A nonconvex variational approach for receptive field estimation

Abstract : Determining the receptive field of a visual sensory neuron is a first but crucial step to- wards the characterization of neurons response to local spatio-temporal stimuli. Existing methods are based on convex optimization methods neglecting biophysical constraints of neurons (bounded firing rate), and they are relatively poor in terms of accuracy and running time. We propose a new method to estimate receptive fields by a nonconvex variational approach, thus relaxing the simplifying and unrealistic assumption of convexity made by standard approaches. The method consists in studying a relaxed discrete energy minimized by a proximal alternating minimization algorithm. We compare our approach with the classical spike-triggered-average technique on sim- ulated data, considering a typical retinal ganglion cell. Results show a high improvement in term of accuracy and convergence with respect to the duration of the experiment.
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Audric Drogoul, Gilles Aubert, Bruno Cessac, Pierre Kornprobst. A nonconvex variational approach for receptive field estimation. [Research Report] 8837, Inria Sophia Antipolis. 2016, pp.42. ⟨hal-01279999v3⟩

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