Innate behavioral biases and preferences can vary significantly among individuals of the same genotype. Though individuality is a fundamental property of behavior, it is not currently understood how individual differences in brain structure and physiology produce idiosyncratic behaviors. Here we present evidence for idiosyncrasy in olfactory behavior and neural responses in We show that individual female from a highly inbred laboratory strain exhibit idiosyncratic odor preferences that persist for days. We used in vivo calcium imaging of neural responses to compare projection neuron (second-order neurons that convey odor information from the sensory periphery to the central brain) responses to the same odors across animals. We found that, while odor responses appear grossly stereotyped, upon closer inspection, many individual differences are apparent across antennal lobe (AL) glomeruli (compact microcircuits corresponding to different odor channels). Moreover, we show that neuromodulation, environmental stress in the form of altered nutrition, and activity of certain AL local interneurons affect the magnitude of interfly behavioral variability. Taken together, this work demonstrates that individual exhibit idiosyncratic olfactory preferences and idiosyncratic neural responses to odors, and that behavioral idiosyncrasies are subject to neuromodulation and regulation by neurons in the AL.

The influence of peripheral physiology on goal-directed behavior involves specialized interoceptive sensory neurons that signal internal state to the brain. Here, we review recent progress to examine the impact of these specialized cell types on neurons and circuits throughout the central nervous system. These new approaches are important for understanding how the needs of the body interact and guide goal-directed behaviors.

Hunger and thirst have distinct goals but control similar ingestive behaviors, and little is known about neural processes that are shared between these behavioral states. We identify glutamatergic neurons in the peri-locus coeruleus (periLC neurons) as a polysynaptic convergence node from separate energy-sensitive and hydration-sensitive cell populations. We develop methods for stable hindbrain calcium imaging in free-moving mice, which show that periLC neurons are tuned to ingestive behaviors and respond similarly to food or water consumption. PeriLC neurons are scalably inhibited by palatability and homeostatic need during consumption. Inhibition of periLC neurons is rewarding and increases consumption by enhancing palatability and prolonging ingestion duration. These properties comprise a double-negative feedback relationship that sustains food or water consumption without affecting food- or water-seeking. PeriLC neurons are a hub between hunger and thirst that specifically controls motivation for food and water ingestion, which is a factor that contributes to hedonic overeating and obesity.

We present a method for microtubule tracking in electron microscopy volumes. Our method first identifies a sparse set of voxels that likely belong to microtubules. Similar to prior work, we then enumerate potential edges between these voxels, which we represent in a candidate graph. Tracks of microtubules are found by selecting nodes and edges in the candidate graph by solving a constrained optimization problem incorporating biological priors on microtubule structure. For this, we present a novel integer linear programming formulation, which results in speed-ups of three orders of magnitude and an increase of 53% in accuracy compared to prior art (evaluated on three 1 . 2 × 4 × 4µm volumes of Drosophila neural tissue). We also propose a scheme to solve the optimization problem in a block-wise fashion, which allows distributed tracking and is necessary to process very large electron microscopy volumes. Finally, we release a benchmark dataset for microtubule tracking, here used for training, testing and validation, consisting of eight 30 x 1000 x 1000 voxel blocks (1 . 2 × 4 × 4µm) of densely annotated microtubules in the CREMI data set (https://github.com/nilsec/micron).

Large scientific projects in genomics and astronomy are influential not because they answer any single question but because they enable investigation of continuously arising new questions from the same data-rich sources. Advances in automated mapping of the brain's synaptic connections (connectomics) suggest that the complicated circuits underlying brain function are ripe for analysis. We discuss benefits of mapping a mouse brain at the level of synapses.

