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Poster

Coding visual information at the level of populations of neurons

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84260

Tolias,  AS
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84972

Siapas AG, Smirnakis,  SM
Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84063

Logothetis,  NK
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Tolias, A., Siapas AG, Smirnakis, S., & Logothetis, N. (2002). Coding visual information at the level of populations of neurons. Poster presented at 32nd Annual Meeting of the Society for Neuroscience (Neuroscience 2002), Orlando, FL, USA.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-DE73-5
Zusammenfassung
Information conveyed through the firing of individual neurons is inherently ambiguous. For instance, different combinations of visual attributes such as orientation, contrast, and motion direction may result in the same rate of firing of a given cell. It is generally assumed that this ambiguity is resolved at the level of populations of neurons; yet the specific coding principles at the network level remain elusive. To examine these principles, we have recorded simultaneously from multiple well-isolated neurons in area V1/V2 of the macaque using a 12 tetrode chronically implanted array. We trained monkeys to report the direction of motion of a random dot display in which the strength of the motion signal was determined by the proportion of coherently moving dots. Since neurons in V1 have relatively small receptive fields, under these motion conditions we find that the mean firing rate of individual neurons does not predict the direction of motion of the stimulus, even when the psychophysical performance of th e animal was optimal. The coding principles underlying this performance are currently analyzed by the explicit characterization of the relationship between activity patterns across multiple neurons and the direction and coherence of the motion stimulus.