Multitasking attractor networks with neuronal threshold noise

Abstract

We consider the multitasking associative network in the low-storage limit and we study its phase diagram with respect to the noise level T and the degree d of dilution in pattern entries. We find that the system is characterized by a rich variety of stable states, including pure states, parallel retrieval states, hierarchically organized states and symmetric mixtures (remarkably, both even and odd), whose complexity increases as the number of patterns P grows. The analysis is performed both analytically and numerically: Exploiting techniques based on partial differential equations, we are able to get the self-consistencies for the order parameters. Such self-consistency equations are then solved and the solutions are further checked through stability theory to catalog their organizations into the phase diagram, which is outlined at the end. This is a further step towards the understanding of spontaneous parallel processing in associative networks.


Autore Pugliese

Tutti gli autori

  • Agliari E. , Barra A. , Galluzzi A. , Isopi M.

Titolo volume/Rivista

NEURAL NETWORKS


Anno di pubblicazione

2014

ISSN

0893-6080

ISBN

Non Disponibile


Numero di citazioni Wos

Nessuna citazione

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Numero di citazioni Scopus

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Settori ERC

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Codici ASJC

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