Target interception is a complex sensorimotor behavior which requires fine tuning of the sensory system and its strategic coordination with the motor system. Despite various theories about how interception is achieved, its neural implementation remains unknown. We have previously shown that hunting dragonflies employ a balance of reactive and predictive control to intercept prey, using sophisticated model driven predictions to account for expected prey and self-motion. Here we explore the neural substrate of this interception system by investigating a well-known class of target-selective descending neurons (TSDNs). These cells have long been speculated to underlie interception steering but have never been studied in a behaving dragonfly. We combined detailed neuroanatomy, high-precision kinematics data and state-of-the-art neural telemetry to measure TSDN activity during flight. We found that TSDNs are exquisitely tuned to prey angular size and speed at ethological distances, and that they synapse directly onto neck and wing motoneurons in an unusual manner. However, we found that TSDNs were only weakly active during flight and are thus unlikely to provide the primary steering signal. Instead, they appear to drive the foveating head movements that stabilize prey on the eye before and likely throughout the interception flight. We suggest the TSDN population implements the reactive portion of the interception steering control system, coordinating head and wing movements to compensate for unexpected prey motion.

Behavioral neuroscience faces two conflicting demands: long-duration recordings from large neural populations and unimpeded animal behavior. To meet this challenge we developed ONIX, an open-source data acquisition system with high data throughput (2 GB s) and low closed-loop latencies (<1 ms) that uses a 0.3-mm thin tether to minimize behavioral impact. Head position and rotation are tracked in three dimensions and used to drive active commutation without torque measurements. ONIX can acquire data from combinations of passive electrodes, Neuropixels probes, head-mounted microscopes, cameras, three-dimensional trackers and other data sources. We performed uninterrupted, long (~7 h) neural recordings in mice as they traversed complex three-dimensional terrain, and multiday sleep-tracking recordings (~55 h). ONIX enabled exploration with similar mobility as nonimplanted animals, in contrast to conventional tethered systems, which have restricted movement. By combining long recordings with full mobility, our technology will enable progress on questions that require high-quality neural recordings during ethologically grounded behaviors.

Simultaneous recordings from hundreds or thousands of neurons are becoming routine because of innovations in instrumentation, molecular tools, and data processing software. Such recordings can be analyzed with data science methods, but it is not immediately clear what methods to use or how to adapt them for neuroscience applications. We review, categorize, and illustrate diverse analysis methods for neural population recordings and describe how these methods have been used to make progress on longstanding questions in neuroscience. We review a variety of approaches, ranging from the mathematically simple to the complex, from exploratory to hypothesis-driven, and from recently developed to more established methods. We also illustrate some of the common statistical pitfalls in analyzing large-scale neural data.

To understand neocortical function, we must first define its cell types. Recent studies indicate that neurons in the deepest cortical layer play roles in mediating thalamocortical interactions and modulating brain state and are implicated in neuropsychiatric disease. However, understanding the functions of deep layer 6 (L6b) neurons has been hampered by the lack of agreed upon definitions for these cell types. We compared commonly used methods for defining L6b neurons, including molecular, transcriptional and morphological approaches as well as transgenic mouse lines, and identified a core population of L6b neurons. This population does not innervate sensory thalamus, unlike layer 6 corticothalamic neurons (L6CThNs) in more superficial layer 6. Rather, single L6b neurons project ipsilaterally between cortical areas. Although L6b neurons undergo early developmental changes, we found that their intrinsic electrophysiological properties were stable after the first postnatal week. Our results provide a consensus definition for L6b neurons, enabling comparisons across studies.

In natural environments, animals must efficiently allocate their choices across multiple concurrently available resources when foraging, a complex decision-making process not fully captured by existing models. To understand how rodents learn to navigate this challenge we developed a novel paradigm in which untrained, water-restricted mice were free to sample from six options rewarded at a range of deterministic intervals and positioned around the walls of a large ( 2m) arena. Mice exhibited rapid learning, matching their choices to integrated reward ratios across six options within the first session. A reinforcement learning model with separate states for staying or leaving an option and a dynamic, global learning rate was able to accurately reproduce mouse learning and decision-making. Fiber photometry recordings revealed that dopamine in the nucleus accumbens core (NAcC), but not dorsomedial striatum (DMS), more closely reflected the global learning rate than local error-based updating. Altogether, our results provide insight into the neural substrate of a learning algorithm that allows mice to rapidly exploit multiple options when foraging in large spatial environments.

