On the K-winners-take-all-network
Advances in neural information processing systems 1
A winner-take-all mechanism based on presynaptic inhibition feedback
Neural Computation
Can Hebbian volume learning explain discontinuities in cortical maps?
Neural Computation
Self-Organizing neural networks
Unsupervised learning and self-organization in networks of spiking neurons
Self-Organizing neural networks
Clustering based on conditional distributions in an auxiliary space
Neural Computation
Unsupervised clustering for nontextual web document classification
Decision Support Systems
Similarity in Perception: A Window to Brain Organization
Journal of Cognitive Neuroscience
Self-organizing neural projections
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Towards a Multimodal Framework for Human Behavior Recognition
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Meaning and the mental lexicon
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
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It is argued that the Self-Organizing Map (SOM) may be implemented in biological neural networks, the cells of which communicate by transsynaptic signals as well as diffuse chemical substances. For the ''Winner Take All'' (WTA) function, a laterally connected neural network seems proper. Hebb's hypothesis about the synaptic modification is replaced in this work by a principle that relates to a chemically interacting small population of neurons. According to this modified law, the synaptic strength vectors also become normalized automatically. The time-variable ''neighborhood function'' needed in the SOM algorithm is most effectively implemented by chemical agents, which are formed or released extracellularly at or in the neighborhood of highly active cells. Such a physiological model then behaves in the same way as the idealized SOM algorithm, which has been found very effective in many information-processing applications.