Enhancing the performance of a hippocampal model by increasing variability early in learning

Using computer simulations of a minimal computational model of hippocampal region CA3, this report investigates how randomization during training alters learned performance. The transitive inference problem is employed for this purpose. Randomizing just the initial network state at the beginning of...

Full description

Saved in:
Bibliographic Details
Published in:Neurocomputing (Amsterdam) 1999-06, Vol.26, p.601-607
Main Authors: Wu, Xiangbao, Levy, William B
Format: Article
Language:English
Subjects:
Brain
Brain models
Computer simulation
Hippocampal model
Initial randomization
Mathematical models
Neural networks
Neurophysiology
Random processes
Transitive inference
Variability
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Using computer simulations of a minimal computational model of hippocampal region CA3, this report investigates how randomization during training alters learned performance. The transitive inference problem is employed for this purpose. Randomizing just the initial network state at the beginning of each training trial profoundly affects learning. That is, no randomization makes the problem unlearnable while a moderate amount of randomized activity optimizes network performance. These results suggest a way to alter learning which may be tested in neuropsychological experiments.
ISSN:0925-2312
1872-8286
DOI:10.1016/S0925-2312(98)00165-9