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  Sound representation methods for spectro-temporal receptive field estimation

Gill, P., Zhang, J., Woolley, S. M., Fremouw, T., & Theunissen, F. E. (2006). Sound representation methods for spectro-temporal receptive field estimation. Journal of Computational Neuroscience, 21(1), 5-20. doi:10.1007/s10827-006-7059-4.

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 Creators:
Gill, P., Author
Zhang, Junli, Author
Woolley, S. M., Author
Fremouw, T., Author
Theunissen, Frederic E.1, Author           
Affiliations:
1University Berkeley, USA, ou_persistent22              

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Free keywords: Acoustic Stimulation/methods Animals Auditory Perception/*physiology Brain/cytology Finches Male *Models, Neurological Neurons/*physiology Nonlinear Dynamics Predictive Value of Tests Reaction Time/physiology *Sound Sound Spectrography/methods Time Factors Time Perception/*physiology
 Abstract: The spectro-temporal receptive field (STRF) of an auditory neuron describes the linear relationship between the sound stimulus in a time-frequency representation and the neural response. Time-frequency representations of a sound in turn require a nonlinear operation on the sound pressure waveform and many different forms for this non-linear transformation are possible. Here, we systematically investigated the effects of four factors in the non-linear step in the STRF model: the choice of logarithmic or linear filter frequency spacing, the time-frequency scale, stimulus amplitude compression and adaptive gain control. We quantified the goodness of fit of these different STRF models on data obtained from auditory neurons in the songbird midbrain and forebrain. We found that adaptive gain control and the correct stimulus amplitude compression scheme are paramount to correctly modelling neurons. The time-frequency scale and frequency spacing also affected the goodness of fit of the model but to a lesser extent and the optimal values were stimulus dependent.

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Language(s): eng - English
 Dates: 2006
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: Other: 16633939
DOI: 10.1007/s10827-006-7059-4
 Degree: -

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Title: Journal of Computational Neuroscience
Source Genre: Journal
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Publ. Info: Boston : Kluwer Academic Publishers
Pages: - Volume / Issue: 21 (1) Sequence Number: - Start / End Page: 5 - 20 Identifier: ISSN: 0929-5313
CoNE: https://pure.mpg.de/cone/journals/resource/954925568787