Retinal Encoding of Natural Scenes

A chromatic feature detector in the retina signals visual context changes

The retina transforms patterns of light into visual feature representations supporting behaviour. These representations are distributed across various types of retinal ganglion cells (RGCs), whose spatial and temporal tuning properties have been studied extensively in many model organisms, including the mouse. However, it has been difficult to link the potentially nonlinear retinal transformations of natural visual inputs to specific ethological purposes. Here, we discover a nonlinear selectivity to chromatic contrast in an RGC type that allows the detection of changes in visual context. We trained a convolutional neural network (CNN) model on large-scale functional recordings of RGC responses to natural mouse movies, and then used this model to search in silico for stimuli that maximally excite distinct types of RGCs. This procedure predicted centre colour opponency in transient suppressed-by-contrast (tSbC) RGCs, a cell type whose function is being debated. We confirmed experimentally that these cells indeed responded very selectively to Green-OFF, UV-ON contrasts. This type of chromatic contrast was characteristic of transitions from ground to sky in the visual scene, as might be elicited by head or eye movements across the horizon. Because tSbC cells performed best among all RGC types at reliably detecting these transitions, we suggest a role for this RGC type in providing contextual information (i.e. sky or ground) necessary for the selection of appropriate behavioural responses to other stimuli, such as looming objects. Our work showcases how a combination of experiments with natural stimuli and computational modelling allows discovering novel types of stimulus selectivity and identifying their potential ethological relevance.

Human perception of flicker-fused letters that are luminance balanced

The Talbot-Plateau law specifies what combinations of flash frequency, duration, and intensity will yield a flicker-fused stimulus that matches the brightness of a steady stimulus. It has proven to be remarkably robust in its predictions, and here we provide additional support though the use of a contrast discrimination task. However, we also find that the visual system can register flicker-fused letters when the combination of frequency and duration is relatively low. The letters are recognized even though they have the same physical luminance as background. We hypothesize that the letters elicit synchronous oscillations that encode for stimulus attributes, which prevents the letter from blending into the background.

Decoding dynamic visual scenes across the brain hierarchy

Understanding the computational mechanisms that underlie the encoding and decoding of environmental stimuli is a crucial investigation in neuroscience. Central to this pursuit is the exploration of how the brain represents visual information across its hierarchical architecture. A prominent challenge resides in discerning the neural underpinnings of the processing of dynamic natural visual scenes. Although considerable research efforts have been made to characterize individual components of the visual pathway, a systematic understanding of the distinctive neural coding associated with visual stimuli, as they traverse this hierarchical landscape, remains elusive. In this study, we leverage the comprehensive Allen Visual Coding-Neuropixels dataset and utilize the capabilities of deep learning neural network models to study neural coding in response to dynamic natural visual scenes across an expansive array of brain regions. Our study reveals that our decoding model adeptly deciphers visual scenes from neural spiking patterns exhibited within each distinct brain area. A compelling observation arises from the comparative analysis of decoding performances, which manifests as a notable encoding proficiency within the visual cortex and subcortical nuclei, in contrast to a relatively reduced encoding activity within hippocampal neurons. Strikingly, our results unveil a robust correlation between our decoding metrics and well-established anatomical and functional hierarchy indexes. These findings corroborate existing knowledge in visual coding related to artificial visual stimuli and illuminate the functional role of these deeper brain regions using dynamic stimuli. Consequently, our results suggest a novel perspective on the utility of decoding neural network models as a metric for quantifying the encoding quality of dynamic natural visual scenes represented by neural responses, thereby advancing our comprehension of visual coding within the complex hierarchy of the brain.

