Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
A self-organizing neural network model of the primary visual cortex
A self-organizing neural network model of the primary visual cortex
Mapping of SOM and LVQ algorithms on a tree shape parallel computer system
Parallel Computing
Parallel processing neural networks and genetic algorithms
Advances in Engineering Software
Mapping of neural network models onto massively parallel hierarchical computer systems
Journal of Systems Architecture: the EUROMICRO Journal
Applications of neural networks to digital communications: a survey
Signal Processing - Special issue on emerging techniques for communication terminals
Parallel Architectures for Artificial Neural Networks: Paradigms and Implementations
Parallel Architectures for Artificial Neural Networks: Paradigms and Implementations
Industrial Applications of Neural Networks
Industrial Applications of Neural Networks
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Tilt Aftereffects in a Self-Organizing Model of the Primary Visual Cortex
Neural Computation
Parallel system design for time-delay neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Asynchronous self-organizing maps
IEEE Transactions on Neural Networks
OTM'05 Proceedings of the 2005 OTM Confederated international conference on On the Move to Meaningful Internet Systems
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We present a parallel algorithm for laterally interconnected synergetically self-organizing map (LISSOM) neural network, a self-organizing map with lateral excitatory and inhibitory connections, to enhance the computational efficiency. A general strategy of balancing workload for different sizes of LISSOM networks on parallel computers is described. The parallel algorithm of LISSOM is implemented on IBM SP2 and PC cluster. The results demonstrate the efficiency of this LISSOM parallel algorithm in processing networks with large sizes. Parallel implementation for different input dimensions in networks of the same size (i.e., 20 × 20) show that the speedup can sustain at a high level. We demonstrate the LISSOM can be applied to complex problems through the parallel algorithm devised in this study.