Creating and controlling visual environments using BonVision

International Brain Laboratory, Banga K, Benson J, Bhagat J, Biderman D, Birman D, Bonacchi N, Bruijns SA, Buchanan K, Campbell RAA, Carandini M, Chapuis GA, Churchland AK, Davatolhagh MF, Lee HD, Faulkner M, Gerçek B, Hu F, Huntenburg J, Hurwitz CL, Khanal A, Krasniak C, Lau P, Langfield C, Mackenzie N, Meijer GT, Miska NJ, Mohammadi Z, Noel JP, Paninski L, Pan-Vazquez A, Rossant C, Roth N, Schartner M, Socha KZ, Steinmetz NA, Svoboda K, Taheri M, Urai AE, Wang S, Wells M, West SJ, Whiteway MR, Winter O, Witten IB, Zhang Y. Reproducibility of in vivo electrophysiological measurements in mice

Understanding brain function relies on the collective work of many labs generating reproducible results. However, reproducibility has not been systematically assessed within the context of electrophysiological recordings during cognitive behaviors. To address this, we formed a multi-lab collaboration using a shared, open-source behavioral task and experimental apparatus. Experimenters in 10 laboratories repeatedly targeted Neuropixels probes to the same location (spanning secondary visual areas, hippocampus, and thalamus) in mice making decisions; this generated a total of 121 experimental replicates, a unique dataset for evaluating reproducibility of electrophysiology experiments. Despite standardizing both behavioral and electrophysiological procedures, some experimental outcomes were highly variable. A closer analysis uncovered that variability in electrode targeting hindered reproducibility, as did the limited statistical power of some routinely used electrophysiological analyses, such as single-neuron tests of modulation by individual task parameters. Reproducibility was enhanced by histological and electrophysiological quality-control criteria. Our observations suggest that data from systems neuroscience is vulnerable to a lack of reproducibility, but that across-lab standardization, including metrics we propose, can serve to mitigate this.

Selectively vulnerable deep cortical layer 5/6 fast-spiking interneurons in Alzheimer's disease models in vivo

Alzheimer's disease (AD) is initiated by amyloid-beta (Aβ) accumulation in the neocortex; however, the cortical layers and neuronal cell types first susceptible to Aβ remain unknown. Using in vivo two-photon Ca2+ imaging in the visual cortex of AD mouse models, we found that cortical layer 5 neurons displayed abnormally prolonged Ca2+ transients before substantial plaque formation. Neuropixels recordings revealed that these abnormal transients were associated with reduced spiking and impaired visual tuning of parvalbumin (PV)-positive fast-spiking interneurons (FSIs) in layers 5/6, whereas PV-FSIs in superficial layers remained unaffected. These dysfunctions occurred alongside a deep-layer-specific reduction in neuronal pentraxin 2 (NPTX2) within excitatory neurons, decreased GluA4 in PV-FSIs, and fewer excitatory synapses onto PV-FSIs. Notably, NPTX2 overexpression increased excitatory input onto layers 5/6 PV-FSIs and rectified their spiking activity. Thus, our findings reveal an early selective impairment of deep cortical layers 5/6 in AD models and identify deep-layer PV-FSIs as therapeutic targets.

DomeVR: Immersive virtual reality for primates and rodents

Immersive virtual reality (VR) environments are a powerful tool to explore cognitive processes ranging from memory and navigation to visual processing and decision making-and to do so in a naturalistic yet controlled setting. As such, they have been employed across different species, and by a diverse range of research groups. Unfortunately, designing and implementing behavioral tasks in such environments often proves complicated. To tackle this challenge, we created DomeVR, an immersive VR environment built using Unreal Engine 4 (UE4). UE4 is a powerful game engine supporting photo-realistic graphics and containing a visual scripting language designed for use by non-programmers. As a result, virtual environments are easily created using drag-and-drop elements. DomeVR aims to make these features accessible to neuroscience experiments. This includes a logging and synchronization system to solve timing uncertainties inherent in UE4; an interactive GUI for scientists to observe subjects during experiments and adjust task parameters on the fly, and a dome projection system for full task immersion in non-human subjects. These key features are modular and can easily be added individually into other UE4 projects. Finally, we present proof-of-principle data highlighting the functionality of DomeVR in three different species: human, macaque and mouse.

