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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 420022.0, MPI für biologische Kybernetik / Biologische Kybernetik
Receptive Fields without Spike-Triggering
Authors:Macke, J.H.; Zeck, G.; Bethge, M.
Editors:Platt, J. C.; Koller, D.; Singer, Y.; Roweis, S.
Date of Publication (YYYY-MM-DD):2008-09
Title of Proceedings:Advances in Neural Information Processing Systems 20: Proceedings of the 2007 Conference
Start Page:969
End Page:976
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:Stimulus selectivity of sensory neurons is often characterized by estimating their
receptive field properties such as orientation selectivity. Receptive fields are usually derived from the mean (or covariance) of the spike-triggered stimulus ensemble. This approach treats each spike as an independent message but does not take into account that information might be conveyed through patterns of neural activity that are distributed across space or time. Can we find a concise description for the processing of a whole population of neurons analogous to the receptive field for single neurons? Here, we present a generalization of the linear receptive field which is not bound to be triggered on individual spikes but can be meaningfully
linked to distributed response patterns. More precisely, we seek to identify those
stimulus features and the corresponding patterns of neural activity that are most
reliably coupled. We use an extension of reverse-correlation methods based on
canonical correlation analysis. The resulting population receptive fields span the
subspace of stimuli that is most informative about the population response. We
evaluate our approach using both neuronal models and multi-electrode recordings
from rabbit retinal ganglion cells. We show how the model can be extended to
capture nonlinear stimulus-response relationships using kernel canonical correla-
tion analysis, which makes it possible to test different coding mechanisms. Our
technique can also be used to calculate receptive fields from multi-dimensional
neural measurements such as those obtained from dynamic imaging methods.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI f�r biologische Kybernetik/NWG Bethge
MPI f�r biologische Kybernetik/Empirical Inference (Dept. Sch�lkopf)
Identifiers:LOCALID:4738
URL:http://books.nips.cc/papers/files/nips20/NIPS2007_...
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