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

Frédéric E. Theunissen, Stephen David, Nandini C. Singh, Anne Hsu, William E. Vinje, Jack L. Gallant

Research output: Contribution to journalArticle

236 Citations (Scopus)

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 auto-correlation 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.

Original languageEnglish (US)
Pages (from-to)289-316
Number of pages28
JournalNetwork: Computation in Neural Systems
Volume12
Issue number3
DOIs
StatePublished - Aug 2001
Externally publishedYes

Fingerprint

Visual Fields
Neurons
Sensory Receptor Cells
Feasibility Studies

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli. / Theunissen, Frédéric E.; David, Stephen; Singh, Nandini C.; Hsu, Anne; Vinje, William E.; Gallant, Jack L.

In: Network: Computation in Neural Systems, Vol. 12, No. 3, 08.2001, p. 289-316.

Research output: Contribution to journalArticle

Theunissen, Frédéric E. ; David, Stephen ; Singh, Nandini C. ; Hsu, Anne ; Vinje, William E. ; Gallant, Jack L. / Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli. In: Network: Computation in Neural Systems. 2001 ; Vol. 12, No. 3. pp. 289-316.
@article{0b64c900c7bf49a9aba03525f4fe61bf,
title = "Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli",
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 auto-correlation 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.",
author = "Theunissen, {Fr{\'e}d{\'e}ric E.} and Stephen David and Singh, {Nandini C.} and Anne Hsu and Vinje, {William E.} and Gallant, {Jack L.}",
year = "2001",
month = "8",
doi = "10.1088/0954-898X/12/3/304",
language = "English (US)",
volume = "12",
pages = "289--316",
journal = "Network: Computation in Neural Systems",
issn = "0954-898X",
publisher = "Informa Healthcare",
number = "3",

}

TY - JOUR

T1 - Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli

AU - Theunissen, Frédéric E.

AU - David, Stephen

AU - Singh, Nandini C.

AU - Hsu, Anne

AU - Vinje, William E.

AU - Gallant, Jack L.

PY - 2001/8

Y1 - 2001/8

N2 - 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 auto-correlation 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.

AB - 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 auto-correlation 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.

UR - http://www.scopus.com/inward/record.url?scp=0042235146&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0042235146&partnerID=8YFLogxK

U2 - 10.1088/0954-898X/12/3/304

DO - 10.1088/0954-898X/12/3/304

M3 - Article

C2 - 11563531

AN - SCOPUS:0042235146

VL - 12

SP - 289

EP - 316

JO - Network: Computation in Neural Systems

JF - Network: Computation in Neural Systems

SN - 0954-898X

IS - 3

ER -