Inference of nonlinear receptive field subunits with spike-triggered clustering

Nishal P. Shah, Nora Brackbill, Colleen Rhoades, Alexandra Kling, Georges Goetz, Alan M. Litke, Alexander Sher, Eero Simoncelli, E. J. Chichilnisky

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article numbere45743
JournaleLife
Volume9
DOIs
StatePublished - Mar 2020

ASJC Scopus subject areas

  • Neuroscience(all)
  • Immunology and Microbiology(all)
  • Biochemistry, Genetics and Molecular Biology(all)

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