Feature discovery by competitive learning
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Supervised VQ Learning Based on Temporal Inhibition
IWANN '99 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Foundations and Tools for Neural Modeling
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A model is presented for a neural network with competitive learning that demonstrates the self-organizing capabilities arising from the inclusion of a simple temporal inhibition mechanism within the neural units. This mechanism consists of the inhibition, for a certain time, of the neuron that generates an action potential; such a process is termed Post_Fire inhibition. The neural inhibition period, or degree of inhibition, and the way it is varied during the learning process, represents a decisive factor in the behaviour of the network, in addition to constituting the main basis for the exploitation of the model. Specifically, we show how Post_Fire inhibition is a simple mechanism that promotes the participation of and cooperation between the units comprising the network; it produces self-organized neural responses that reveal spatio–temporal characteristics of input data. Analysis of the inherent properties of the Post_Fire inhibition and the examples presented show its potential for applications such as vector quantization, clustering, pattern recognition, feature extraction and object segmentation. Finally, it should be noted that the Post_Fire inhibition mechanism is treated here as an efficient abstraction of biologically plausible mechanisms, which simplifies its implementation.