Reading a Neural Code

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 th...

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Bibliographic Details
Published inScience (American Association for the Advancement of Science) Vol. 252; no. 5014; pp. 1854 - 1857
Main Authors Bialek, William, Rieke, Fred, Rob R. de Ruyter van Steveninck, Warland, David
Format Journal Article
LanguageEnglish
Published Washington, DC American Society for the Advancement of Science 28.06.1991
American Association for the Advancement of Science
The American Association for the Advancement of Science
Subjects
Algorithms
Animals
Biochemistry. Physiology. Immunology
Biological and medical sciences
Cellular biology
Decryption
Diptera
Fundamental and applied biological sciences. Psychology
Insecta
Invertebrates
Mathematics
Modeling
Models, Neurological
Nervous system
Neurons
Neurons - physiology
Neurons, Afferent - physiology
Noise spectra
Photoreceptor Cells - physiology
Photoreceptors
Physiology. Development
Sensory stimulation
Signal noise
Spectral energy distribution
Vision research
Visual Perception
Visual system
Waveforms
White noise
Insecta
Central nervous system
Discharge pattern
Calliphora erythrocephala
Decoding
Algorithm
Visual stimulus
Visual pathway
Calliphoridae
Coding
Arthropoda
Neural integration
Simulation model
Invertebrata
Diptera
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Summary: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.
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ISSN:0036-8075
1095-9203
DOI:10.1126/science.2063199