On the maximization of information flow between spiking neurons.
A feedforward spiking network represents a nonlinear transformation that maps a set of input spikes to a set of output spikes. This mapping transforms the joint probability distribution of incoming spikes into a joint distribution of output spikes. We present an algorithm for synaptic adaptation that aims to maximize the entropy of this output distribution, thereby creating a model for the joint distribution of the incoming point processes. The learning rule that is derived depends on the precise pre- and postsynaptic spike timings. When trained on correlated spike trains, the network learns to extract independent spike trains, thereby uncovering the underlying statistical structure and creating a more efficient representation of the incoming spike trains.
Parra
Lucas C
LC
Biomedical Engineering Department, City College of New York, New York, NY 10033, USA. parra@ccny.cuny.edu
Beck
Jeffrey M
JM
Bell
Anthony J
AJ
D000465:Algorithms; D004594:Electrophysiology; D019277:Entropy; D019706:Excitatory Postsynaptic Potentials / Q000502:physiology; D005246:Feedback; D007257:Information Theory; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D016012:Poisson Distribution