Inference of nonlinear receptive field subunits with spike-triggered clustering.

Abstract:

:Responses of sensory neurons are often modeled using a weighted combination of rectified linear subunits. Since these subunits often cannot be measured directly, a flexible method is needed to infer their properties from the responses of downstream neurons. We present a method for maximum likelihood estimation of subunits by soft-clustering spike-triggered stimuli, and demonstrate its effectiveness in visual neurons. For parasol retinal ganglion cells in macaque retina, estimated subunits partitioned the receptive field into compact regions, likely representing aggregated bipolar cell inputs. Joint clustering revealed shared subunits between neighboring cells, producing a parsimonious population model. Closed-loop validation, using stimuli lying in the null space of the linear receptive field, revealed stronger nonlinearities in OFF cells than ON cells. Responses to natural images, jittered to emulate fixational eye movements, were accurately predicted by the subunit model. Finally, the generality of the approach was demonstrated in macaque V1 neurons.

journal_name

Elife

journal_title

eLife

authors

Shah NP,Brackbill N,Rhoades C,Kling A,Goetz G,Litke AM,Sher A,Simoncelli EP,Chichilnisky EJ

doi

10.7554/eLife.45743

subject

Has Abstract

pub_date

2020-03-09 00:00:00

issn

2050-084X

pii

45743

journal_volume

9

pub_type

杂志文章

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