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Published in: Neural Computing and Applications 17/2020

28-01-2020 | Original Article

A biologically plausible network model for pattern storage and recall inspired by Dentate Gyrus

Authors: V. Vidya Janarthanam, S. Vishwanath, A. P. Shanthi

Published in: Neural Computing and Applications | Issue 17/2020

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Abstract

In the race to achieve better performance, artificial intelligence has become more about the end rather than the means, which is general intelligence. This work aims to bridge the gap between the two by finding a complementary midline. The objective of this work is to project the role of Dentate Gyrus in enhancing the performance of an autoassociative network, paving the way to develop a biologically plausible neural network which, in the future, would help in simulating the network present in our brain. The proposed network imbibes biological similarities with respect to connectivity, weight updation, and activation function. Dentate Gyrus uses pre-integration lateral inhibition form of learning, and the autoassociative network is implemented using Hopfield network. The performance of the autoassociative network in the presence and absence of Dentate Gyrus is observed across multiple parameters. The results show an increase of 38% in storage capacity and a decrease of 15% in the error tolerance capability of the network in the presence of Dentate Gyrus.

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Metadata
Title
A biologically plausible network model for pattern storage and recall inspired by Dentate Gyrus
Authors
V. Vidya Janarthanam
S. Vishwanath
A. P. Shanthi
Publication date
28-01-2020
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 17/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04670-3

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