effector .
Fig. 2: Neural networks trained with RL or SL achieved high performance in controlling the effector .
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Fabrizio Musacchio
@pixeltracker@sigmoid.social  路  activity timestamp 2 weeks ago

馃 New paper by Pedamonti et al. (2025, Nature Comm.) shows that the #hippocampus supports multi-task #ReinforcementLearning under partial observability. Mice flexibly inferred hidden task states 馃悂, and only models with recurrent memory reproduced behavior, linking #hippocampal dynamics to #POMDP (Partially Observable Multi-Task Reinforcement Learning) inference.

馃實 https://doi.org/10.1038/s41467-025-64591-9

#Neuroscience #CompNeuro

Fig. 1: Ego-allocentric task setup in animals and reinforcement learning agents.
Fig. 1: Ego-allocentric task setup in animals and reinforcement learning agents.
Fig. 1: Ego-allocentric task setup in animals and reinforcement learning agents.
Fig. 4: Hippocampal RL agents with recurrence capture animal behaviour.
Fig. 4: Hippocampal RL agents with recurrence capture animal behaviour.
Fig. 4: Hippocampal RL agents with recurrence capture animal behaviour.
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