Neural encoding of global statistical features of natural sounds.
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1
Rockefeller University, United States
We investigate the statistical structure of water sounds, and show that water sound can be generated as a fractal controlled by two global parameters. Psychophysical studies show that human perception of this signal as a natural object differs as the parameters are changed, suggesting that these parameters are sufficient to represent the family of water sounds. We propose that the early auditory system may extract and encode the statistics of these stimuli by performing a scale-invariant analysis of the sound signal. We next use these sounds in electrophysiological experiments to map the neural circuit that might encode the global statistical parameters of these signals. Neural responses in the primary auditory cortex of awake rats (A1) correlated with the changes in the global parameters of the synthetic sounds, both at the level of the firing rate, and the specific timing and timing precision of individual firing events. Changes in global parameters of the stimuli further resulted in temporal changes in the receptive field of the A1 neurons. The temporal receptive field was slower for stimuli with longer auto-correlation structure, and faster for stimuli with faster auto-correlation structure. Adding a time-dependent non-linear feedback term improved the fit of the linear-non-linear model. We propose the hypothesis that encoding of natural environmental sounds requires comparison of global statistics of these sounds at a range of timescales.
Conference:
Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010.
Presentation Type:
Poster Presentation
Topic:
Poster session I
Citation:
Geffen
MN,
Taillefumier
T and
Magnasco
M
(2010). Neural encoding of global statistical features of natural sounds..
Front. Neurosci.
Conference Abstract:
Computational and Systems Neuroscience 2010.
doi: 10.3389/conf.fnins.2010.03.00131
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Received:
01 Mar 2010;
Published Online:
01 Mar 2010.
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Correspondence:
Maria N Geffen, Rockefeller University, New York, United States, mgeffen@rockefeller.edu