---
res:
bibo_abstract:
- Models of neural responses to stimuli with complex spatiotemporal correlation
structure often assume that neurons are selective for only a small number of linear
projections of a potentially high-dimensional input. In this review, we explore
recent modeling approaches where the neural response depends on the quadratic
form of the input rather than on its linear projection, that is, the neuron is
sensitive to the local covariance structure of the signal preceding the spike.
To infer this quadratic dependence in the presence of arbitrary (e.g., naturalistic)
stimulus distribution, we review several inference methods, focusing in particular
on two information theoryâ€“based approaches (maximization of stimulus energy and
of noise entropy) and two likelihood-based approaches (Bayesian spike-triggered
covariance and extensions of generalized linear models). We analyze the formal
relationship between the likelihood-based and information-based approaches to
demonstrate how they lead to consistent inference. We demonstrate the practical
feasibility of these procedures by using model neurons responding to a flickering
variance stimulus.@eng
bibo_authorlist:
- foaf_Person:
foaf_givenName: Kanaka
foaf_name: Rajan, Kanaka
foaf_surname: Rajan
- foaf_Person:
foaf_givenName: Olivier
foaf_name: Marre, Olivier
foaf_surname: Marre
- foaf_Person:
foaf_givenName: Gasper
foaf_name: Tkacik, Gasper
foaf_surname: Tkacik
foaf_workInfoHomepage: http://www.librecat.org/personId=3D494DCA-F248-11E8-B48F-1D18A9856A87
orcid: 0000-0002-6699-1455
bibo_doi: 10.1162/NECO_a_00463
bibo_issue: '7'
bibo_volume: 25
dct_date: 2013^xs_gYear
dct_language: eng
dct_publisher: MIT Press @
dct_title: Learning quadratic receptive fields from neural responses to natural
stimuli@
...