In this study we therefore apply both STA and STC analysis to the mouse retina, demonstrating, as in the salamander studies, that STC is able to identify novel characteristics of RFs. As for the retinal system, STC analysis on RFs has been applied in salamander and one monkey study, but not yet, to the best of our knowledge, to the mouse retina. Other groups have studied the theoretical properties of STC, its generalization to different stimulus distributions, and relation to Wiener / Volterra series, and demonstrated its benefits in previous studies on the V1 complex cells in macaques and the H1 neurons in fly visual cortex. STC on the other hand makes use of the second moment to explore directions of differing variance in stimulus space.
Furthermore, STA may not be suitable for identifying certain cell types for instance, ON/OFF cells respond equally to the on- and offset of a light stimulus, resulting in a non-informative STA.Īs a first-order statistic, STA captures only a single feature in stimulus space. However, since the STA is the average of the spike-triggered stimulus, potentially more complex patterns in the RF features may be missed. Several attempts have been made to use STA for investigating various spatiotemporal patterns of RFs and their implication on retinal circuits. The average of these patterns is known as reverse correlation or spike-triggered average (STA). The RF is obtained by averaging the stimulus patterns eliciting the spikes of a cell. The temporal and spatial information of RF are important factors in understanding how visual stimulus is encoded by RGC activities. The receptive field (RF) of a retinal ganglion cell (RGC) acts as the basic element of the visual information processing in the retina. Each type of RGC shows distinct properties regarding its ON/ OFF response, direction selectivity, color vision, contrast adaptation, intrinsic photosensitive response, etc. It is well known that RGCs consist of approximately 20 morphological subtypes and about 40 physiological subtypes. Visual information is encoded by the retina, in which spiking activities of retinal ganglion cells (RGCs) are conveyed to the brain. Thus, combining STA and STC and considering the proposed sub-types unveil novel characteristics of RFs in the mouse retina and offer a more holistic understanding of the neural coding mechanisms of mouse RGCs. The other patterns (T3-T5), which are contrasting, alternating, and monophasic patterns, could be related to inhibitory inputs from amacrine cells. The transient biphasic pattern (T2) allows one to characterize complex patterns in RFs of ON/OFF cells. In particular, the relatively slow biphasic pattern (T1) could be related to excitatory inputs from bipolar cells. We propose five sub-types (T1-T5) in the STC of mouse RGCs together with their physiological implications. We first classified mouse RGCs into ON, OFF, and ON/OFF cells according to their response to full-field light stimulus, and then investigated the spatiotemporal patterns of RFs with random checkerboard stimulation, using both STA and STC analysis.
This study compares STA and STC for the characterization of RFs and demonstrates that STC has an advantage over STA for identifying novel spatiotemporal features of RFs in mouse RGCs. As an alternative, spike-triggered covariance (STC) has been proposed to characterize more complex patterns in RFs. The receptive field (RF) of each RGC is usually calculated by spike-triggered average (STA), which is fast and easy to understand, but limited to simple and unimodal RFs. Retinal ganglion cells (RGCs) encode various spatiotemporal features of visual information into spiking patterns.