Differences in nonlinearities determine retinal cell types

Abstract

Classifying neurons in different types is still an open challenge. In the retina, recent works have taken advantage of the ability to record a large number of cells to classify ganglion cells into different types based on functional information. While the first attempts in this direction used the receptive field properties of each cell to classify them, more recent approaches have proposed to cluster ganglion cells directly based on their response to standard stimuli. These two approaches have not been compared directly. Here we recorded the responses of a large number of ganglion cells and compared two methods for classifying them into types, one based on the receptive field properties, and the other one using their responses to standard stimuli. We show that the stimulus-based approach allows separating more types than the receptive field-based method, leading to a better classification. This better granularity is due to the fact that the stimulus-based method takes into account not only the linear part of ganglion cell function, but also non-linearities. A careful characterization of non-linear processing is thus key to allow functional classification of sensory neurons.

A chromatic feature detector in the retina signals visual context changes

“…For simplicity, we refer to such a time-lapsed scan of a local population of GCL cells as a “recording”. Despite the low frame rate, the Ca 2+ responses can be related to the spike rate (77–80). For documenting the position of the recording fields, the retina under the microscope was oriented such that the most ventral edge pointed always towards the experimenter.…”