Paper Info
Title
Optimised Attractor Neural Networks with External Inputs
Abstract
Attractor neural networks resemble the brain in many key aspects, such as their high connectivity, feedback, non-local storage of information and tolerance to damage. The models are also amenable to calculation, using mean field theory, and computer simulation. These methods have enabled properties such as the capacity of the network, the quality of pattern retrieval, and robustness to damage, to be accurately determined. In this paper a biologically motivated input method for external stimuli is studied. A straightforward signal-to-noise calculation gives an indication of the properties of the network. Calculations using mean field theory and including the external stimuli are carried out. A threshold is introduced, the value of which is chosen to optimize the performance of the network, and sparsely coded patterns are considered. The network is shown to have enhanced capacity, improved quality of retrieval, and increased robustness to the random elimination of neurons.
Year
DOI
Venue
1993
10.1007/3-540-56798-4_142
IWANN
Keywords
Field
DocType
external inputs,optimised attractor neural networks,mean field theory,computer simulation,sparse coding
Attractor,Pattern recognition,Computer science,Input method,Robustness (computer science),Mean field theory,Artificial intelligence,Stimulus (physiology),Artificial neural network,Machine learning
Conference
ISBN
Citations 
PageRank 
3-540-56798-4
0
0.34
References 
Authors
1
1
Name
Order
Citations
PageRank
Anthony N. Burkitt148746.71