Construction of a Hierarchical Organization in Semantic Memory: A Model Based on Neural Masses and Gamma-Band Synchronization

Semantic memory is characterized by a hierarchical organization of concepts based on shared properties. However, this aspect is insufficiently dealt with in recent neurocomputational models. Moreover, in many cognitive problems that exploit semantic memory, gamma-band synchronization can be relevant in favoring information processing and feature binding. In this work, we propose an attractor network model of semantic memory. Each computational unit, coding for a different feature, is described with a neural mass circuit oscillating in the gamma range. The model is trained with an original nonsymmetric Hebb rule based on a presynaptic gating mechanism. After training, the network creates a taxonomy of categories, distinguishes between subordinate and superordinate concepts, and discriminates between salient and marginal features. Examples are provided concerning a fourteen-animal taxonomy, including several subcategories. A sensitivity analysis reveals the robustness of the network but also points out conditions leading to confusion among categories, similar to the one observed in dreaming and some neurological disorders. Finally, the analysis emphasizes the role of fast GABAergic interneurons and inhibitory-excitatory balance to allow the correct synchronization of features. The model represents an original attempt to deal with a hierarchical organization of objects in semantic memory and correlated patterns, still exploiting gamma-band synchronization to favor neural processing. The same ideas, introduced in a more sophisticated multilayer network, can deepen our knowledge of semantic memory organization in the brain. Finally, they can open new perspectives in quantitatively analyzing neurological disorders connected with distorted semantics.

Automated summary

This study explores the hierarchical organization of semantic memory using an attractor network model. The model utilizes gamma-band synchronization to facilitate information processing and feature binding, and creates a taxonomy, distinguishes concepts, and discriminates features through training. Sensitivity analysis reveals the robustness of the network, but also identifies conditions that may lead to confusion. The analysis emphasizes the role of GABAergic interneurons and inhibitory-excitatory balance in feature synchronization.