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Summary: Sampling behaviors have sensory consequences that can hinder perceptual stability. In olfaction, sniffing affects early odor encoding, mimicking a sudden change in odor concentration. We examined how the inhalation speed affects the representation of odor concentration in the main olfactory cortex. Neurons combine the odor input with a global top-down signal preceding the sniff and a mechanosensory feedback generated by the air passage through the nose during inhalation. Still, the population representation of concentration is remarkably sniff invariant. This is because the mechanosensory and olfactory responses are uncorrelated within and across neurons. Thus, faster odor inhalation and an increase in concentration change the cortical activity pattern in distinct ways. This encoding strategy affords tolerance to potential concentration fluctuations caused by varying inhalation speeds. Since mechanosensory reafferences are widespread across sensory systems, the coding scheme described here may be a canonical strategy to mitigate the sensory ambiguities caused by movements.

Animals can adjust their behavior in response to changes in the environment when these changes can be predicted. Here the authors show the role of the cerebellum in zebrafish that change their swimming as they adjust to long-lasting changes in visual feedback

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.

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.

Impairments of the dialog between excitation and inhibition (E/I) is commonly associated to neuropsychiatric disorders like autism, bipolar disorders and epilepsy. Moderate levels of hyperexcitability can lead to mild alterations of the EEG and are often associated with cognitive deficits even in the absence of overt seizures. Indeed, various testing paradigms have shown degraded performances in presence of acute or chronic non-ictal epileptiform activity. Evidences from both animal models and the clinics suggest that anomalous activity can cause cognitive deficits by transiently disrupting cortical processing, independently from the underlying etiology of the disease. Here, we will review our understanding of the influence of an abnormal EEG activity on brain computation in the context of the available clinical data and in genetic or pharmacological animal models.