Rules for information maximization in spiking neurons using intrinsic plasticity

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.