Pre-existing visual responses in a projection-defined dopamine population explain individual learning trajectories

A key challenge of learning a new task is that the environment is high dimensional-there are many different sensory features and possible actions, with typically only a small reward-relevant subset. Although animals can learn to perform complex tasks that involve arbitrary associations between stimuli, actions, and rewards,1,2,3,4,5,6 a consistent and striking result across varied experimental paradigms is that in initially acquiring such tasks, large differences between individuals are apparent in the learning process.7,8,9,10,11,12 What neural mechanisms contribute to initial task acquisition, and why do some individuals learn a new task much more quickly than others? To address these questions, we recorded longitudinally from dopaminergic (DA) axon terminals in mice learning a visual decision-making task.7 Across striatum, DA responses tracked idiosyncratic and side-specific learning trajectories, consistent with widespread reward prediction error coding across DA terminals. However, even before any rewards were delivered, contralateral-side-specific visual responses were present in DA terminals, primarily in the dorsomedial striatum (DMS). These pre-existing responses predicted the extent of learning for contralateral stimuli. Moreover, activation of these terminals improved contralateral performance. Thus, the initial conditions of a projection-specific and feature-specific DA signal help explain individual learning trajectories. More broadly, this work suggests that functional heterogeneity across DA projections may serve to bias target regions toward learning about different subsets of task features, providing a potential mechanism to address the dimensionality of the initial task learning problem.

Innate face-selectivity in the brain of young domestic chicks

Shortly after birth, both naïve animals and newborn babies exhibit a spontaneous attraction to faces and face-like stimuli. While neurons selectively responding to faces have been found in the inferotemporal cortex of adult primates, face-selective domains in the brains of young monkeys seem to develop only later in life after exposure to faces. This has fueled a debate on the role of experience in the development of face-detector mechanisms, since face preferences are well documented in naïve animals, such as domestic chicks reared without exposure to faces. Here, we demonstrate that neurons in a higher-order processing brain area of one-week-old face-naïve domestic chicks selectively respond to a face-like configuration. Our single-cell recordings show that these neurons do not respond to alternative configurations or isolated facial features. Moreover, the population activity of face-selective neurons accurately encoded the face-like stimulus as a unique category. Thus, our findings show that face selectivity is present in the brains of very young animals without preexisting experience.

GoFish: a foray into open-source, aquatic behavioral automation

As the most species-rich vertebrate group, fish provide an array of opportunities to investigate the link between ecological interactions and the evolution of behavior and cognition, yet, as an animal model, they are relatively underutilized in studies of comparative cognition. To address this gap, we developed a fully automated platform for behavioral experiments in aquatic species, GoFish. GoFish includes closed-loop control of task contingencies using real-time video tracking, presentation of visual stimuli, automatic food reward dispensers, and built-in data acquisition. The hardware is relatively inexpensive and accessible, and all software components of the platform are open-source. GoFish facilitates experimental automation, allowing for customization of high-throughput protocols and the efficient acquisition of rich behavioral data. We hope this platform proves to be a useful tool for the research community, facilitating refined, reproducible behavioral experiments on aquatic species in comparative cognition, behavioral ecology, and neuroscience.

Development of neural circuits for social motion perception in schooling fish

The collective behavior of animal groups emerges from the interactions among individuals. These social interactions produce the coordinated movements of bird flocks and fish schools, but little is known about their developmental emergence and neurobiological foundations. By characterizing the visually based schooling behavior of the micro glassfish Danionella cerebrum, we found that social development progresses sequentially, with animals first acquiring the ability to aggregate, followed by postural alignment with social partners. This social maturation was accompanied by the development of neural populations in the midbrain that were preferentially driven by visual stimuli that resemble the shape and movements of schooling fish. Furthermore, social isolation over the course of development impaired both schooling behavior and the neural encoding of social motion in adults. This work demonstrates that neural populations selective for the form and motion of conspecifics emerge with the experience-dependent development of collective movement.

