The Essential Complexity of Auditory Receptive Fields

Ivar L. Thorson, Jean Liénard, Stephen David

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

Encoding properties of sensory neurons are commonly modeled using linear finite impulse response (FIR) filters. For the auditory system, the FIR filter is instantiated in the spectro-temporal receptive field (STRF), often in the framework of the generalized linear model. Despite widespread use of the FIR STRF, numerous formulations for linear filters are possible that require many fewer parameters, potentially permitting more efficient and accurate model estimates. To explore these alternative STRF architectures, we recorded single-unit neural activity from auditory cortex of awake ferrets during presentation of natural sound stimuli. We compared performance of > 1000 linear STRF architectures, evaluating their ability to predict neural responses to a novel natural stimulus. Many were able to outperform the FIR filter. Two basic constraints on the architecture lead to the improved performance: (1) factorization of the STRF matrix into a small number of spectral and temporal filters and (2) low-dimensional parameterization of the factorized filters. The best parameterized model was able to outperform the full FIR filter in both primary and secondary auditory cortex, despite requiring fewer than 30 parameters, about 10% of the number required by the FIR filter. After accounting for noise from finite data sampling, these STRFs were able to explain an average of 40% of A1 response variance. The simpler models permitted more straightforward interpretation of sensory tuning properties. They also showed greater benefit from incorporating nonlinear terms, such as short term plasticity, that provide theoretical advances over the linear model. Architectures that minimize parameter count while maintaining maximum predictive power provide insight into the essential degrees of freedom governing auditory cortical function. They also maximize statistical power available for characterizing additional nonlinear properties that limit current auditory models.

Original languageEnglish (US)
Article numbere1004628
JournalPLoS Computational Biology
Volume11
Issue number12
DOIs
StatePublished - 2015

Fingerprint

Receptive Field
FIR filters
Impulse Response
Filter
filter
cortex
Auditory Cortex
linear models
Cortex
sensory neurons
Linear Models
ferrets
sensory properties
Ferrets
Sensory Receptor Cells
Linear Filter
Statistical Power
Parameterization
Impulse response
Factorization

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Modeling and Simulation
  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Molecular Biology
  • Ecology
  • Cellular and Molecular Neuroscience

Cite this

The Essential Complexity of Auditory Receptive Fields. / Thorson, Ivar L.; Liénard, Jean; David, Stephen.

In: PLoS Computational Biology, Vol. 11, No. 12, e1004628, 2015.

Research output: Contribution to journalArticle

Thorson, Ivar L. ; Liénard, Jean ; David, Stephen. / The Essential Complexity of Auditory Receptive Fields. In: PLoS Computational Biology. 2015 ; Vol. 11, No. 12.
@article{a4634994914640beaf8918e12d309dbd,
title = "The Essential Complexity of Auditory Receptive Fields",
abstract = "Encoding properties of sensory neurons are commonly modeled using linear finite impulse response (FIR) filters. For the auditory system, the FIR filter is instantiated in the spectro-temporal receptive field (STRF), often in the framework of the generalized linear model. Despite widespread use of the FIR STRF, numerous formulations for linear filters are possible that require many fewer parameters, potentially permitting more efficient and accurate model estimates. To explore these alternative STRF architectures, we recorded single-unit neural activity from auditory cortex of awake ferrets during presentation of natural sound stimuli. We compared performance of > 1000 linear STRF architectures, evaluating their ability to predict neural responses to a novel natural stimulus. Many were able to outperform the FIR filter. Two basic constraints on the architecture lead to the improved performance: (1) factorization of the STRF matrix into a small number of spectral and temporal filters and (2) low-dimensional parameterization of the factorized filters. The best parameterized model was able to outperform the full FIR filter in both primary and secondary auditory cortex, despite requiring fewer than 30 parameters, about 10{\%} of the number required by the FIR filter. After accounting for noise from finite data sampling, these STRFs were able to explain an average of 40{\%} of A1 response variance. The simpler models permitted more straightforward interpretation of sensory tuning properties. They also showed greater benefit from incorporating nonlinear terms, such as short term plasticity, that provide theoretical advances over the linear model. Architectures that minimize parameter count while maintaining maximum predictive power provide insight into the essential degrees of freedom governing auditory cortical function. They also maximize statistical power available for characterizing additional nonlinear properties that limit current auditory models.",
author = "Thorson, {Ivar L.} and Jean Li{\'e}nard and Stephen David",
year = "2015",
doi = "10.1371/journal.pcbi.1004628",
language = "English (US)",
volume = "11",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "12",

}

TY - JOUR

T1 - The Essential Complexity of Auditory Receptive Fields

AU - Thorson, Ivar L.

