Fig. 1: The PFC-MD model framework with the synaptic plasticity.
From: Rapid context inference in a thalamocortical model using recurrent neural networks
A A cortico-thalamic neural network model with a Hebbian learning rule in the PFC-to-MD connections to infer temporal context and MD gating in the PFC. B The challenge in continual learning. Standard artificial neural networks modeling one brain region are optimized on single contexts and suffer from catastrophic forgetting. The learned critical model parameters of old contexts are changed in new contexts. The right arrows denote the learning process with context switch. C We propose a synaptic plasticity with pre-synaptic and post-synaptic traces, adaptive thresholding, and winner-take-all in the MD to make the neural network infer temporal contexts and enable continual learning.
