Biology subjects

DeAngelis, G.

Publications and source records attributed to DeAngelis, G..

5 recordsLinked to original sources

bioRxiv2025

Bayesian causal inference unifies perceptual and neuronal processing of center-surround motion in area MT

Center-surround interactions are a hallmark of visual processing and are especially prominent in area MT, where surround motion can either suppress or facilitate neuronal responses depending on context. However, existing mechanistic descriptions, including divisive normalization, do not explain the full diversity of these effects or their relationship to motion perception. Here, we show that both perceptual and neuronal center-surround phenomena can be understood as consequences of Bayesian causal inference over reference frames. Building on a normative model of motion perception, we derived predictions for the mean responses and variability of single MT neurons across the full fourdimensional space of center and surround directions and speeds. The model generates structured patterns of suppression, facilitation, and coordinate-frame selectivity that qualitatively match the diversity of center-surround effects reported in primate MT. Our results provide a unified computational account linking motion integration and segmentation in perception with contextual response modulation in MT, and yield testable predictions for how the visual system infers and represents reference frames.

neuroscience↗

bioRxiv2022

Scene-relative object motion biases depth percepts

An important function of the visual system is to represent 3D scene structure from a sequence of 2D images projected onto the retinae. During observer translation, the relative image motion of stationary objects at different distances (motion parallax) provides potent depth information. However, if an object moves relative to the scene, this complicates the computation of depth from motion parallax since there will be an additional component of image motion related to scene-relative object motion. To correctly compute depth from motion parallax, only the component of image motion caused by self-motion should be used by the brain. Previous experimental and theoretical work on perception of depth from motion parallax has assumed that objects are stationary in the world. Thus, it is unknown whether perceived depth based on motion parallax is biased by object motion relative to the scene. Naive human subjects viewed a virtual 3D scene consisting of a ground plane and stationary background objects, while lateral self-motion was simulated by optic flow. A target object could be either stationary or moving laterally at different velocities, and subjects were asked to judge the depth of the object relative to the plane of fixation. Subjects showed a far bias when object and observer moved in the same direction, and a near bias when object and observer moved in opposite directions. This pattern of biases is expected if subjects confound image motion due to self-motion with that due to scene-relative object motion. These biases were large when the object was viewed monocularly, and were greatly reduced, but not eliminated, when binocular disparity cues were provided. Our findings establish that scene-relative object motion can confound perceptual judgements of depth during self-motion.

neuroscience↗

bioRxiv2021

A neural mechanism for detecting object motion during self-motion

Detecting objects that move in a scene is a fundamental computation performed by the visual system. This computation is greatly complicated by observer motion, which causes most objects to move across the retinal image. How the visual system detects scene-relative object motion during self-motion is poorly understood. Human behavioral studies suggest that the visual system may identify local conflicts between motion parallax and binocular disparity cues to depth, and may use these signals to detect moving objects. We describe a novel mechanism for performing this computation based on neurons in macaque area MT with incongruent depth tuning for binocular disparity and motion parallax cues. Neurons with incongruent tuning respond selectively to scene-relative object motion and their responses are predictive of perceptual decisions when animals are trained to detect a moving object during self-motion. This finding establishes a novel functional role for neurons with incongruent tuning for multiple depth cues.

neuroscience↗

bioRxiv2018

Neuronal thresholds and correlations in the peripheral vestibular system during rotation discrimination

Neuronal and behavioral thresholds were measured simultaneously as trained male macaques performed a yaw rotation discrimination task in darkness. When corrected to account for variations in neuronal direction preferences, neurons in the vestibular nuclei and semicircular canal afferents had discrimination thresholds that were only two-fold smaller than behavioral thresholds. There was no significant trial-by-trial correlation between neuronal activity and perceptual decisions, despite the presence of significant pair-wise noise correlations. The lack of choice-related activity during rotation discrimination contrasts with the robust correlations observed previously between brainstem neurons and choices during translation perception. These results suggest task-dependent differences in subcortical processing of vestibular signals, as well as how signals related to perceptual decisions may propagate back to early stages of sensory processing.\n\nSIGNIFICANCE STATEMENTThis is the first ever simultaneous recordings of neural and behavioral thresholds during rotation discrimination. Its importance lies on the fact that the vestibular system provides an excellent model to probe origins of perception because directional selectivity signals are similar at many levels of processing, from afferents to cortex. The findings of similar neuronal and behavioral discrimination thresholds, significant inter-neuronal correlations, but lack of correlations between behavior and neuronal activity of both afferents and central brainstem neurons are intriguing and suggest task-dependent organization of early sensory areas.

neuroscience↗

bioRxiv2017

Inferring decoding strategies for multiple correlated neural populations

Studies of neuron-behaviour correlation and causal manipulation have long been used separately to understand the neural basis of perception. Yet these approaches sometimes lead to drastically conflicting conclusions about the functional role of brain areas. Theories that focus only on choice-related neuronal activity cannot reconcile those findings without additional experiments involving large-scale recordings to measure interneuronal correlations. By expanding current theories of neural coding and incorporating results from inactivation experiments, we demonstrate here that it is possible to infer decoding weights of different brain areas without precise knowledge of the correlation structure. We apply this technique to neural data collected from two different cortical areas in macaque monkeys trained to perform a heading discrimination task. We identify two opposing decoding schemes, each consistent with data depending on the nature of correlated noise. Our theory makes specific testable predictions to distinguish these scenarios experimentally without requiring measurement of the underlying noise correlations.\n\nAuthor SummaryThe neocortex is structurally organized into distinct brain areas. The role of specific brain areas in sensory perception is typically studied using two kinds of laboratory experiments: those that measure correlations between neural activity and reported percepts, and those that inactivate a brain region and measure the resulting changes in percepts. The two types of experiments have generally been interpreted in isolation, in part because no theory has been able combine their outcomes. Here, we describe a mathematical framework that synthesizes both kinds of results, giving us a new way to assess how different brain areas contribute to perception. When we apply our framework to experiments on behaving monkeys, we discover two models that can explain the perplexing finding that one brain area can predict an animals percepts, even though the percepts are not affected when that brain area is inactivated. The two models ascribe dramatically different efficiencies to brain computation. We show that these two models can be distinguished by an experiment that measures correlations while inactivating different brain areas.

neuroscience↗