Optic flow

Natural vision involves encoding complex inputs with spatial and temporal variations in luminance and contrast, which vary widely within and across visual scenes. This engages multiple interacting neural circuits that process these features and implement functions like gain control, decorrelation, and contextual prediction. One hallmark of such circuit interactions at the cortical level is that they give rise to fast oscillatory dynamics, which are reflected in the local field potential (LFP). While recent studies in mice have linked oscillations driven by artificial stimuli to specific thalam

Predicting neural activity in higher visual areas (HVAs) in mice during spatial navigation is challenging due to the complex interaction between spatial and temporal processing. One recent two-stream hypothesis for rodents proposed by Saleem (2020) suggests that form-based goals and detailed landmark signals are processed by cortical areas biased towards the central visual field, while self-motion signals are processed in the periphery. Inspired by this, we developed a deep learning model that splits visual input into two streams: a convolutional neural network (CNN) processed the central visu

…target-sensitive neurons found in the optic lobes are known as small target motion detectors (STMDs). STMDs are believed to be presynaptic to the target selective descending neurons (TSDNs) that are in turn presynaptic to motor neurons. In TSDNs, the response to targ

…studied how the mouse Nucleus of the Optic Tract (NOT) integrates retinal visual information and how this supports behavioral responses. NOT mediates the horizontal optokinetic reflex, the com

…as visual motion signals, known as optic flow. Fast and efficient interpretation of the global structure of optic flow is thought to be critical for maintaining stability and guiding the animal's course during navigation. In flies, the lobula plate tangential cells (LPTCs), a highly interconnected network of ~60 neurons, are known to be specialised optic flow detectors. Bridging the sensory periphery with motor output structures, LPTCs are ideally positioned for course c

…melanogaster, where optic flow visual stimulation modulates walking or flying patterns. Despite a large body of literature on OKR, there is still limited understanding about the impact on OKR of concomitant, potentially conflictual, inputs originating from other sensory channels, as happens in the environment (multimodal integration). We combined optic flow stimulation with the pre

Retinotopically organized visual area 6 (V6) processes optic flow in animals and humans. We previously demonstrated that V6 is a sensory independent area involved in egocentric navigation. Indeed, V6 of congenitally blind (CB) people encodes auditory input for egocentric navigation similarly to the way V6 of sighted people encodes visual cues. In the current study, rest functional connectivity was used to investigate training induced brain connectivity changes in CB participants. CB participants were scanned during resting state sessions before and after a three-day training period learning to

…which produces both mechanosensory (airflow) and visual (optic flow) experience. How the fly brain integrates these sensory inputs to estimate ambient wind direction is not known. Here we explore how a circuit in the Drosophila Central Complex (CX) might integrate airflow and optic flow

…information — whereby the speed of optic flow generated by self-motion depends on the depth of visual cues. This enables animals to estimate depth by comparing visual motion and self-motion speeds. As neurons in the mouse primary visual cortex (V1) are broadly modulated by locomotion, we hypothesized that they may integrate visual- and locomotion-related signals

…correlate with self-motion and with optic flow resulting from rotations and translations in 2D. However, flying animals, like bats and inse

Sensory inputs available to animals are often open to multiple interpretations. When evaluating the likelihood of a certain interpretation, it is important that they consider not only evidence confirming that interpretation, but also evidence against it. Human reasoning often fails to appropriately weigh negative evidence, a phenomenon termed confirmation bias. However, since behaving based on false beliefs is costly, animals may detect negative evidence better in the context of innate, naturalistic behaviors. An example of such behaviors is visual course stabilization. Existing models assume

Mice sample the visual scene by moving their head and eyes. However, little is known about how eye and head position are integrated into visual processing during natural movement, because studies of visual processing are generally performed during head fixation. To address this, we analyzed the retinal input encountered during free exploration by measuring the mouse’s visual scene (“worldcam”) and eye/head position using head-mounted cameras and an inertial measurement unit (IMU). Based on these measures, we trained a neural network to estimate the retinal input by gaze-correcting the worldcam

