Learning quadratic receptive fields from neural responses to natural stimuli

K. Rajan, O. Marre, G. Tkacik, Neural Computation 25 (2013) 1661–1692.


Journal Article | Published | English
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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 theory–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.
Publishing Year
Date Published
2013-07-01
Journal Title
Neural Computation
Volume
25
Issue
7
Page
1661 - 1692
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Rajan K, Marre O, Tkacik G. Learning quadratic receptive fields from neural responses to natural stimuli. Neural Computation. 2013;25(7):1661-1692. doi:10.1162/NECO_a_00463
Rajan, K., Marre, O., & Tkacik, G. (2013). Learning quadratic receptive fields from neural responses to natural stimuli. Neural Computation, 25(7), 1661–1692. https://doi.org/10.1162/NECO_a_00463
Rajan, Kanaka, Olivier Marre, and Gasper Tkacik. “Learning Quadratic Receptive Fields from Neural Responses to Natural Stimuli.” Neural Computation 25, no. 7 (2013): 1661–92. https://doi.org/10.1162/NECO_a_00463.
K. Rajan, O. Marre, and G. Tkacik, “Learning quadratic receptive fields from neural responses to natural stimuli,” Neural Computation, vol. 25, no. 7, pp. 1661–1692, 2013.
Rajan K, Marre O, Tkacik G. 2013. Learning quadratic receptive fields from neural responses to natural stimuli. Neural Computation. 25(7), 1661–1692.
Rajan, Kanaka, et al. “Learning Quadratic Receptive Fields from Neural Responses to Natural Stimuli.” Neural Computation, vol. 25, no. 7, MIT Press , 2013, pp. 1661–92, doi:10.1162/NECO_a_00463.

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