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A fundamental problem for any sensory system is that the motion of the sensory organ introduces a disturbance in sensory measurement. Nevertheless, animal nervous systems can distinguish and compensate for the sensory consequences of movement by keeping track of them. Hypotheses for estimating the motion of a sensory organ include a corollary discharge associated with motor output and mechanosensory inputs from the moving sense organ. However, how motion-related mechanosensory inputs can reduce motion-dependent sensory ambiguity is unknown. We addressed this question in the olfactory system, where changes in inhalation dynamics alter the sorption of odor molecules by the nasal mucosa and mechanically activate olfactory sensory neurons. We found that changes in inhalation dynamics varied the odor concentration reported by the individual neurons in the piriform cortex. However, the actual odor concentration could be decoded from the population activity regardless of the inhalation dynamics. Dynamics-invariant decoding was possible because piriform cortex neurons have a heterogeneously mixed sensitivity for odor concentration and inhalation dynamics. Thus, the mechanosensory input generated by inhalation expanded the dimensionality of the olfactory encoding space and disentangled the decoding of the actual odor concentration from the disturbance caused by the sniff. Orthogonal integration of motion-related mechanosensory information during the encoding stage may be a canonical strategy adopted by nervous systems to mitigate the sensory ambiguities generated by the motion of the sensory organ.

Many oligodendrocyte precursor cells (OPCs) do not differentiate to form myelin, suggesting additional roles of this cell population. The zebrafish optic tectum contains OPCs in regions devoid of myelin. Elimination of these OPCs impaired precise control of retinal ganglion cell axon arbor size during formation and maturation of retinotectal connectivity, and in consequence impaired visual acuity. Therefore, OPCs fine-tune neural circuit structure and function independently of their canonical role to make myelin.Competing Interest StatementThe authors have declared no competing interest.

The cerebellum is a highly conserved structure of the vertebrate central nervous system that plays a role in the timing and calibration of motor sequences. Its function is supported by the convergence of fibers from granule cells (GCs) and inferior olive neurons (IONs) onto Purkinje cells (PCs). Theories of cerebellar function postulate that IONs convey error signals to PCs that, paired with the contextual information provided by GCs, can be used as a teaching signal to guide motor learning.Here, we use the larval zebrafish to investigate (i) how sensory representations of the same stimulus vary across GCs and IONs and (ii) how PC activity reflects these two different input streams. We use population calcium imaging to measure the cell responses to flashes of diverse luminance and duration to show that IONs and GCs encode different stimulus properties. First, most GCs show tonic and graded responses, as opposed to IONs, whose activity peaks only at on and off luminance transitions, in agreement with the notion that GCs and IONs encode context and error information, respectively. Secondly, we show that GC activity is patterned over time: some neurons had sustained responses for the entire duration of the stimulus, while in others activity was ramping up with slow time constants. This suggests that, by performing temporal integration, GCs could provide a basis that PCs may use to decode time. Finally, we show how PC activity can be largely reconstructed by a linear combination of granule cells and inferior olive neurons. Together, our observations give support to the notion of an error signal coming from IONs, and provide the first experimental evidence for a temporal patterning of GC activity over many seconds.

Animals can use different strategies to navigate. They may guide their movements by relying on external cues in their environment or, alternatively, by using an internal cognitive map of the space around them and their position within it. An essential part of this representation are heading cells, neurons whose activity depends on the heading direction of the animal. Although those cells have been found in vertebrates, the full network has never been observed and there is very little mechanistic understanding of how these cells acquire their response properties. In this study, we use volumetric functional imaging in larval zebrafish to observe, for the first time in a vertebrate, a full network that encodes allocentric heading direction. This network of approximately one hundred inhibitory neurons is arranged in an anatomical circle in the anterior hindbrain. Its activity is driven purely by the integration of internally generated signals, indicating that a simple vertebrate brain can encode maps of how an animal moves within its surroundings. Single cell reconstructions of electron micrographs allow us to uncover how the connectivity pattern of neurons within the network supports the implementation of a ring attractor network. The neurons we identify share features with neurons in the dorsal tegmentum nucleus of rodents and the fly central complex, showing that similar connectivity and mechanistic principles underlie the generation of cognitive maps of heading direction across the animal kingdom.

Three-dimensional (3D) digital brain atlases and high-throughput brain-wide imaging techniques generate large multidimensional datasets that can be registered to a common reference frame. Generating insights from such datasets depends critically on visualization and interactive data exploration, but this a challenging task. Currently available software is dedicated to single atlases, model species or data types, and generating 3D renderings that merge anatomically registered data from diverse sources requires extensive development and programming skills. Here, we present brainrender: an open-source Python package for interactive visualization of multidimensional datasets registered to brain atlases. Brainrender facilitates the creation of complex renderings with different data types in the same visualization and enables seamless use of different atlas sources. High-quality visualizations can be used interactively and exported as high-resolution figures and animated videos. By facilitating the visualization of anatomically registered data, brainrender should accelerate the analysis, interpretation, and dissemination of brain-wide multidimensional data.

