Learning structured representations

SHRUTI is a connectionist model that demonstrates how a network of neuron-like elements can encode a large body of semantic, episodic, and causal knowledge, and rapidly make decisions and perform explanatory and predictive reasoning. To further ground this model in the functioning of the brain it must be shown that components of the model can be learned in a neurally plausible manner. Previous work has already demonstrated the rapid learning of episodic facts via cortico-hippocampal interactions. Here we discuss how other SHRUTI representations such as causal rules, statistical and semantic knowledge, and categories might be learned. (C) 2003 Elsevier Science B.V. All rights reserved.