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Rapid context inference in a thalamocortical model using recurrent neural networks.

Wei-Long Zheng1,2,3, Zhongxuan Wu4, Ali Hummos5

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This study introduces a neural circuit model of prefrontal cortex (PFC) and mediodorsal thalamus (MD) interactions for rapid context inference. The model demonstrates how Hebbian plasticity supports cognitive flexibility and continual learning, mitigating catastrophic forgetting.

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Area of Science:

  • Computational Neuroscience
  • Cognitive Neuroscience

Background:

  • Cognitive flexibility is essential for context-appropriate behavior.
  • Thalamocortical circuits, particularly prefrontal cortex (PFC) and mediodorsal thalamus (MD) interactions, are vital for temporal context inference.
  • The precise neural mechanisms underlying context inference remain unclear.

Purpose of the Study:

  • To propose and investigate a PFC-MD neural circuit model for rapid, online context inference.
  • To elucidate the neural mechanisms enabling cognitive flexibility and continual learning.

Main Methods:

  • Developed a computational model of PFC-MD interactions incorporating Hebbian plasticity.
  • Implemented pre-synaptic traces, adaptive thresholding, and winner-take-all normalization in the model.
  • Simulated sequential learning of cognitive tasks to evaluate model performance.

Main Results:

  • The model MD thalamus successfully inferred temporal contexts from PFC inputs within a few trials.
  • The PFC-MD model demonstrated continual learning by gating context-irrelevant PFC neurons.
  • The model alleviated catastrophic forgetting and showed knowledge transfer across tasks.

Conclusions:

  • Biological properties of thalamocortical circuits can support rapid context inference and continual learning.
  • The proposed PFC-MD model offers insights into the neural basis of cognitive flexibility.
  • Hebbian plasticity in thalamocortical circuits is a key mechanism for adaptive behavior.