The recent development of high-resolution three-dimensional (3D) digital brain atlases and high-throughput brain wide imaging techniques has fueled the generation of large datasets that can be registered to a common reference frame. This registration facilitates integrating data from different sources and resolutions to assemble rich multidimensional datasets. Generating insights from these new types of datasets depends critically on the ability to easily visualize and explore the data in an interactive manner. This is, however, a challenging task. Currently available software is dedicated to single atlases, model species or data types, and generating 3D renderings that merge anatomically registered data from diverse sources requires extensive development and programming skills. To address this challenge, we have developedbrainrender: a generic, open-source Python package for simultaneous and interactive visualization of multidimensional datasets registered to brain atlases.Brainrenderhas been designed to facilitate the creation of complex custom renderings and can be used programmatically or through a graphical user interface. It can easily render different data types in the same visualization, including user-generated data, and enables seamless use of different brain atlases using the same code base. In addition,brainrendergenerates high-quality visualizations that can be used interactively and exported as high-resolution figures and animated videos. By facilitating the visualization of anatomically registered data,brainrendershould accelerate the analysis, interpretation, and dissemination of brain-wide multidimensional data.

Animals must adapt their behavior to survive in a changing environment. Behavioral adaptations can be evoked by two mechanisms: feedback control and internal-model-based control. Feedback controllers can maintain the sensory state of the animal at a desired level under different environmental conditions. In turn, internal models learn the relationship between behavior and resulting sensory consequences in order to modify the behavior when this relationship changes. Here, we present multiple perturbations in visual feedback to larval zebrafish performing the optomotor response and show that they react to these perturbations through a feedback control mechanism. In contrast, if a perturbation is long-lasting, fish adapt their behavior by updating a cerebellum-dependent internal model. We use modelling and functional imaging to show that neuronal requirements for these mechanisms are met in the larval zebrafish brain. Our results illustrate the role of the cerebellum in encoding internal models and how these can calibrate neuronal circuits involved in reactive behaviors depending on the interactions between animal and environment.HighlightsBehavioral reactions to unexpected changes in visual feedback are implemented by a feedback control mechanismA long-lasting change in visual feedback updates the state of the neuronal controllerThe cerebellar internal model mediates this recalibration

We present Stytra, a flexible, open-source software package, written in Python and designed to cover all the general requirements involved in larval zebrafish behavioral experiments. It provides timed stimulus presentation, interfacing with external devices and simultaneous real-time tracking of behavioral parameters such as position, orientation, tail and eye motion in both freely-swimming and head-restrained preparations. Stytra logs all recorded quantities, metadata, and code version in standardized formats to allow full provenance tracking, from data acquisition through analysis to publication. The package is modular and expandable for different experimental protocols and setups. Current releases can be found at https://github.com/portugueslab/stytra. We also provide complete documentation with examples for extending the package to new stimuli and hardware, as well as a schema and parts list for behavioral setups. We showcase Stytra by reproducing previously published behavioral protocols in both head-restrained and freely-swimming larvae. We also demonstrate the use of the software in the context of a calcium imaging experiment, where it interfaces with other acquisition devices. Our aims are to enable more laboratories to easily implement behavioral experiments, as well as to provide a platform for sharing stimulus protocols that permits easy reproduction of experiments and straightforward validation. Finally, we demonstrate how Stytra can serve as a platform to design behavioral experiments involving tracking or visual stimulation with other animals and provide an example integration with the DeepLabCut neural network-based tracking method.

Epileptiform activity is associated with impairment of brain function even in absence of seizures, as demonstrated by failures in various testing paradigm in presence of hypersynchronous interictal spikes (ISs). Clinical evidence suggests that cognitive deficits might be directly caused by the anomalous activity rather than by its underlying etiology. Indeed, we seek to understand whether ISs interfere with neuronal processing in connected areas not directly participating in the hypersynchronous activity in an acute model of epilepsy. Here we cause focal ISs in the visual cortex of anesthetized mice and we determine that, even if ISs do not invade the opposite hemisphere, the local field potential is subtly disrupted with a modulation of firing probability imposed by the contralateral IS activity. Finally, we find that visual processing is altered depending on the temporal relationship between ISs and stimulus presentation. We conclude that focal ISs interact with normal cortical dynamics far from the epileptic focus, disrupting endogenous oscillatory rhythms and affecting information processing.

Epileptiform activity is associated with impairment of brain function even in absence of seizures, as demonstrated by failures in various testing paradigm in presence of hypersynchronous interictal spikes (ISs). Clinical evidence suggests that cognitive deficits might be directly caused by the anomalous activity rather than by its underlying etiology. Indeed, we seek to understand whether ISs interfere with neuronal processing in connected areas not directly participating in the hypersynchronous activity in an acute model of epilepsy. Here we cause focal ISs in the visual cortex of anesthetized mice and we determine that, even if ISs do not invade the opposite hemisphere, the local field potential is subtly disrupted with a modulation of firing probability imposed by the contralateral IS activity. Finally, we find that visual processing is altered depending on the temporal relationship between ISs and stimulus presentation. We conclude that focal ISs interact with normal cortical dynamics far from the epileptic focus, disrupting endogenous oscillatory rhythms and affecting information processing.