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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| Published in | Science (American Association for the Advancement of Science) Vol. 252; no. 5014; pp. 1854 - 1857 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| 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 | |
| Online Access | Get full text |
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| 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. |
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| AbstractList | An approach to neural coding that is characterized from the perspective of an organism is discussed. The neural code culminated in algorithms for real-time stimulus estimation based on the example of a spike train. 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. |
| Audience | Academic |
| Author | Bialek, William Rieke, Fred Warland, David Rob R. de Ruyter van Steveninck |
| Author_xml | – sequence: 1 givenname: William surname: Bialek fullname: Bialek, William – sequence: 2 givenname: Fred surname: Rieke fullname: Rieke, Fred – sequence: 3 fullname: Rob R. de Ruyter van Steveninck – sequence: 4 givenname: David surname: Warland fullname: Warland, David |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=4972648$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/2063199$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1007/978-1-4613-3970-0 10.1121/1.394835 10.1152/jn.1990.64.2.351 10.1002/j.1538-7305.1948.tb01338.x 10.1113/jphysiol.1926.sp002273 10.1085/jgp.41.4.675 10.1007/BF00695351 10.1007/BF01015565 |
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| Keywords | 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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| References | (e_1_2_1_8_1) 1974; 89 (e_1_2_1_10_1) 1987; 81 (e_1_2_1_5_1) 1989; 86 (e_1_2_1_17_1) 1988; 234 (e_1_2_1_16_1) 1981; 30 e_1_2_1_6_1 (e_1_2_1_13_1) 1990; 64 e_1_2_1_12_1 (e_1_2_1_15_1) 1948; 27 (e_1_2_1_3_1) 1990; 2 (e_1_2_1_1_1) 1926; 61 (e_1_2_1_4_1) 1958; 41 e_1_2_1_14_1 (e_1_2_1_2_1) 1990; 59 (e_1_2_1_7_1) 1983; 80 (e_1_2_1_11_1) 1968; 6 (e_1_2_1_20_1) 1975; 14 e_1_2_1_9_1 e_1_2_1_18_1 e_1_2_1_19_1 |
| References_xml | – ident: e_1_2_1_14_1 – ident: e_1_2_1_9_1 doi: 10.1007/978-1-4613-3970-0 – ident: e_1_2_1_6_1 – volume: 14 start-page: 570 year: 1975 ident: e_1_2_1_20_1 publication-title: INVESTIGATIVE OPHTHALMOLOGY – volume: 81 start-page: 665 year: 1987 ident: e_1_2_1_10_1 publication-title: JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA doi: 10.1121/1.394835 – volume: 64 start-page: 351 year: 1990 ident: e_1_2_1_13_1 publication-title: JOURNAL OF NEUROPHYSIOLOGY doi: 10.1152/jn.1990.64.2.351 – volume: 30 start-page: 103 year: 1981 ident: e_1_2_1_16_1 publication-title: ADVANCES IN PHYSIOLOGICAL SCIENCES – volume: 27 start-page: 379 year: 1948 ident: e_1_2_1_15_1 publication-title: BELL SYSTEM TECHNICAL JOURNAL doi: 10.1002/j.1538-7305.1948.tb01338.x – ident: e_1_2_1_19_1 – volume: 80 start-page: 2799 year: 1983 ident: e_1_2_1_7_1 publication-title: Proceedings of the National Academy of Sciences of the United States of America – volume: 234 start-page: 379 year: 1988 ident: e_1_2_1_17_1 publication-title: PROCEEDINGS OF THE ROYAL SOCIETY OF LONDON SERIES B-BIOLOGICAL SCIENCES – volume: 6 start-page: 221 year: 1968 ident: e_1_2_1_11_1 publication-title: NEUROSCIENCES RESEARCH PROGRAM BULLETIN – volume: 61 start-page: 49 year: 1926 ident: e_1_2_1_1_1 publication-title: JOURNAL OF PHYSIOLOGY-LONDON doi: 10.1113/jphysiol.1926.sp002273 – ident: e_1_2_1_18_1 – volume: 41 start-page: 675 year: 1958 ident: e_1_2_1_4_1 publication-title: JOURNAL OF GENERAL PHYSIOLOGY doi: 10.1085/jgp.41.4.675 – volume: 89 start-page: 331 year: 1974 ident: e_1_2_1_8_1 publication-title: JOURNAL OF COMPARATIVE PHYSIOLOGY doi: 10.1007/BF00695351 – volume: 59 start-page: 103 year: 1990 ident: e_1_2_1_2_1 publication-title: JOURNAL OF STATISTICAL PHYSICS doi: 10.1007/BF01015565 – ident: e_1_2_1_12_1 – volume: 2 start-page: 513 year: 1990 ident: e_1_2_1_3_1 publication-title: 1989 Lectures in Complex Systems, SFI Studies in the Sciences of Complexity – volume: 86 start-page: 1698 year: 1989 ident: e_1_2_1_5_1 publication-title: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA |
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| Snippet | Traditional approaches to neural coding characterize the encoding of known stimuli in average neural responses. Organisms face nearly the opposite... An approach to neural coding that is characterized from the perspective of an organism is discussed. The neural code culminated in algorithms for real-time... |
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| SubjectTerms | 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 |
| Title | Reading a Neural Code |
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