Paper Info
Title
An analysis of connectivity and function in hippocampal associative memory
Abstract
We discuss effects of realistically dilute connectivity on a neural network previously proposed as a model of hippocampal associative memory. Several criteria for setting neuronal activation thresholds are studied. Even with optimal thresholding, dilution induces a substantial information loss in stored patterns compared to presented patterns. Consequently, a stricter constraint than previous ones arises on the model's storage capacity. Furthermore, we argue that such constraints depend sensitively on the specific, subjective criteria chosen for storage quality. We thus propose that additional performance measures be considered. In particular, the relationship between firing rates of original and attractor patterns is discussed.
Year
DOI
Venue
1999
10.1016/S0925-2312(99)00048-X
Neurocomputing
Keywords
Field
DocType
Attractor neural networks,Associative memory,Hippocampus
Attractor,Information loss,Content-addressable memory,Pattern recognition,Bidirectional associative memory,Artificial intelligence,Thresholding,Artificial neural network,Hippocampal formation,Hippocampus,Mathematics,Machine learning
Journal
Volume
ISSN
Citations 
26-27
0925-2312
0
PageRank 
References 
Authors
0.34
2
1
Name
Order
Citations
PageRank
Miguel Maravall151.58