Rules for information maximization in spiking neurons using intrinsic plasticity

Authors:
Prashant Joshi;Jochen Triesch
Affiliations:
Frankfurt Institute of Advanced Studies, J. W. Goethe University, Frankfurt, Germany;Frankfurt Institute of Advanced Studies, J. W. Goethe University, Frankfurt, Germany
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

Information theory predicts the need for information maximization as sensory information must be compressed into a limited range of responses that spiking neurons can generate. We propose computational theory and learning rules based on information theory that lead to information maximization using intrinsic plasticity in a stochastically spiking neuron model. Computer simulations are used to verify the theoretical results. Further experiments show that the intrinsic plasticity rules described in this article lead to a desired exponential output distribution, firing-rate homeostasis, and adaptation to sensory deprivation in our model as observed in cortical neurons.