Literature DB >> 21849547

Extra-classical tuning predicts stimulus-dependent receptive fields in auditory neurons.

David M Schneider1, Sarah M N Woolley.   

Abstract

The receptive fields of many sensory neurons are sensitive to statistical differences among classes of complex stimuli. For example, excitatory spectral bandwidths of midbrain auditory neurons and the spatial extent of cortical visual neurons differ during the processing of natural stimuli compared to the processing of artificial stimuli. Experimentally characterizing neuronal nonlinearities that contribute to stimulus-dependent receptive fields is important for understanding how neurons respond to different stimulus classes in multiple sensory modalities. Here we show that in the zebra finch, many auditory midbrain neurons have extra-classical receptive fields, consisting of sideband excitation and sideband inhibition. We also show that the presence, degree, and asymmetry of stimulus-dependent receptive fields during the processing of complex sounds are predicted by the presence, valence, and asymmetry of extra-classical tuning. Neurons for which excitatory bandwidth expands during the processing of song have extra-classical excitation. Neurons for which frequency tuning is static and for which excitatory bandwidth contracts during the processing of song have extra-classical inhibition. Simulation experiments further demonstrate that stimulus-dependent receptive fields can arise from extra-classical tuning with a static spike threshold nonlinearity. These findings demonstrate that a common neuronal nonlinearity can account for the stimulus dependence of receptive fields estimated from the responses of auditory neurons to stimuli with natural and non-natural statistics.

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Year:  2011        PMID: 21849547      PMCID: PMC3164972          DOI: 10.1523/JNEUROSCI.5790-10.2011

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  64 in total

1.  Different subthreshold mechanisms underlie song selectivity in identified HVc neurons of the zebra finch.

Authors:  R Mooney
Journal:  J Neurosci       Date:  2000-07-15       Impact factor: 6.167

2.  Changes of AI receptive fields with sound density.

Authors:  David T Blake; Michael M Merzenich
Journal:  J Neurophysiol       Date:  2002-12       Impact factor: 2.714

3.  Neural adjustments to image blur.

Authors:  Michael A Webster; Mark A Georgeson; Shernaaz M Webster
Journal:  Nat Neurosci       Date:  2002-09       Impact factor: 24.884

4.  Selectivity for conspecific song in the zebra finch auditory forebrain.

Authors:  Julie A Grace; Noopur Amin; Nandini C Singh; Frédéric E Theunissen
Journal:  J Neurophysiol       Date:  2003-01       Impact factor: 2.714

5.  Tuning for spectro-temporal modulations as a mechanism for auditory discrimination of natural sounds.

Authors:  Sarah M N Woolley; Thane E Fremouw; Anne Hsu; Frédéric E Theunissen
Journal:  Nat Neurosci       Date:  2005-09-04       Impact factor: 24.884

6.  Neural mechanisms underlying selectivity for the rate and direction of frequency-modulated sweeps in the inferior colliculus of the pallid bat.

Authors:  Zoltan M Fuzessery; Marlin D Richardson; Michael S Coburn
Journal:  J Neurophysiol       Date:  2006-06-21       Impact factor: 2.714

7.  Spectrotemporal receptive fields in the inferior colliculus revealing selectivity for spectral motion in conspecific vocalizations.

Authors:  Sari Andoni; Na Li; George D Pollak
Journal:  J Neurosci       Date:  2007-05-02       Impact factor: 6.167

8.  The consequences of response nonlinearities for interpretation of spectrotemporal receptive fields.

Authors:  G Björn Christianson; Maneesh Sahani; Jennifer F Linden
Journal:  J Neurosci       Date:  2008-01-09       Impact factor: 6.167

9.  A synaptic basis for auditory-vocal integration in the songbird.

Authors:  Eric E Bauer; Melissa J Coleman; Todd F Roberts; Arani Roy; Jonathan F Prather; Richard Mooney
Journal:  J Neurosci       Date:  2008-02-06       Impact factor: 6.167

10.  Response properties of the auditory telencephalon in songbirds change with recent experience and season.

Authors:  Thomas A Terleph; Kai Lu; David S Vicario
Journal:  PLoS One       Date:  2008-08-06       Impact factor: 3.240

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  21 in total

1.  Precise feature based time scales and frequency decorrelation lead to a sparse auditory code.

Authors:  Chen Chen; Heather L Read; Monty A Escabí
Journal:  J Neurosci       Date:  2012-06-20       Impact factor: 6.167

2.  Habitat-related differences in auditory processing of complex tones and vocal signal properties in four songbirds.

Authors:  Jeffrey R Lucas; Alejandro Vélez; Kenneth S Henry
Journal:  J Comp Physiol A Neuroethol Sens Neural Behav Physiol       Date:  2015-02-15       Impact factor: 1.836

3.  Coding principles of the canonical cortical microcircuit in the avian brain.

Authors:  Ana Calabrese; Sarah M N Woolley
Journal:  Proc Natl Acad Sci U S A       Date:  2015-02-17       Impact factor: 11.205

4.  Online stimulus optimization rapidly reveals multidimensional selectivity in auditory cortical neurons.

Authors:  Anna R Chambers; Kenneth E Hancock; Kamal Sen; Daniel B Polley
Journal:  J Neurosci       Date:  2014-07-02       Impact factor: 6.167

Review 5.  Neural processing of natural sounds.

Authors:  Frédéric E Theunissen; Julie E Elie
Journal:  Nat Rev Neurosci       Date:  2014-06       Impact factor: 34.870

6.  Development of echolocation calls and neural selectivity for echolocation calls in the pallid bat.

Authors:  Khaleel A Razak; Zoltan M Fuzessery
Journal:  Dev Neurobiol       Date:  2014-08-28       Impact factor: 3.964

7.  Spectrotemporal contrast kernels for neurons in primary auditory cortex.

Authors:  Neil C Rabinowitz; Ben D B Willmore; Jan W H Schnupp; Andrew J King
Journal:  J Neurosci       Date:  2012-08-15       Impact factor: 6.167

Review 8.  Early experience shapes vocal neural coding and perception in songbirds.

Authors:  Sarah M N Woolley
Journal:  Dev Psychobiol       Date:  2012-06-18       Impact factor: 3.038

9.  Midbrain auditory selectivity to natural sounds.

Authors:  Melville J Wohlgemuth; Cynthia F Moss
Journal:  Proc Natl Acad Sci U S A       Date:  2016-02-16       Impact factor: 11.205

Review 10.  Incorporating behavioral and sensory context into spectro-temporal models of auditory encoding.

Authors:  Stephen V David
Journal:  Hear Res       Date:  2017-12-31       Impact factor: 3.208

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