Extremely dilute modular neuronal networks: neocortical memory retrieval dynamics

Journal of Computational Neuroscience
Carlo Fulvi Mari

Abstract

A model of columnar networks of neocortical association areas is studied. The neuronal network is composed of many Hebbian autoassociators, or modules, each of which interacts with a relatively small number of the others, randomly chosen. Any module encodes and stores a number of elementary percepts, or features. Memory items, or patterns, are peculiar combinations of features sparsely distributed over the multi-modular network. Any feature stored in any module can be involved in several of the stored patterns; feature-sharing is in fact source of local ambiguities and, consequently, a potential cause of erroneous memory retrieval spreading through the model network in pattern completion tasks. The memory retrieval dynamics of the large modular autoassociator is investigated by combining mathematical analysis and numerical simulations. An oscillatory retrieval process is proposed that is very efficient in overcoming feature-sharing drawbacks; it requires a mechanism that modulates the robustness of local attractors to noise, and neuronal activity sparseness such that quiescent and active modules are about equally noisy to any post-synaptic module.Moreover, it is shown that statistical correlation between 'kinds' of features acr...Continue Reading

Citations

May 25, 2007·Neural Computation·Christopher Johansson, Anders Lansner
Feb 8, 2016·Journal of Computational Neuroscience·Alexis M Dubreuil, Nicolas Brunel
Nov 21, 2013·Network : Computation in Neural Systems·Cristina Meli, Anders Lansner

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