Next Article in Journal
Special Issue on Rate Distortion Theory and Information Theory
Next Article in Special Issue
Modeling of a Neural System Based on Statistical Mechanics
Previous Article in Journal
Dissecting Deep Learning Networks—Visualizing Mutual Information
Previous Article in Special Issue
Statistical Mechanics of On-Line Learning Under Concept Drift
Open AccessFeature PaperArticle

The Capacity for Correlated Semantic Memories in the Cortex

1
Cognitive Neuroscience, SISSA—International School for Advanced Studies, Via Bonomea 265, 34136 Trieste, Italy
2
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; https://doi.org/10.3390/e20110824
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
Show Figures

Figure 1

MDPI and ACS Style

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

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop