title: Selective population rate coding: a possible computational role of gamma oscillations in selective attention.
abstract: Selective attention is often accompanied by gamma oscillations in local field potentials and spike field coherence in brain areas related to visual, motor, and cognitive information processing. Gamma oscillations are implicated to play an important role in, for example, visual tasks including object search, shape perception, and speed detection. However, the mechanism by which gamma oscillations enhance cognitive and behavioral performance of attentive subjects is still elusive. Using feedforward fan-in networks composed of spiking neurons, we examine a possible role for gamma oscillations in selective attention and population rate coding of external stimuli. We implement the concept proposed by Fries ( 2005 ) that under dynamic stimuli, neural populations effectively communicate with each other only when there is a good phase relationship among associated gamma oscillations. We show that the downstream neural population selects a specific dynamic stimulus received by an upstream population and represents it by population rate coding. The encoded stimulus is the one for which gamma rhythm in the corresponding upstream population is resonant with the downstream gamma rhythm. The proposed role for gamma oscillations in stimulus selection is to enable top-down control, a neural version of time division multiple access used in communication engineering.
authors.affiliation: Graduate School of Information Science and Technology, University of Tokyo, Bunkyo, Tokyo 113-8656, Japan. masuda@mist.i.u-tokyo.ac.jp
authors.forename: Naoki
authors.initials: N
authors.lastname: Masuda
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D001288:Attention / Q000502:physiology*; D001683:Biological Clocks / Q000502:physiology*; D001931:Brain Mapping; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009433:Neural Inhibition; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D011930:Reaction Time / Q000502:physiology; D013569:Synapses / Q000502:physiology; D013997:Time Factors