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  Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli

Theunissen, F. E., David, S. V., Singh, N. C., Hsu, A., Vinje, W. E., & Gallant, J. L. (2001). Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli. Network: Computation in Neural Systems, 12(3), 289-316. doi:10.1080/net.12.3.289.316.

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Genre: Journal Article

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 Creators:
Theunissen, Frederic E.1, Author              
David, S. V., Author
Singh, N. C., Author
Hsu, A., Author
Vinje, W. E., Author
Gallant, J. L., Author
Affiliations:
1University Berkeley, USA, ou_persistent22              

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Free keywords: Acoustic Stimulation Algorithms Animals Auditory Cortex/cytology/physiology Auditory Perception/*physiology Models, Neurological Neurons, Afferent/*physiology Photic Stimulation Primates/physiology Prosencephalon/physiology Songbirds Space Perception/physiology Visual Cortex/cytology/physiology Visual Perception/*physiology Vocalization, Animal
 Abstract: We present a generalized reverse correlation technique that can be used to estimate the spatio-temporal receptive fields (STRFs) of sensory neurons from their responses to arbitrary stimuli such as auditory vocalizations or natural visual scenes. The general solution for STRF estimation requires normalization of the stimulus-response cross-correlation by the stimulus autocorrelation matrix. When the second-order stimulus statistics are stationary, normalization involves only the diagonal elements of the Fourier-transformed auto-correlation matrix (the power spectrum). In the non-stationary case normalization requires the entire auto-correlation matrix. We present modelling studies that demonstrate the feasibility and accuracy of this method as well as neurophysiological data comparing STRFs estimated using natural versus synthetic stimulus ensembles. For both auditory and visual neurons, STRFs obtained with these different stimuli are similar, but exhibit systematic differences that may be functionally significant. This method should be useful for determining what aspects of natural signals are represented by sensory neurons and may reveal novel response properties of these neurons.

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Language(s): eng - English
 Dates: 2001
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: Other: 11563531
DOI: 10.1080/net.12.3.289.316
 Degree: -

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Title: Network: Computation in Neural Systems
  Other : Netw.-Comput. Neural Syst.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Bristol : IOP Pub.
Pages: - Volume / Issue: 12 (3) Sequence Number: - Start / End Page: 289 - 316 Identifier: ISSN: 0954-898X
CoNE: https://pure.mpg.de/cone/journals/resource/954925576018