title: Frequency separation by an excitatory-inhibitory network.
abstract: We consider a situation in which individual features of the input are represented in the neural system by different frequencies of periodic firings. Thus, if two of the features are presented concurrently, the input to the system will consist of a superposition of two periodic trains. In this paper we present an algorithm that is capable of extracting the individual features from the composite signal by separating the signal into periodic spike trains with different frequencies. We show that the algorithm can be implemented in a biophysically based excitatory-inhibitory network model. The frequency separation process works over a range of frequencies determined by time constants of the model's intrinsic variables. It does not rely on a "resonance" phenomenon and is not tuned to a discrete set of frequencies. The frequency separation is still reliable when the timing of incoming spikes is noisy.
authors.affiliation: Department of Mathematics, University of Utah, 155 S 1400 E, Salt Lake City, UT 84112, USA. borisyuk@math.utah.edu
authors.forename: Alla
authors.initials: A
authors.lastname: Borisyuk
authors.forename: Janet
authors.initials: J
authors.lastname: Best
authors.forename: David
authors.initials: D
authors.lastname: Terman
mesh_terms: D000465:Algorithms; D000818:Animals; D001703:Biophysics; D003198:Computer Simulation; D006801:Humans; D008564:Membrane Potentials / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009433:Neural Inhibition / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology*