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. Subunits estimated from parasol retinal ganglion cells (RGCs) in macaque retina partitioned the receptive field into compact regions, likely representing aggregated bipolar cell inputs. Joint clustering revealed shared subunits in neighboring RGCs, 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.

Each file corresponds to a different figure from the paper. Each file consists of fields which correspond to the corresponding panel. The field consists of response (dimensions: Time x number of cells) and stimulus (Time x pixels), filtered in time with the the temporal filter estimated from STA (as described in the paper). Stimulus pixels form a rectangular grid of size 'stim_dim1 x stim_dim2'. 

The files are loaded using Pickle in python.