Algorithmic analysis of irrational rotations in a single neuron model
We study computability of real-valued functions and the information needed for simulation of dynamical systems. In particular, we describe application of Kolmogorov complexity theory to computer simulation of irrational rotations in a single neuron model. We deduce the information needed for a parameter in simulating the dynamics by showing a difference in Kolmogorov complexity between a computable parameter and a non-computable parameter. Finally, we show that all trajectories generated by irrational rotations are non-computable iff its parameter is non-computable. (C) 2002 Elsevier Science (USA). All rights reserved.
irrational rotation single neuron model computability kolmogorov complexity algorithmic information theory bayes code mdl code nagumo-sato model sturmian sequence symbolic dynamics