Literature DB >> 29331232

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

Stephen V David1.   

Abstract

For several decades, auditory neuroscientists have used spectro-temporal encoding models to understand how neurons in the auditory system represent sound. Derived from early applications of systems identification tools to the auditory periphery, the spectro-temporal receptive field (STRF) and more sophisticated variants have emerged as an efficient means of characterizing representation throughout the auditory system. Most of these encoding models describe neurons as static sensory filters. However, auditory neural coding is not static. Sensory context, reflecting the acoustic environment, and behavioral context, reflecting the internal state of the listener, can both influence sound-evoked activity, particularly in central auditory areas. This review explores recent efforts to integrate context into spectro-temporal encoding models. It begins with a brief tutorial on the basics of estimating and interpreting STRFs. Then it describes three recent studies that have characterized contextual effects on STRFs, emerging over a range of timescales, from many minutes to tens of milliseconds. An important theme of this work is not simply that context influences auditory coding, but also that contextual effects span a large continuum of internal states. The added complexity of these context-dependent models introduces new experimental and theoretical challenges that must be addressed in order to be used effectively. Several new methodological advances promise to address these limitations and allow the development of more comprehensive context-dependent models in the future.
Copyright © 2017 The Author. Published by Elsevier B.V. All rights reserved.

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Year:  2017        PMID: 29331232      PMCID: PMC6292525          DOI: 10.1016/j.heares.2017.12.021

Source DB:  PubMed          Journal:  Hear Res        ISSN: 0378-5955            Impact factor:   3.208


  141 in total

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

2.  Spectrotemporal analysis of evoked and induced electroencephalographic responses in primary auditory cortex (A1) of the awake monkey.

Authors:  Mitchell Steinschneider; Yonatan I Fishman; Joseph C Arezzo
Journal:  Cereb Cortex       Date:  2007-06-22       Impact factor: 5.357

3.  Effects of behavioral performance on single-unit firing patterns in inferior colliculus of the rhesus monkey.

Authors:  A Ryan; J Miller
Journal:  J Neurophysiol       Date:  1977-07       Impact factor: 2.714

4.  The spectro-temporal receptive field. A functional characteristic of auditory neurons.

Authors:  A M Aertsen; P I Johannesma
Journal:  Biol Cybern       Date:  1981       Impact factor: 2.086

5.  Differential dynamic plasticity of A1 receptive fields during multiple spectral tasks.

Authors:  Jonathan B Fritz; Mounya Elhilali; Shihab A Shamma
Journal:  J Neurosci       Date:  2005-08-17       Impact factor: 6.167

6.  Tuning in to sound: frequency-selective attentional filter in human primary auditory cortex.

Authors:  Sandra Da Costa; Wietske van der Zwaag; Lee M Miller; Stephanie Clarke; Melissa Saenz
Journal:  J Neurosci       Date:  2013-01-30       Impact factor: 6.167

7.  Global brain dynamics embed the motor command sequence of Caenorhabditis elegans.

Authors:  Saul Kato; Harris S Kaplan; Tina Schrödel; Susanne Skora; Theodore H Lindsay; Eviatar Yemini; Shawn Lockery; Manuel Zimmer
Journal:  Cell       Date:  2015-10-17       Impact factor: 41.582

8.  Comparison of LFP-based and spike-based spectro-temporal receptive fields and cross-correlation in cat primary auditory cortex.

Authors:  Jos J Eggermont; Raymundo Munguia; Martin Pienkowski; Greg Shaw
Journal:  PLoS One       Date:  2011-05-23       Impact factor: 3.240

9.  Reconstructing speech from human auditory cortex.

Authors:  Brian N Pasley; Stephen V David; Nima Mesgarani; Adeen Flinker; Shihab A Shamma; Nathan E Crone; Robert T Knight; Edward F Chang
Journal:  PLoS Biol       Date:  2012-01-31       Impact factor: 8.029

10.  Engaging in an auditory task suppresses responses in auditory cortex.

Authors:  Gonzalo H Otazu; Lung-Hao Tai; Yang Yang; Anthony M Zador
Journal:  Nat Neurosci       Date:  2009-04-12       Impact factor: 24.884

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  8 in total

1.  Quantifying Neuronal Information Flow in Response to Frequency and Intensity Changes in the Auditory Cortex.

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2.  Cortical adaptation to sound reverberation.

Authors:  Ben D B Willmore; Kerry M M Walker; Nicol S Harper; Aleksandar Z Ivanov; Andrew J King
Journal:  Elife       Date:  2022-05-26       Impact factor: 8.713

3.  An Emergent Population Code in Primary Auditory Cortex Supports Selective Attention to Spectral and Temporal Sound Features.

Authors:  Joshua D Downer; Jessica R Verhein; Brittany C Rapone; Kevin N O'Connor; Mitchell L Sutter
Journal:  J Neurosci       Date:  2021-07-01       Impact factor: 6.709

4.  Multiple sounds degrade the frequency representation in monkey inferior colliculus.

Authors:  Shawn M Willett; Jennifer M Groh
Journal:  Eur J Neurosci       Date:  2021-12-30       Impact factor: 3.698

Review 5.  A Survey on Probabilistic Models in Human Perception and Machines.

Authors:  Lux Li; Robert Rehr; Patrick Bruns; Timo Gerkmann; Brigitte Röder
Journal:  Front Robot AI       Date:  2020-07-07

Review 6.  Neural Substrates and Models of Omission Responses and Predictive Processes.

Authors:  Alessandro Braga; Marc Schönwiesner
Journal:  Front Neural Circuits       Date:  2022-02-01       Impact factor: 3.492

Review 7.  Recent advances in understanding the auditory cortex.

Authors:  Andrew J King; Sundeep Teki; Ben D B Willmore
Journal:  F1000Res       Date:  2018-09-26

8.  Spectral tuning of adaptation supports coding of sensory context in auditory cortex.

Authors:  Mateo Lopez Espejo; Zachary P Schwartz; Stephen V David
Journal:  PLoS Comput Biol       Date:  2019-10-18       Impact factor: 4.475

  8 in total

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