Chemical synapses are the major sites of communication between neurons in the nervous system and mediate either excitatory or inhibitory signaling. At excitatory synapses, glutamate is the primary neurotransmitter and upon release from presynaptic vesicles, is detected by postsynaptic glutamate receptors, which include ionotropic AMPA and NMDA receptors. Here, we have developed methods to identify glutamatergic synapses in brain tissue slices, label AMPA receptors with small gold nanoparticles (AuNPs), and prepare lamella for cryo-electron tomography studies. The targeted imaging of glutamatergic synapses in the lamella is facilitated by fluorescent pre- and postsynaptic signatures, and the subsequent tomograms allow for the identification of key features of chemical synapses, including synaptic vesicles, the synaptic cleft, and AuNP-labeled AMPA receptors. These methods pave the way for imaging brain regions at high resolution, using unstained, unfixed samples preserved under near-native conditions.

In the fruit fly, Drosophila melanogaster, connectome data and genetic tools provide a unique opportunity to study complex behaviors including navigation, mating, aggression, and grooming in an organism with a tractable nervous system of 140,000 neurons. Here we present the Fly Disco, a flexible system for high quality video collection, optogenetic manipulation, and fine-grained behavioral analysis of freely walking and socializing fruit fly groups. The data collection hardware and software automates the collection of videos synced to programmable optogenetic stimuli. Key pipeline features include behavioral analysis based on trajectories of 21 keypoints and optogenetic-specific summary statistics and data visualization. We created the multifly dataset for pose estimation that includes 9701 examples enriched in complex behaviors. All hardware designs, software, and the multifly dataset are freely available.

Clostridium perfringens is a Gram-positive anaerobic spore-forming bacterial pathogen of humans and animals. C. perfringens also produces type IV pili (T4P) and has two complete sets of T4P-associated genes, one of which has been shown to produce surface pili needed for cell adherence. One hypothesis about the role of the other set of T4P genes is that they could comprise a system analogous to the type II secretion systems (TTSS) found in Gram-negative bacteria, which is used to export folded proteins from the periplasm through the outer membrane to the extracellular environment. Gram-positive bacteria have a similar secretion barrier in the thick peptidoglycan (PG) layer, which blocks secretion of folded proteins >25 kD. To determine if the T4P-associated genes comprise a Gram-positive TTSS, the secretome of mutants lacking type IV pilins were examined and a single protein, a von Willebrand A domain containing protein BsaC (CPE0517) was identified as being dependent on PilA3 for secretion. BsaC is in an operon with a signal peptidase and two putative biofilm matrix proteins with homology to Bacillus subtilis TasA. One of these proteins, BsaA, was shown by another group to produce high mol wt oligomers. We analyzed BsaA monomer interactions with de novo modeling, which projected that the monomers formed isopeptide bonds as part of a donor strand exchange process. Mutations in residues predicted to form the isopeptide bonds led to loss of oligomerization, supporting the predicted bond formation process. Phylogenetic analysis showed the BsaA family of proteins are widespread among bacteria and archaea but only a subset are predicted to form isopeptide bonds.

A cognitive compass enabling spatial navigation requires neural representation of heading direction (HD), yet the neural circuit architecture enabling this representation remains unclear. While various network models have been proposed to explain HD systems, these models rely on simplified circuit architectures that are incompatible with empirical observations from connectomes. Here we construct a novel network model for the fruit fly HD system that satisfies both connectome-derived architectural constraints and the functional requirement of continuous heading representation. We characterize an ensemble of continuous attractor networks where compass neurons providing local mutual excitation are coupled to inhibitory neurons. We discover a new mechanism where continuous heading representation emerges from combining symmetric and anti-symmetric activity patterns. Our analysis reveals three distinct realizations of these networks that all match observed compass neuron activity but differ in their predictions for inhibitory neuron activation patterns. Further, we found that deviations from these realizations can be compensated by cell-type-specific rescaling of synaptic weights, which could be potentially achieved through neuromodulation. This framework can be extended to incorporate the complete fly central complex connectome and could reveal principles of neural circuits representing other continuous quantities, such as spatial location, across insects and vertebrates.

Synaptic transmission mediated by various neurotransmitters influences a wide range of behaviors. However, understanding how neuromodulatory transmitters encode diverse behaviors and affect their functions remains challenging. Here, we introduce GESIAP3.0, an advanced, third-generation image analysis program based on genetically encoded sensors. This tool enables precise quantitative analysis of transmission in both awake, freely moving animals and immobilized subjects. GESIAP3.0 incorporates movement correction algorithms that effectively eliminate image displacement in behaving animals while optimizing synaptic information extraction and simplifying computations on commodity computers. Quantitative analysis of cholinergic, dopaminergic, and serotonergic transmission, corrected for tissue movement, revealed synaptic properties consistent with measurements from ex vivo wide-field and in vivo two-photon imaging under stable conditions. This validates the applicability of GESIAP3.0 for analyzing synaptic properties of neuromodulatory transmission in behaving animals.