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The Capacity for Correlated Semantic Memories in the Cortex

Cognitive Neuroscience, SISSA—International School for Advanced Studies, Via Bonomea 265, 34136 Trieste, Italy
Kavli Institute for Systems Neuroscience/Centre for Neural Computation, Norwegian University of Science and Technology, 7491 Trondheim, Norway
Author to whom correspondence should be addressed.
Entropy 2018, 20(11), 824;
Received: 14 August 2018 / Revised: 11 October 2018 / Accepted: 23 October 2018 / Published: 26 October 2018
(This article belongs to the Special Issue Statistical Mechanics of Neural Networks)
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 dataset of nouns. We find that such correlations reduce the storage capacity of a Potts network to a limited extent, so that the number of concepts that can be stored and retrieved in a large, human-scale cortical network may still be of order 107, as originally estimated without correlations. 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. View Full-Text
Keywords: Potts network; attractor neural networks; autoassociative memory; cortex; semantic memory Potts network; attractor neural networks; autoassociative memory; cortex; semantic memory
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MDPI and ACS Style

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

AMA Style

Boboeva V, Brasselet R, Treves A. The Capacity for Correlated Semantic Memories in the Cortex. Entropy. 2018; 20(11):824.

Chicago/Turabian Style

Boboeva, Vezha, Romain Brasselet, and Alessandro Treves. 2018. "The Capacity for Correlated Semantic Memories in the Cortex" Entropy 20, no. 11: 824.

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