Speed-Selectivity in Retinal Ganglion Cells is Sharpened by Broad Spatial Frequency, Naturalistic Stimuli

Abstract

Motion detection represents one of the critical tasks of the visual system and has motivated a large body of research. However, it remains unclear precisely why the response of retinal ganglion cells (RGCs) to simple artificial stimuli does not predict their response to complex, naturalistic stimuli. To explore this topic, we use Motion Clouds (MC), which are synthetic textures that preserve properties of natural images and are merely parameterized, in particular by modulating the spatiotemporal spectrum complexity of the stimulus by adjusting the frequency bandwidths. By stimulating the retina of the diurnal rodent, Octodon degus with MC we show that the RGCs respond to increasingly complex stimuli by narrowing their adjustment curves in response to movement. At the level of the population, complex stimuli produce a sparser code while preserving movement information; therefore, the stimuli are encoded more efficiently. Interestingly, these properties were observed throughout different populations of RGCs. Thus, our results reveal that the response at the level of RGCs is modulated by the naturalness of the stimulus - in particular for motion - which suggests that the tuning to the statistics of natural images already emerges at the level of the retina.

Predictive visual motion extrapolation emerges spontaneously and without supervision at each layer of a hierarchical neural network with spike-timing-dependent plasticity

“…These neurons are maximally activated by motion in their preferred direction and strongly suppressed by motion in the opposite direction. Within the retina there are a number of mechanisms involving multilayered retinal circuits that provide reliable motion detection (Frechette et al, 2005; Manookin et al, 2018), consistent with the broad experimental evidence for velocity tuning neurons in the retina of mammals and vertebrates (Olveczky et al, 2007; Vaney et al, 2012; Ravello et al, 2019; for a review see Wei, 2018). This motion selective information is transmitted via the LGN to the primary visual cortex, V1, where direction selective neurons are concentrated in layer 4B of V1 and project from there to higher motion processing areas of the visual hierarchy pathway, particularly area MT (Maunsell & van Essen, 1983).…”

“…This population code includes sub-populations of neurons tuned to both position and velocity, as has previously been proposed by Khoei et al (2017) and consistent with the known velocity-tuning of a large proportion of visual neurons in the early visual system (Orban et al, 1986). This sub-population coding of the velocity of the stimulus at each layer is inherited from the lower layers, beginning in the retina (Berry et al, 1999; Ravello et al, 2019) and LGN (Sillito et al, 1994), and then passed on to the primary visual cortex (Janke et al, 2004; Benvenuti et al, 2020), and it is further enhanced in the motion-processing centers of areas MT and MST (Maunsell & Van Essen, 1983; Koch et al, 1989; Perrone & Thiele, 2001; Inaba et al, 2011).…”

From the Retina to Action: Dynamics of Predictive Processing in the Visual System

“…For instance, [54] has shown that neurons in cat's area 17 respond more selectively when presenting natural images (which consist locally to a sum of edges) compared to a single edge. Recently, [55] has shown that a similar result may occur in rodents as soon as in the retina. Behaviorally, this ts also with the observation in humans that more complex textures are driving more robustly eye movements [55].…”

“…Recently, [55] has shown that a similar result may occur in rodents as soon as in the retina. Behaviorally, this ts also with the observation in humans that more complex textures are driving more robustly eye movements [55]. Such phenomena are consistent with the predictive processing principle that by accumulating coherent information, the a posteriori probability (and hence the response of the system) gets more precise.…”

Parallel processing of natural images by overlapping retinal neuronal ensembles

“…The experimental protocol for MEA recording we used here has been described before (Palacios-Munoz et al, 2014;Ravello et al, 2019). In brief, stimuli were generated using an appropriated code, where the timing of the images was controlled using a custom-built software based on Psychtoolbox for MATLAB.…”