Can excitable media be considered as computational systems?
Selcted papers from a meeting on Waves and pattern in chemical and biological media
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Simple model of spiking neurons
IEEE Transactions on Neural Networks
Which model to use for cortical spiking neurons?
IEEE Transactions on Neural Networks
Facial expression recognition based on liquid state machines built of alternative neuron models
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Towards spatio-temporal pattern recognition using evolving spiking neural networks
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
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The properties of separation ability and computational efficiency of Liquid State Machines depend on the neural model employed and on the connection density in the liquid column. A simple model of part of mammalians visual system consisting of one hypercolumn was examined. Such a system was stimulated by two different input patterns, and the Euclidean distance, as well as the partial and global entropy of the liquid column responses were calculated. Interesting insights could be drawn regarding the properties of different neural models used in the liquid hypercolumn, and on the effect of connection density on the information representation capability of the system.