Flexible neural population dynamics govern the speed and stability of sensory encoding in mouse visual cortex

Time courses of neural responses underlie real-time sensory processing and perception. How these temporal dynamics change may be fundamental to how sensory systems adapt to different perceptual demands. By simultaneously recording from hundreds of neurons in mouse primary visual cortex, we examined neural population responses to visual stimuli at sub-second timescales, during different behavioural states. We discovered that during active behavioural states characterised by locomotion, single-neurons shift from transient to sustained response modes, facilitating rapid emergence of visual stimulus tuning. Differences in single-neuron response dynamics were associated with changes in temporal dynamics of neural correlations, including faster stabilisation of stimulus-evoked changes in the structure of correlations during locomotion. Using Factor Analysis, we examined temporal dynamics of latent population responses and discovered that trajectories of population activity make more direct transitions between baseline and stimulus-encoding neural states during locomotion. This could be partly explained by dampening of oscillatory dynamics present during stationary behavioural states. Functionally, changes in temporal response dynamics collectively enabled faster, more stable and more efficient encoding of new visual information during locomotion. These findings reveal a principle of how sensory systems adapt to perceptual demands, where flexible neural population dynamics govern the speed and stability of sensory encoding.

Mesolimbic dopamine ramps reflect environmental timescales

Mesolimbic dopamine activity occasionally exhibits ramping dynamics, reigniting debate on theories of dopamine signaling. This debate is ongoing partly because the experimental conditions under which dopamine ramps emerge remain poorly understood. Here, we show that during Pavlovian and instrumental conditioning, mesolimbic dopamine ramps are only observed when the inter-trial interval is short relative to the trial period. These results constrain theories of dopamine signaling and identify a critical variable determining the emergence of dopamine ramps.

Diving into the zebrafish brain: exploring neuroscience frontiers with genetic tools, imaging techniques, and behavioral insights

The zebrafish (Danio rerio) is increasingly used in neuroscience research. Zebrafish are relatively easy to maintain, and their high fecundity makes them suitable for high-throughput experiments. Their small, transparent embryos and larvae allow for easy microscopic imaging of the developing brain. Zebrafish also share a high degree of genetic similarity with humans, and are amenable to genetic manipulation techniques, such as gene knockdown, knockout, or knock-in, which allows researchers to study the role of specific genes relevant to human brain development, function, and disease. Zebrafish can also serve as a model for behavioral studies, including locomotion, learning, and social interactions. In this review, we present state-of-the-art methods to study the brain function in zebrafish, including genetic tools for labeling single neurons and neuronal circuits, live imaging of neural activity, synaptic dynamics and protein interactions in the zebrafish brain, optogenetic manipulation, and the use of virtual reality technology for behavioral testing. We highlight the potential of zebrafish for neuroscience research, especially regarding brain development, neuronal circuits, and genetic-based disorders and discuss its certain limitations as a model.

Development of neural circuits for social motion perception in schooling fish

Many animals move in groups, where collective behavior emerges from the interactions amongst individuals. These social interactions produce the coordinated movements of bird flocks and fish schools, but little is known about their developmental emergence and neurobiological foundations. By characterizing the visually-based schooling behavior of the micro glassfish Danionella cerebrum, here we found that social development progresses sequentially, with animals first acquiring the ability to aggregate, followed by postural alignment with social partners. This social maturation was accompanied by the development of neural populations in the midbrain and forebrain that were preferentially driven by visual stimuli that resemble the shape and movements of schooling fish. The development of these neural circuits enables the social coordination required for collective movement.

Interactions between rodent visual and spatial systems during navigation

Many behaviours that are critical for animals to survive and thrive rely on spatial navigation. Spatial navigation, in turn, relies on internal representations about one's spatial location, one's orientation or heading direction and the distance to objects in the environment. Although the importance of vision in guiding such internal representations has long been recognized, emerging evidence suggests that spatial signals can also modulate neural responses in the central visual pathway. Here, we review the bidirectional influences between visual and navigational signals in the rodent brain. Specifically, we discuss reciprocal interactions between vision and the internal representations of spatial position, explore the effects of vision on representations of an animal's heading direction and vice versa, and examine how the visual and navigational systems work together to assess the relative distances of objects and other features. Throughout, we consider how technological advances and novel ethological paradigms that probe rodent visuo-spatial behaviours allow us to advance our understanding of how brain areas of the central visual pathway and the spatial systems interact and enable complex behaviours.

Visual spatial location influences selection of instinctive behaviours in mouse

Visual stimuli can elicit instinctive approach and avoidance behaviours. In mouse, vision is known to be important for both avoidance of an overhead threat and approach toward a potential terrestrial prey. The stimuli used to characterize these behaviours, however, vary in both spatial location (overhead or near the ground plane) and visual feature (rapidly expanding disc or slowly moving disc). We therefore asked how mice responded to the same visual features presented in each location. We found that a looming black disc induced escape behaviour when presented overhead or to the side of the animal, but the escapes produced by side-looms were less vigorous and often preceded by freezing behaviour. Similarly, small moving discs induced freezing behaviour when presented overhead or to the side of the animal, but side sweeps also elicited approach behaviours, such that mice explored the area of the arena near where the stimulus had been presented. Our observations therefore show that mice combine cues to the location and features of visual stimuli when selecting among potential behaviours.

