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This article is part of the supplement: Sixteenth Annual Computational Neuroscience Meeting: CNS*2007

Open Access Oral presentation

Neural coding of natural stimuli: information at sub-millisecond resolution

Ilya Nemenman1*, Geoffrey D Lewen2, William Bialek3 and Rob R de Ruyter van Steveninck4

Author Affiliations

1 Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA

2 The Hun School of Princeton, 176 Edgerstoune Road, Princeton, NJ 08540, USA

3 Joseph Henry Laboratories of Physics and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA

4 Department of Physics, Indiana University, Bloomington, IN 47405, USA

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BMC Neuroscience 2007, 8(Suppl 2):S7  doi:10.1186/1471-2202-8-S2-S7

The electronic version of this article is the complete one and can be found online at:


Published:6 July 2007

© 2007 Nemenman et al; licensee BioMed Central Ltd.

Oral presentation

Our knowledge of the sensory 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. New experimental methods allow us to deliver more nearly natural visual stimuli, comparable to those which flies encounter in free, acrobatic flight, and 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 ~60 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. Under these naturalistic conditions, the system's information transmission rate still increases with higher photon flux, even though individual photoreceptors are counting more than one million photons per second. Further, exploiting the relatively slow dynamics of the stimulus, the system removes redundancy and so generates a more efficient neural code.