# Avalanches and Generalized Memory Associativity in a Network Model for Conscious and Unconscious Mental Functioning

## TL;DR

This paper investigates avalanche dynamics in a complex network model of brain memory systems, linking unconscious and conscious processes, and compares simulation results with neuroimaging data to support nonextensive statistical mechanics.

## Contribution

It introduces a model combining unconscious and conscious memory modules using a generalized Boltzmann Machine, analyzing avalanche distributions to connect brain activity patterns with nonextensive statistics.

## Key findings

- Avalanche size distributions match fMRI signal propagation patterns.
- Nonextensive statistical mechanics better models brain network dynamics.
- The model supports Freud's ideas on unconscious and conscious memory interactions.

## Abstract

We explore statistical characteristics of avalanches associated with the dynamics of a complex-network model, where two modules corresponding to sensorial and symbolic memories interact, representing unconscious and conscious mental processes. The model illustrates Freud's ideas regarding the neuroses and that consciousness is related with symbolic and linguistic memory activity in the brain. It incorporates the Stariolo-Tsallis generalization of the Boltzmann Machine in order to model memory retrieval and associativity. In the present work, we define and measure avalanche size distributions during memory retrieval, in order to gain insight regarding basic aspects of the functioning of these complex networks. The avalanche sizes defined for our model should be related to the time consumed and also to the size of the neuronal region which is activated, during memory retrieval. This allows the qualitative comparison of the behaviour of the distribution of cluster sizes, obtained during fMRI measurements of the propagation of signals in the brain, with the distribution of avalanche sizes obtained in our simulation experiments. This comparison corroborates the indication that the Nonextensive Statistical Mechanics formalism may indeed be more well suited to model the complex networks which constitute brain and mental structure.