A feature-based model of semantic memory: The importance of being chaotic

Abstract

Semantic memory representations have often be modeled in terms of a collection of semantic features. Although feature-based models show a great explanatory power with respect to cognitive and neuropsychological phenomena, they appear to be underspecified if interpreted from a neuro-computational perspective. Here we investigate the retrieval dynamics in a feature-based semantic memory model, in which the features are represented by neurons of the Hindmarsh-Rose type in the chaotic regime. We study the state of synchronization among features coding for the same or different representations and compare the correlation patterns obtained by analyzing the whole neural signal and a manipulated signal in which the sub-threshold component is ruled out. In all cases we find stronger correlations among features belonging to the same representations. We apply a formal method in order to represent the state of synchronization of features which are simultaneously coding for different representations. In this case, the synchronization and de-synchronization pattern that allows for a shared feature to participate in multiple memory representations appears to be better defined when the whole signal is considered. We interpret the simulation results as suggestive of a role for chaotic dynamics in allowing for flexible composition of elementary meaningful units in memory representations. © Springer-Verlag Berlin Heidelberg 2005.