AU - Liénard, Jean

AU - David, Stephen

PY - 2015

Y1 - 2015

N2 - Encoding properties of sensory neurons are commonly modeled using linear finite impulse response (FIR) filters. For the auditory system, the FIR filter is instantiated in the spectro-temporal receptive field (STRF), often in the framework of the generalized linear model. Despite widespread use of the FIR STRF, numerous formulations for linear filters are possible that require many fewer parameters, potentially permitting more efficient and accurate model estimates. To explore these alternative STRF architectures, we recorded single-unit neural activity from auditory cortex of awake ferrets during presentation of natural sound stimuli. We compared performance of > 1000 linear STRF architectures, evaluating their ability to predict neural responses to a novel natural stimulus. Many were able to outperform the FIR filter. Two basic constraints on the architecture lead to the improved performance: (1) factorization of the STRF matrix into a small number of spectral and temporal filters and (2) low-dimensional parameterization of the factorized filters. The best parameterized model was able to outperform the full FIR filter in both primary and secondary auditory cortex, despite requiring fewer than 30 parameters, about 10% of the number required by the FIR filter. After accounting for noise from finite data sampling, these STRFs were able to explain an average of 40% of A1 response variance. The simpler models permitted more straightforward interpretation of sensory tuning properties. They also showed greater benefit from incorporating nonlinear terms, such as short term plasticity, that provide theoretical advances over the linear model. Architectures that minimize parameter count while maintaining maximum predictive power provide insight into the essential degrees of freedom governing auditory cortical function. They also maximize statistical power available for characterizing additional nonlinear properties that limit current auditory models.

AB - Encoding properties of sensory neurons are commonly modeled using linear finite impulse response (FIR) filters. For the auditory system, the FIR filter is instantiated in the spectro-temporal receptive field (STRF), often in the framework of the generalized linear model. Despite widespread use of the FIR STRF, numerous formulations for linear filters are possible that require many fewer parameters, potentially permitting more efficient and accurate model estimates. To explore these alternative STRF architectures, we recorded single-unit neural activity from auditory cortex of awake ferrets during presentation of natural sound stimuli. We compared performance of > 1000 linear STRF architectures, evaluating their ability to predict neural responses to a novel natural stimulus. Many were able to outperform the FIR filter. Two basic constraints on the architecture lead to the improved performance: (1) factorization of the STRF matrix into a small number of spectral and temporal filters and (2) low-dimensional parameterization of the factorized filters. The best parameterized model was able to outperform the full FIR filter in both primary and secondary auditory cortex, despite requiring fewer than 30 parameters, about 10% of the number required by the FIR filter. After accounting for noise from finite data sampling, these STRFs were able to explain an average of 40% of A1 response variance. The simpler models permitted more straightforward interpretation of sensory tuning properties. They also showed greater benefit from incorporating nonlinear terms, such as short term plasticity, that provide theoretical advances over the linear model. Architectures that minimize parameter count while maintaining maximum predictive power provide insight into the essential degrees of freedom governing auditory cortical function. They also maximize statistical power available for characterizing additional nonlinear properties that limit current auditory models.

UR - http://www.scopus.com/inward/record.url?scp=84953302346&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84953302346&partnerID=8YFLogxK

U2 - 10.1371/journal.pcbi.1004628

DO - 10.1371/journal.pcbi.1004628

M3 - Article

C2 - 26683490

AN - SCOPUS:84953302346

VL - 11

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 12

M1 - e1004628

ER -