Walking humans and running mice: perception and neural encoding of optic flow during self-motion

Locomotion produces full-field optic flow that often dominates the visual motion inputs to an observer. The perception of optic flow is in turn important for animals to guide their heading and interact with moving objects. Understanding how locomotion influences optic flow processing and perception is therefore essential to understand how animals successfully interact with their environment. Here, we review research investigating how perception and neural encoding of optic flow are altered during self-motion, focusing on locomotion. Self-motion has been found to influence estimation and sensitivity for optic flow speed and direction. Nonvisual self-motion signals also increase compensation for self-driven optic flow when parsing the visual motion of moving objects. The integration of visual and nonvisual self-motion signals largely follows principles of Bayesian inference and can improve the precision and accuracy of self-motion perception. The calibration of visual and nonvisual self-motion signals is dynamic, reflecting the changing visuomotor contingencies across different environmental contexts. Throughout this review, we consider experimental research using humans, non-human primates and mice. We highlight experimental challenges and opportunities afforded by each of these species and draw parallels between experimental findings. These findings reveal a profound influence of locomotion on optic flow processing and perception across species. This article is part of a discussion meeting issue 'New approaches to 3D vision'.

Altered low-frequency brain rhythms precede changes in gamma power during tauopathy

Neurodegenerative disorders are associated with widespread disruption to brain activity and brain rhythms. Some disorders are linked to dysfunction of the membrane-associated protein Tau. Here, we ask how brain rhythms are affected in rTg4510 mouse model of tauopathy, at an early stage of tauopathy (5 months), and at a more advanced stage (8 months). We measured brain rhythms in primary visual cortex in presence or absence of visual stimulation, while monitoring pupil diameter and locomotion to establish behavioral state. At 5 months, we found increased low-frequency rhythms during resting state in tauopathic animals, associated with periods of abnormally increased neural synchronization. At 8 months, this increase in low-frequency rhythms was accompanied by a reduction of power in the gamma range. Our results therefore show that slower rhythms are impaired earlier than gamma rhythms in this model of tauopathy, and suggest that electrophysiological measurements can track the progression of tauopathic neurodegeneration.

Plasticity in visual cortex is disrupted in a mouse model of tauopathy

Alzheimer's disease and other dementias are thought to underlie a progressive impairment of neural plasticity. Previous work in mouse models of Alzheimer's disease shows pronounced changes in artificially-induced plasticity in hippocampus, perirhinal and prefrontal cortex. However, it is not known how degeneration disrupts intrinsic forms of brain plasticity. Here we characterised the impact of tauopathy on a simple form of intrinsic plasticity in the visual system, which allowed us to track plasticity at both long (days) and short (minutes) timescales. We studied rTg4510 transgenic mice at early stages of tauopathy (5 months) and a more advanced stage (8 months). We recorded local field potentials in the primary visual cortex while animals were repeatedly exposed to a stimulus over 9 days. We found that both short- and long-term visual plasticity were already disrupted at early stages of tauopathy, and further reduced in older animals, such that it was abolished in mice expressing mutant tau. Additionally, visually evoked behaviours were disrupted in both younger and older mice expressing mutant tau. Our results show that visual cortical plasticity and visually evoked behaviours are disrupted in the rTg4510 model of tauopathy. This simple measure of plasticity may help understand how tauopathy disrupts neural circuits, and offers a translatable platform for detection and tracking of the disease.

Feature selectivity can explain mismatch signals in mouse visual cortex

Sensory experience often depends on one's own actions, including self-motion. Theories of predictive coding postulate that actions are regulated by calculating prediction error, which is the difference between sensory experience and expectation based on self-generated actions. Signals consistent with prediction error have been reported in the mouse visual cortex (V1) when visual flow coupled to running was unexpectedly stopped. Here, we show that such signals can be elicited by visual stimuli uncoupled to an animal running. We record V1 neurons while presenting drifting gratings that unexpectedly stop. We find strong responses to visual perturbations, which are enhanced during running. Perturbation responses are strongest in the preferred orientation of individual neurons, and perturbation-responsive neurons are more likely to prefer slow visual speeds. Our results indicate that prediction error signals can be explained by the convergence of known motor and sensory signals, providing a purely sensory and motor explanation for purported mismatch signals.

