Treffer: A biologically inspired dual-network memory model for reduction of catastrophic forgetting

Title:
A biologically inspired dual-network memory model for reduction of catastrophic forgetting
Authors:
HATTORI, Motonobu
Source:
Neurocomputing (Amsterdam). 134:262-268
Publisher Information:
Amsterdam: Elsevier, 2014.
Publication Year:
2014
Physical Description:
print, 19 ref
Original Material:
INIST-CNRS
Subject Terms:
Cognition, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Gestion des mémoires et des fichiers (y compris la protection et la sécurité des fichiers), Memory and file management (including protection and security), Systèmes d'information. Bases de données, Information systems. Data bases, Intelligence artificielle, Artificial intelligence, Connexionnisme. Réseaux neuronaux, Connectionism. Neural networks, Sciences biologiques et medicales, Biological and medical sciences, Sciences biologiques fondamentales et appliquees. Psychologie, Fundamental and applied biological sciences. Psychology, Psychologie. Psychophysiologie, Psychology. Psychophysiology, Processus d'acquisition. Mémoire, Learning. Memory, Mémoire, Memory, Homme, Human, Psychologie. Psychanalyse. Psychiatrie, Psychology. Psychoanalysis. Psychiatry, Chaos, Caos, Cognition, Cognición, Gestion mémoire, Storage management, Gestión memoria, Gyrus dentelé, Dentate gyrus, Circunvolución dentada, Hippocampe, Hippocampus, Hipocampo, Modèle ordre réduit, Reduced order model, Modelo orden reducido, Modélisation, Modeling, Modelización, Mémoire, Memory, Memoria, Oubli, Forgetting, Olvido, Recherche information, Information retrieval, Búsqueda información, Réseau neuronal, Neural network, Red neuronal, Simulation ordinateur, Computer simulation, Simulación computadora, Turnover, Biomimétique, Biomimetics, Biomimética, Catastrophic forgetting, Chaotic neural network, Complementary learning systems, Dual-network, Neuronal turnover
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
ISSN:
0925-2312
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28332173
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Psychology. Ethology

FRANCIS
Accession Number:
edscal.28332173
Database:
PASCAL Archive

Weitere Informationen

Neural networks encounter serious catastrophic forgetting when information is learned sequentially, which is unacceptable for both a model of human memory and practical engineering applications. In this study, we propose a novel biologically inspired dual-network memory model that can significantly reduce catastrophic forgetting. The proposed model consists of two distinct neural networks: hippocampal and neocortical networks. Information is first stored in the hippocampal network, and thereafter, it is transferred to the neocortical network. In the hippocampal network, chaotic behavior of neurons in the CA3 region of the hippocampus and neuronal turnover in the dentate gyrus region are introduced. Chaotic recall by CA3 enables retrieval of stored information in the hippocampal network. Thereafter, information retrieved from the hippocampal network is interleaved with previously stored information and consolidated by using pseudopatterns in the neocortical network. The computer simulation results show the effectiveness of the proposed dual-network memory model.