The processing of natural visual environments rich with motion is central to animal survival. Many aspects of perceiving and detecting visual motion are already mature before the onset of visual experience. How the visual system develops to enable these functions in the absence of external stimuli remains unclear. A prominent transient feature of developing circuits is the ability to generate complex spatiotemporal patterns of spontaneous activity, even before sensory organs mature. For example, before vision onset in the mammalian retina, activity propagates in the form of spatiotemporally st

Self-motion through a visual world creates a pattern of expanding visual motion called optic flow. Heading estimation from the optic flow is accurate in rigid environments. But it becomes challenging when other humans introduce an independent motion to the scene. The biological motion of human walkers consists of translation through space and associated limb articulation. The characteristic motion pattern is regular, though complex. A world full of humans moving around is nonrigid, causing heading errors. But limb articulation alone does not perturb the global structure of the flow field, matc

The apparent motion of features in a visual scene — optic flow — provides rich information about the animal’s self-motion and its environment [Gibson 1950]. An animal estimates the optic flow with motion sensitive neurons. In flies, the T4/T5 neurons encode small-field directional motion signals by sampling neighboring eye facets. The signals are then integrated by large-field neurons, which are tuned to complex global patterns of optic flow. Are these patterns simply inherited from their T4/5 inputs or synthesized by a more complex computation? Near the center of the eye, the 4 T4 subtypes ha

Hierarchical predictive coding (hPC) is one of the major theories of sensory processing and cortical computation. It provides a comprehensive explanation of the role of top-down connections in cortex, by proposing how they can guide sensory processing, and has inspired many experimental studies, ranging from electrophysiological studies of individual neurons to high-level studies of cognitive computation. Still, after twenty years of research, important questions remain open: Experimental evidence for error units, which are central to the theory, is inconclusive, and little theoretical work ex

Animals can discriminate time intervals ranging from microseconds to days, which serves to regulate functions as diverse as sound localization to circadian rhythmicity (Mauk and Buonomano, 2004; Buhusi and Meck, 2005). While the mechanisms that enable temporal processing at the microsecond and day scales are relatively well understood (Jeffress, 1948; Rosbash and Hall, 1989), those that enable animals to discriminate time in the behaviorally relevant seconds to minutes scale are not clear. It is believed that there might be at least two distinct interval timing mechanisms, one involving the ce

I am working on color vision in Drosophila, identifying behaviors that involve color vision and understanding the neural circuits supporting them (Longden 2016). I have a long-term interest in understanding how neural computations operate reliably under changing circumstances, be they external changes in the sensory context, or internal changes of state such as hunger and locomotion. On internal state-modulation of sensory processing, I have shown how hunger alters visual motion processing in blowflies (Longden et al. 2014), and identified a role for octopamine in modulating motion vision duri

…prominent example is the encoding of optic flow fields that are generated during self-motion of the animal and will therefore depend on the type of locomotion. How evolution has matched computational resources to the behavioral constraints of an animal is not known. Here we use in vivo two photon imaging to record from a population of >3.500 local-direction selecti

…“compass” signal is combined with optic flow to compute and represent the direction that a fly is traveling. This provides a general framework for understand how flies perform vector computations.

Animals are able to perceive self-motion and navigate in their environment using optic flow information. They often perform visually guided stabilization behaviors like the optokinetic (OKR) or optomotor response (OMR) in order to maintain their eye and body position relative to the moving surround. But how does the animal manage to perform appropriate behavioral response and how are processing tasks divided between the various non-cortical visual brain areas? Experiments have shown that the zebrafish pretectum, which is homologous to the mammalian accessory optic system, is involved in the OK

The development of neural circuits is profoundly impacted by both spontaneous and sensory experience. This is perhaps most well studied in the visual system, where disruption of early spontaneous activity called retinal waves prior to eye opening and visual deprivation after eye opening leads to alterations in the response properties and connectivity in several visual centers in the brain. We address this question in the retina, which comprises multiple circuits that encode different features of the visual scene, culminating in over 40 different types of retinal ganglion cells. Direction-selec