Categorically distinct types of receptive fields in early visual cortex

Abstract

In the visual cortex, distinct types of neurons have been identified based on cellular morphology, response to injected current, or expression of specific markers, but neurophysiological studies have revealed visual receptive field (RF) properties that appear to be on a continuum, with only two generally recognized classes: simple and complex. Most previous studies have characterized visual responses of neurons using stereotyped stimuli such as bars, gratings, or white noise and simple system identification approaches (e.g., reverse correlation). Here we estimate visual RF models of cortical neurons using visually rich natural image stimuli and regularized regression system identification methods and characterize their spatial tuning, temporal dynamics, spatiotemporal behavior, and spiking properties. We quantitatively demonstrate the existence of three functionally distinct categories of simple cells, distinguished by their degree of orientation selectivity (isotropic or oriented) and the nature of their output nonlinearity (expansive or compressive). In addition, these three types have differing average values of several other properties. Cells with nonoriented RFs tend to have smaller RFs, shorter response durations, no direction selectivity, and high reliability. Orientation-selective neurons with an expansive output nonlinearity have Gabor-like RFs, lower spontaneous activity and responsivity, and spiking responses with higher sparseness. Oriented RFs with a compressive nonlinearity are spatially nondescript and tend to show longer response latency. Our findings indicate multiple physiologically defined types of RFs beyond the simple/complex dichotomy, suggesting that cortical neurons may have more specialized functional roles rather than lying on a multidimensional continuum.

Beyond differences in means: robust graphical methods to compare two groups in neuroscience

“…5 show other examples of asymmetric effects in skewed distributions. Both panels show results from recordings from the cat visual cortex from two research groups (Chanauria et al, 2016;Talebi & Baker, 2016). Panel B illustrates the adaptation response (amplitude of shift) of two independent groups of neurones with opposite responses (attractive vs. repulsive adaptation).…”

“…(C) Data from Fig. 9A of Talebi & Baker (2016). Data in panel A were obtained from a table in the original publication.…”

A Guide to Robust Statistical Methods in Neuroscience

“…First focus on latency. Talebi and Baker (2016) used Student's t-test to compare means. Comparing the means for nonOri and ex-pOri, no significant difference is found.…”

A Guide to Robust Statistical Methods in Neuroscience

“…The next illustrations are based on data analyzed by Talebi and Baker (2016) and presented in their Figure 9. The goal was to estimate visual receptive field (RF) models of neurones from a cat's visual cortex using natural image stimuli.…”