Department of Physics, Stanford University, Stanford, United States
Colleen Rhoades
Department of Bioengineering, Stanford University, Stanford, United States
Alexandra Kling
Department of Neurosurgery, Stanford School of Medicine, Stanford, United States; Department of Ophthalmology, Stanford University, Stanford, United States; Hansen Experimental Physics Laboratory, Stanford University, Stanford, United States
Georges Goetz
Department of Neurosurgery, Stanford School of Medicine, Stanford, United States; Department of Ophthalmology, Stanford University, Stanford, United States; Hansen Experimental Physics Laboratory, Stanford University, Stanford, United States
Department of Neurosurgery, Stanford School of Medicine, Stanford, United States; Department of Ophthalmology, Stanford University, Stanford, United States; Hansen Experimental Physics Laboratory, Stanford University, Stanford, United States
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.