Spatial modulation of dark versus bright stimulus responses in the mouse visual system

A fundamental task of the visual system is to respond to both increases and decreases of luminance with action potentials (ON and OFF responses1-4). OFF responses are stronger, faster, and more salient than ON responses in primary visual cortex (V1) of both cats5,6 and primates,7,8 but in ferrets9 and mice,10 ON responses can be stronger, weaker,11 or balanced12 in comparison to OFF responses. These discrepancies could arise from differences in species, experimental techniques, or stimulus properties, particularly retinotopic location in the visual field, as has been speculated;9 however, the role of retinotopy for ON/OFF dominance has not been systematically tested across multiple scales of neural activity within species. Here, we measured OFF versus ON responses across large portions of visual space with silicon probe and whole-cell patch-clamp recordings in mouse V1 and lateral geniculate nucleus (LGN). We found that OFF responses dominated in the central visual field, whereas ON and OFF responses were more balanced in the periphery. These findings were consistent across local field potential (LFP), spikes, and subthreshold membrane potential in V1, and were aligned with spatial biases in ON and OFF responses in LGN. Our findings reveal that retinotopy may provide a common organizing principle for spatial modulation of OFF versus ON processing in mammalian visual systems.

Standardized and reproducible measurement of decision-making in mice

Progress in science requires standardized assays whose results can be readily shared, compared, and reproduced across laboratories. Reproducibility, however, has been a concern in neuroscience, particularly for measurements of mouse behavior. Here, we show that a standardized task to probe decision-making in mice produces reproducible results across multiple laboratories. We adopted a task for head-fixed mice that assays perceptual and value-based decision making, and we standardized training protocol and experimental hardware, software, and procedures. We trained 140 mice across seven laboratories in three countries, and we collected 5 million mouse choices into a publicly available database. Learning speed was variable across mice and laboratories, but once training was complete there were no significant differences in behavior across laboratories. Mice in different laboratories adopted similar reliance on visual stimuli, on past successes and failures, and on estimates of stimulus prior probability to guide their choices. These results reveal that a complex mouse behavior can be reproduced across multiple laboratories. They establish a standard for reproducible rodent behavior, and provide an unprecedented dataset and open-access tools to study decision-making in mice. More generally, they indicate a path toward achieving reproducibility in neuroscience through collaborative open-science approaches.

The International Brain Laboratory, Aguillon-Rodriguez V, Angelaki D, Bayer H, Bonacchi N, Carandini M, Cazettes F, Chapuis G, Churchland AK, Dan Y, Dewitt E, Faulkner M, Forrest H, Haetzel L, Häusser M, Hofer SB, Hu F, Khanal A, Krasniak C, Laranjeira I, Mainen ZF, Meijer G, Miska NJ, Mrsic-Flogel TD, Murakami M, Noel JP, Pan-Vazquez A, Rossant C, Sanders J, Socha K, Terry R, Urai AE, Vergara H, Wells M, Wilson CJ, Witten IB, Wool LE, Zador AM. Standardized and reproducible measurement of decision-making in mice

Progress in science requires standardized assays whose results can be readily shared, compared, and reproduced across laboratories. Reproducibility, however, has been a concern in neuroscience, particularly for measurements of mouse behavior. Here, we show that a standardized task to probe decision-making in mice produces reproducible results across multiple laboratories. We adopted a task for head-fixed mice that assays perceptual and value-based decision making, and we standardized training protocol and experimental hardware, software, and procedures. We trained 140 mice across seven laboratories in three countries, and we collected 5 million mouse choices into a publicly available database. Learning speed was variable across mice and laboratories, but once training was complete there were no significant differences in behavior across laboratories. Mice in different laboratories adopted similar reliance on visual stimuli, on past successes and failures, and on estimates of stimulus prior probability to guide their choices. These results reveal that a complex mouse behavior can be reproduced across multiple laboratories. They establish a standard for reproducible rodent behavior, and provide an unprecedented dataset and open-access tools to study decision-making in mice. More generally, they indicate a path toward achieving reproducibility in neuroscience through collaborative open-science approaches.