Theory of self-organization of cortical maps
Advances in neural information processing systems 1
Introduction to the theory of neural computation
Introduction to the theory of neural computation
A comparative study of the Kohonen self-organizing map and the elastic net
Proceedings of the workshop on Computational learning theory and natural learning systems (vol. 2) : intersections between theory and experiment: intersections between theory and experiment
A type of duality between self-organizing maps and minimal wiring
Neural Computation
A theoretical comparison of batch-mode, on-line, cyclic, and almost-cyclic learning
IEEE Transactions on Neural Networks
Lateral and elastic interactions: deriving one form from another
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Deriving cortical maps and elastic nets from topology-preserving maps
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
| Hi-index | 0.00 |
We have developed a topology-preserving elastic net which combines both lateral and synaptic interactions to obtain topologically ordered representations (receptive fields) of an external stimulus. Existing neural models that preserve the topology by utilizing lateral interactions, such as the Kohonen-type maps, and by utilizing synaptic interactions, such as elastic-type nets, appear as limiting cases of this model. Utilizing both lateral and synaptic interactions can be beneficial for preservation of the topology.