title: Optimal stimulus coding by neural populations using rate codes.
abstract: We create a framework based on Fisher information for determining the most effective population coding scheme for representing a continuous-valued stimulus attribute over its entire range. Using this scheme, we derive optimal single- and multi-neuron rate codes for homogeneous populations using several statistical models frequently used to describe neural data. We show that each neuron's discharge rate should increase quadratically with the stimulus and that statistically independent neural outputs provides optimal coding. Only cooperative populations can achieve this condition in an informationally effective way.
authors.affiliation: Department of Electrical & Computer Engineering, MS 366, Rice University, 6100 Main Street, Houston, TX 77251-1892, USA. dhj@rice.edu
authors.forename: Don H
authors.initials: DH
authors.lastname: Johnson
authors.forename: Will
authors.initials: W
authors.lastname: Ray
mesh_terms: D000200:Action Potentials; D000818:Animals; D004594:Electrophysiology; D005568:Forms and Records Control; D007257:Information Theory; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D011153:Population