The mouse suprachiasmatic nucleus encodes irradiance via a diverse population of neurons monotonically tuned to different ranges of intensity

Many neurons of the mammalian master circadian oscillator in the suprachiasmatic nuclei (SCN) respond to light pulses with irradiance-dependent changes in firing. Here, we set out to better understand this irradiance coding ability by considering how the SCN tracks more continuous changes in irradiance at both population and single unit level. To this end, we recorded extracellular activity in the SCN of anaesthetised mice presented with up + down irradiance staircase stimuli covering moonlight to daylight conditions and incorporating epochs with steady light or superimposed higher frequency modulations (temporal white noise (WN) and frequency/contrast chirps). Single unit activity was extracted by spike sorting. The population response of SCN units to this stimulus was a progressive increase in firing rate at higher irradiances. This relationship was symmetrical for up vs. down phases of the ramp in the presence of white noise or chirps but exhibited hysteresis for steady light, with firing systematically higher during increasing irradiance. Single units also showed a monotonic relationship between firing and irradiance but exhibited diversity not only in response polarity (increases vs. decreases in firing), but also in the sensitivity (EC50 ) and slope of fitted functions. These data show that individual SCN neurons exhibit monotonic relationships between irradiance and firing rate but differ in the irradiance range over which they respond. This property may help the SCN to encode the large differences in irradiance found in nature using neurons with a constrained range of firing rates. KEY POINTS: Daily changes in environmental light (irradiance) entrain the suprachiasmatic nucleus (SCN) circadian clock. The mouse SCN shows graded increases in neurophysiological activity with light pulses of increasing irradiance. We show that this monotonic relationship between firing rate and irradiance is retained at population and single unit level when probed with more naturalistic staircase increases and decreases in irradiance. The irradiance response is more reliable in the presence of ongoing higher temporal frequency modulations in light intensity than under steady light. Single units varied in sensitivity allowing the population to cover a wide range of irradiances. Irradiance coding in the SCN has characteristics of a sparse code with individual neurons tracking different portions of the natural irradiance range. This property may address the challenge of encoding a 109 -fold day:night difference in irradiance within the constrained range of firing rates available to individual neurons.

Diversity of Ganglion Cell Responses to Saccade-Like Image Shifts in the Primate Retina

Saccades are a fundamental part of natural vision. They interrupt fixations of the visual gaze and rapidly shift the image that falls onto the retina. These stimulus dynamics can cause activation or suppression of different retinal ganglion cells, but how they affect the encoding of visual information in different types of ganglion cells is largely unknown. Here, we recorded spiking responses to saccade-like shifts of luminance gratings from ganglion cells in isolated marmoset retinas and investigated how the activity depended on the combination of presaccadic and postsaccadic images. All identified cell types, On and Off parasol and midget cells, as well as a type of Large Off cells, displayed distinct response patterns, including particular sensitivity to either the presaccadic or the postsaccadic image or combinations thereof. In addition, Off parasol and Large Off cells, but not On cells, showed pronounced sensitivity to whether the image changed across the transition. Stimulus sensitivity of On cells could be explained based on their responses to step changes in light intensity, whereas Off cells, in particular, parasol and the Large Off cells, seem to be affected by additional interactions that are not triggered during simple light-intensity flashes. Together, our data show that ganglion cells in the primate retina are sensitive to different combinations of presaccadic and postsaccadic visual stimuli. This contributes to the functional diversity of the output signals of the retina and to asymmetries between On and Off pathways and provides evidence of signal processing beyond what is triggered by isolated steps in light intensity.SIGNIFICANCE STATEMENT Sudden eye movements (saccades) shift our direction of gaze, bringing new images in focus on our retinas. To study how retinal neurons deal with these rapid image transitions, we recorded spiking activity from ganglion cells, the output neurons of the retina, in isolated retinas of marmoset monkeys while shifting a projected image in a saccade-like fashion across the retina. We found that the cells do not just respond to the newly fixated image, but that different types of ganglion cells display different sensitivities to the presaccadic and postsaccadic stimulus patterns. Certain Off cells, for example, are sensitive to changes in the image across transitions, which contributes to differences between On and Off information channels and extends the range of encoded stimulus features.