Paper Info
Title
Comparison of plasticity of self-optimizing neural networks and natural neural networks
Abstract
The paper interplays between plasticity processes of natural neural networks [9] and Self-Optimizing Neural Networks (SONNs) [7]. The natural neural networks (NNNs) have great possibility in adaptation to environment. The possibility to adapt is based on the chemical processes changing synaptic plasticity and adapting neural network topology during life. The described SONNs are able to adapt their topology to the given problem (i.e. artificial neural network environment) in the functionally similar way the natural neural networks do. The SONNs as well as the NNNs solve together the two essential problems: neural networks topology optimization and weights parameters computation for the given environment. The ontogenic SONNs development gradually adapts network topology specializing the network to the given problem. The fully automatic deterministic self-adapting mechanism of SONNs does not use any a priori configuration parameters and is free from different training problems.
Year
DOI
Venue
2005
10.1007/11499220_17
IWINAC (1)
Keywords
Field
DocType
neural network topology,natural neural network,neural network,topology optimization,different training problem,artificial neural network environment,essential problem,adapts network topology,ontogenic sonns development,great possibility,synaptic plasticity,network topology,artificial neural network
Nervous system network models,Physical neural network,Computer science,Stochastic neural network,Recurrent neural network,Time delay neural network,Types of artificial neural networks,Artificial intelligence,Artificial neural network,Cellular neural network,Machine learning
Conference
Volume
ISSN
ISBN
3561
0302-9743
3-540-26298-9
Citations 
PageRank 
References 
6
0.78
2
Authors
2
Name
Order
Citations
PageRank
Adrian Horzyk15312.76
Ryszard Tadeusiewicz2956141.52