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Rapid context inference in a thalamocortical model using recurrent neural networks

Computer Science

Rapid context inference in a thalamocortical model using recurrent neural networks

W. Zheng, Z. Wu, et al.

This groundbreaking research by Wei-Long Zheng, Zhongxuan Wu, Ali Hummos, Guangyu Robert Yang, and Michael M. Halassa explores a computational model illustrating how the interaction of the prefrontal cortex and mediodorsal thalamus can lead to rapid context inference. Discover how this innovative model supports continual learning while mitigating catastrophic forgetting in sequential cognitive tasks.

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Playback language: English
Abstract
This paper presents a computational model of the prefrontal cortex (PFC) and mediodorsal thalamus (MD) interaction that utilizes a Hebbian plasticity rule to support rapid, online context inference. The model demonstrates that the MD thalamus can infer temporal contexts from PFC inputs within a few trials, achieved through PFC-to-MD synaptic plasticity with pre-synaptic traces and adaptive thresholding, along with winner-take-all normalization in the MD. The model also shows how the thalamus gates context-irrelevant neurons in the PFC, facilitating continual learning and alleviating catastrophic forgetting. The model's performance is evaluated on sequential cognitive tasks, showcasing its ability to switch between contexts and transfer knowledge.
Publisher
Nature Communications
Published On
Sep 27, 2024
Authors
Wei-Long Zheng, Zhongxuan Wu, Ali Hummos, Guangyu Robert Yang, Michael M. Halassa
Tags
prefrontal cortex
mediodorsal thalamus
Hebbian plasticity
context inference
continual learning
catastrophic forgetting
synaptic plasticity
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