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Neural Coding of Natural Stimuli: Information at Sub-Millisecond Resolution

Overview of attention for article published in PLoS Computational Biology, March 2008
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Title
Neural Coding of Natural Stimuli: Information at Sub-Millisecond Resolution
Published in
PLoS Computational Biology, March 2008
DOI 10.1371/journal.pcbi.1000025
Pubmed ID
Authors

Ilya Nemenman, Geoffrey D. Lewen, William Bialek, Rob R. de Ruyter van Steveninck

Abstract

Sensory information about the outside world is encoded by neurons in sequences of discrete, identical pulses termed action potentials or spikes. There is persistent controversy about the extent to which the precise timing of these spikes is relevant to the function of the brain. We revisit this issue, using the motion-sensitive neurons of the fly visual system as a test case. Our experimental methods allow us to deliver more nearly natural visual stimuli, comparable to those which flies encounter in free, acrobatic flight. New mathematical methods allow us to draw more reliable conclusions about the information content of neural responses even when the set of possible responses is very large. We find that significant amounts of visual information are represented by details of the spike train at millisecond and sub-millisecond precision, even though the sensory input has a correlation time of approximately 55 ms; different patterns of spike timing represent distinct motion trajectories, and the absolute timing of spikes points to particular features of these trajectories with high precision. Finally, the efficiency of our entropy estimator makes it possible to uncover features of neural coding relevant for natural visual stimuli: first, the system's information transmission rate varies with natural fluctuations in light intensity, resulting from varying cloud cover, such that marginal increases in information rate thus occur even when the individual photoreceptors are counting on the order of one million photons per second. Secondly, we see that the system exploits the relatively slow dynamics of the stimulus to remove coding redundancy and so generate a more efficient neural code.

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Mendeley readers

The data shown below were compiled from readership statistics for 255 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 11 4%
Germany 6 2%
United Kingdom 4 2%
France 2 <1%
Austria 2 <1%
Canada 2 <1%
Netherlands 2 <1%
Israel 2 <1%
Poland 2 <1%
Other 5 2%
Unknown 217 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 74 29%
Student > Ph. D. Student 69 27%
Student > Master 21 8%
Professor > Associate Professor 16 6%
Professor 14 5%
Other 39 15%
Unknown 22 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 81 32%
Neuroscience 45 18%
Physics and Astronomy 27 11%
Engineering 22 9%
Computer Science 20 8%
Other 30 12%
Unknown 30 12%