Calcium imaging with fluorescent protein sensors is widely used to record activity in neuronal populations. The transform between neural activity and calcium-related fluorescence involves nonlinearities and low-pass filtering, but the effects of the transformation on analyses of neural populations are not well understood. We compared neuronal spikes and fluorescence in matched neural populations in behaving mice. We report multiple discrepancies between analyses performed on the two types of data, including changes in single-neuron selectivity and population decoding. These were only partially resolved by spike inference algorithms applied to fluorescence. To model the relation between spiking and fluorescence we simultaneously recorded spikes and fluorescence from individual neurons. Using these recordings we developed a model transforming spike trains to synthetic-imaging data. The model recapitulated the differences in analyses. Our analysis highlights challenges in relating electrophysiology and imaging data, and suggests forward modeling as an effective way to understand differences between these data.

Understanding how the brain encodes and processes information requires the recording of neural activity that underlies different behaviors. Recent efforts in fluorescent protein engineering have succeeded in developing powerful tools for visualizing neural activity, in general by coupling neural activity to different properties of a fluorescent protein scaffold. Here, we take advantage of a previously unexploited class of reversibly switchable fluorescent proteins to engineer a new type of calcium sensor. We introduce rsCaMPARI, a genetically encoded calcium marker engineered from a reversibly switchable fluorescent protein that enables spatiotemporally precise marking, erasing, and remarking of active neuron populations under brief, user-defined time windows of light exposure. rsCaMPARI photoswitching kinetics are modulated by calcium concentration when illuminating with blue light, and the fluorescence can be reset with violet light. We demonstrate the utility of rsCaMPARI for marking and remarking active neuron populations in freely swimming zebrafish.

Drosophila melanogaster is an established model for neuroscience research with relevance in biology and medicine. Until recently, research on the Drosophila brain was hindered by the lack of a complete and uniform nomenclature. Recognizing this, Ito et al. (2014) produced an authoritative nomenclature for the adult insect brain, using Drosophila as the reference. Here, we extend this nomenclature to the adult thoracic and abdominal neuromeres, the ventral nerve cord (VNC), to provide an anatomical description of this major component of the Drosophila nervous system. The VNC is the locus for the reception and integration of sensory information and involved in generating most of the locomotor actions that underlie fly behaviors. The aim is to create a nomenclature, definitions, and spatial boundaries for the Drosophila VNC that are consistent with other insects. The work establishes an anatomical framework that provides a powerful tool for analyzing the functional organization of the VNC.

The dopamine transporter (DAT) is critical for spatiotemporal control of dopaminergic neurotransmission and the target for therapeutic agents, including ADHD medications, and abused substances, such as cocaine. Here, we develop new fluorescently labeled ligands that bind DAT with high affinity and enable single-molecule detection of the transporter. The cocaine analogue MFZ2-12 (1) was conjugated to novel rhodamine-based Janelia Fluorophores (JF549 and JF646). High affinity binding of the resulting ligands to DAT was demonstrated by potent inhibition of [3H]dopamine uptake in DAT transfected CAD cells and by competition radioligand binding experiments on rat striatal membranes. Visualization of binding was substantiated by confocal or TIRF microscopy revealing selective binding of the analogues to DAT transfected CAD cells. Single particle tracking experiments were performed with JF549-conjugated DG3-80 (3) and JF646-conjugated DG4-91 (4) on DAT transfected CAD cells enabling quantification and categorization of the dynamic behavior of DAT into four distinct motion classes (immobile, confined, Brownian, and directed). Finally, we show that the ligands can be used in direct stochastic optical reconstruction microscopy (dSTORM) experiments permitting further analyses of DAT distribution on the nanoscale. In summary, these novel fluorescent ligands are promising new tools for studying DAT localization and regulation with single-molecule resolution.

Deep neural networks trained to inpaint partially occluded images show a deep understanding of image composition and have even been shown to remove objects from images convincingly. In this work, we investigate how this implicit knowledge of image composition can be be used to separate cells in densely populated microscopy images. We propose a measure for the independence of two image regions given a fully self-supervised inpainting network and separate objects by maximizing this independence. We evaluate our method on two cell segmentation datasets and show that cells can be separated completely unsupervised. Furthermore, combined with simple foreground detection, our method yields instance segmentation of similar quality to fully supervised methods.