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

Authors
  • Shah, Nishal P1
  • Brackbill, Nora2
  • Rhoades, Colleen3
  • Kling, Alexandra4, 5, 6
  • Goetz, Georges4, 5, 6
  • Litke, Alan M7
  • Sher, Alexander8
  • Simoncelli, Eero9, 10
  • Chichilnisky, E J4, 5, 6
  • 1 Department of Electrical Engineering, Stanford University, Stanford, United States. , (United States)
  • 2 Department of Physics, Stanford University, Stanford, United States. , (United States)
  • 3 Department of Bioengineering, Stanford University, Stanford, United States. , (United States)
  • 4 Department of Neurosurgery, Stanford School of Medicine, Stanford, United States. , (United States)
  • 5 Department of Ophthalmology, Stanford University, Stanford, United States. , (United States)
  • 6 Hansen Experimental Physics Laboratory, Stanford University, Stanford, United States. , (United States)
  • 7 Institute for Particle Physics, University of California, Santa Cruz, Santa Cruz, United States. , (United States)
  • 8 Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, Santa Cruz, United States. , (United States)
  • 9 Center for Neural Science, New York University, New York, United States. , (United States)
  • 10 Howard Hughes Medical Institute, Chevy Chase, United States. , (United States)
Type
Published Article
Journal
eLife
Publisher
"eLife Sciences Organisation, Ltd."
Publication Date
Mar 09, 2020
Volume
9
Identifiers
DOI: 10.7554/eLife.45743
PMID: 32149600
Source
Medline
Keywords
Language
English
License
Unknown

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. © 2020, Shah et al.

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