title: Reading a neural code.
abstract: Traditional approaches to neural coding characterize the encoding of known stimuli in average neural responses. Organisms face nearly the opposite task--extracting information about an unknown time-dependent stimulus from short segments of a spike train. Here the neural code was characterized from the point of view of the organism, culminating in algorithms for real-time stimulus estimation based on a single example of the spike train. These methods were applied to an identified movement-sensitive neuron in the fly visual system. Such decoding experiments determined the effective noise level and fault tolerance of neural computation, and the structure of the decoding algorithms suggested a simple model for real-time analog signal processing with spiking neurons.
authors.affiliation: Department of Physics, University of California, Berkeley 94720.
authors.forename: W
authors.initials: W
authors.lastname: Bialek
authors.forename: F
authors.initials: F
authors.lastname: Rieke
authors.forename: R R
authors.initials: RR
authors.lastname: de Ruyter van Steveninck
authors.forename: D
authors.initials: D
authors.lastname: Warland
mesh_terms: D000465:Algorithms; D000818:Animals; D004175:Diptera; D008433:Mathematics; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D009475:Neurons, Afferent / Q000502:physiology*; D010786:Photoreceptor Cells / Q000502:physiology; D014796:Visual Perception