Preprint Article Version 1 This version is not peer-reviewed

The Capacity for Correlated Semantic Memories in the Cortex

Version 1 : Received: 14 August 2018 / Approved: 14 August 2018 / Online: 14 August 2018 (12:33:52 CEST)

A peer-reviewed article of this Preprint also exists.

Boboeva, V.; Brasselet, R.; Treves, A. The Capacity for Correlated Semantic Memories in the Cortex. Entropy 2018, 20, 824. Boboeva, V.; Brasselet, R.; Treves, A. The Capacity for Correlated Semantic Memories in the Cortex. Entropy 2018, 20, 824.

Journal reference: Entropy 2018, 20, 824
DOI: 10.3390/e20110824

Abstract

A statistical analysis of semantic memory should reflect the complex, multifactorial structure of the relations among its items. Still, a dominant paradigm in the study of semantic memory has been the idea that the mental representation of concepts is structured along a simple branching tree spanned by superordinate and subordinate categories. We propose a generative model of item representation with correlations that overcomes the limitations of a tree structure. The items are generated through "factors" that represent semantic features or real-world attributes. The correlation between items has its source in the extent to which items share such factors and the strength of such factors: if many factors are balanced, correlations are overall low; whereas if a few factors dominate, they become strong. Our model allows for correlations that are neither trivial nor hierarchical, but may reproduce the general spectrum of correlations present in a data-set of nouns. We provide an estimate of the number of concepts that can be stored and retrieved by a large-scale cortical network, the Potts network, which is perhaps approximately 107 with human cortical parameters. When this storage capacity is exceeded, however, retrieval fails completely only for balanced factors; above a critical degree of imbalance, a phase transition leads to a regime where the network still extracts considerable information about the cued item, even if not recovering its detailed representation: partial categorization seems to emerge spontaneously as a consequence of the dominance of particular factors, rather than being imposed ad hoc. We argue this to be a relevant model of semantic memory resilience in Tulving’s remember/know paradigms.

Subject Areas

potts network; attractor neural networks; auto-associative memory; cortex; semantic memory

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