During sensorimotor learning, neuronal networks change to optimize the associations between action and perception. In this study, we examine how the brain harnesses neuronal patterns that correspond to the current action-perception state during learning. To this end, we recorded activity from motor cortex while monkeys either performed a familiar motor task (movement-state) or learned to control the firing rate of a target neuron using a brain-machine interface (BMI-state). Before learning, monkeys were placed in an observation-state, where no action was required. We found that neuronal patterns during the BMI-state were markedly different from the movement-state patterns. BMI-state patterns were initially similar to those in the observation-state and evolved to produce an increase in the firing rate of the target neuron. The overall activity of the non-target neurons remained similar after learning, suggesting that excitatory-inhibitory balance was maintained. Indeed, a novel neural-level reinforcement-learning network model operating in a chaotic regime of balanced excitation and inhibition predicts our results in detail. We conclude that during BMI learning, the brain can adapt patterns corresponding to the current action-perception state to gain rewards. Moreover, our results show that we can predict activity changes that occur during learning based on the pre-learning activity. This new finding may serve as a key step toward clinical brain-machine interface applications to modify impaired brain activity.

The central amygdala (CEA) has been richly studied for interpreting function and behavior according to specific cell types and circuits. Such work has typically defined molecular cell types by classical inhibitory marker genes; consequently, whether marker-gene-defined cell types exhaustively cover the CEA and co-vary with connectivity remains unresolved. Here, we combined single-cell RNA sequencing, multiplexed fluorescent in situ hybridization, immunohistochemistry, and long-range projection mapping to derive a “bottom-up” understanding of CEA cell types. In doing so, we identify two major cell types, encompassing one-third of all CEA neurons, that have gone unresolved in previous studies. In spatially mapping these novel types, we identify a non-canonical CEA subdomain associated with Nr2f2 expression and uncover an Isl1-expressing medial cell type that accounts for many long-range CEA projections. Our results reveal new CEA organizational principles across cell types and spatial scales and provide a framework for future work examining cell-type-specific behavior and function.

The detection of visual motion enables sophisticated animal navigation, and studies on flies have provided profound insights into the cellular and circuit bases of this neural computation. The fly's directionally selective T4 and T5 neurons encode ON and OFF motion, respectively. Their axons terminate in one of the four retinotopic layers in the lobula plate, where each layer encodes one of the four directions of motion. Although the input circuitry of the directionally selective neurons has been studied in detail, the synaptic connectivity of circuits integrating T4/T5 motion signals is largely unknown. Here, we report a 3D electron microscopy reconstruction, wherein we comprehensively identified T4/T5's synaptic partners in the lobula plate, revealing a diverse set of new cell types and attributing new connectivity patterns to the known cell types. Our reconstruction explains how the ON- and OFF-motion pathways converge. T4 and T5 cells that project to the same layer connect to common synaptic partners and comprise a core motif together with bilayer interneurons, detailing the circuit basis for computing motion opponency. We discovered pathways that likely encode new directions of motion by integrating vertical and horizontal motion signals from upstream T4/T5 neurons. Finally, we identify substantial projections into the lobula, extending the known motion pathways and suggesting that directionally selective signals shape feature detection there. The circuits we describe enrich the anatomical basis for experimental and computations analyses of motion vision and bring us closer to understanding complete sensory-motor pathways.

Our brains are capable of remarkably stable stimulus representations despite time-varying neural activity. For instance, during delay periods in working memory tasks, while stimuli are represented in working memory, neurons in the prefrontal cortex, thought to support the memory representation, exhibit time-varying neuronal activity. Since neuronal activity encodes the stimulus, its time-varying dynamics appears to be paradoxical and incompatible with stable network stimulus representations. Indeed, this finding raises a fundamental question: can stable representations only be encoded with stable neural activity, or, its corollary, is every change in activity a sign of change in stimulus representation?

The courtship song of the Drosophila male serves as a genetically tractable model for the investigation of the neural mechanisms of decision-making, action selection, and motor pattern generation. Singing has been causally linked to the activity of the set of neurons that express the sex-specific fru transcripts, but the specific neurons involved have not been identified. Here we identify five distinct classes of fru neuron that trigger or compose the song. Our data suggest that P1 and pIP10 neurons in the brain mediate the decision to sing, and to act upon this decision, while the thoracic neurons dPR1, vPR6, and vMS11 are components of a central pattern generator that times and shapes the song’s pulses. These neurons are potentially connected in a functional circuit, with the descending pIP10 neuron linking the brain and thoracic song centers. Sexual dimorphisms in each of these neurons may explain why only males sing.

Most land animals normally walk forward but switch to backward walking upon sensing an obstacle or danger in the path ahead. A change in walking direction is likely to be triggered by descending "command" neurons from the brain that act upon local motor circuits to alter the timing of leg muscle activation. Here we identify descending neurons for backward walking in Drosophila--the MDN neurons. MDN activity is required for flies to walk backward when they encounter an impassable barrier and is sufficient to trigger backward walking under conditions in which flies would otherwise walk forward. We also identify ascending neurons, MAN, that promote persistent backward walking, possibly by inhibiting forward walking. These findings provide an initial glimpse into the circuits and logic that control walking direction in Drosophila.

By using a combination of dye injections, clonal labeling, and molecular markers, we have reconstructed the axonal connections between brain and ventral nerve cord of the first-instar Drosophila larva. Out of the approximately 1,400 neurons that form the early larval brain hemisphere, less than 50 cells have axons descending into the ventral nerve cord. Descending neurons fall into four topologically defined clusters located in the anteromedial, anterolateral, dorsal, and basoposterior brain, respectively. The anterolateral cluster represents a lineage derived from a single neuroblast. Terminations of descending neurons are almost exclusively found in the anterior part of the ventral nerve cord, represented by the gnathal and thoracic neuromeres. This region also contains small numbers of neurons with axons ascending into the brain. Terminals of the ascending axons are found in the same basal brain regions that also contain descending neurons. We have mapped ascending and descending axons to the previously described scaffold of longitudinal fiber tracts that interconnect different neuromeres of the ventral nerve cord and the brain. This work provides a structural framework for functional and genetic studies addressing the control of Drosophila larval behavior by brain circuits.

It is often assumed that highly-branched neuronal structures perform compartmentalized computations. However, previously we showed that the Gastric Mill (GM) neuron in the crustacean stomatogastric ganglion (STG) operates like a single electrotonic compartment, despite having thousands of branch points and total cable length >10 mm (Otopalik et al., 2017a; 2017b). Here we show that compact electrotonic architecture is generalizable to other STG neuron types, and that these neurons present direction-insensitive, linear voltage integration, suggesting they pool synaptic inputs across their neuronal structures. We also show, using simulations of 720 cable models spanning a broad range of geometries and passive properties, that compact electrotonus, linear integration, and directional insensitivity in STG neurons arise from their neurite geometries (diameters tapering from 10-20 µm to \uline< 2 µm at their terminal tips). A broad parameter search reveals multiple morphological and biophysical solutions for achieving different degrees of passive electrotonic decrement and computational strategies in the absence of active properties.

Animals consolidate some, but not all, learning experiences into long-term memory. Across the animal kingdom, sleep has been found to have a beneficial effect on the consolidation of recently formed memories into long-term storage. However, the underlying mechanisms of sleep dependent memory consolidation are poorly understood. Here, we show that consolidation of courtship long-term memory in is mediated by reactivation during sleep of dopaminergic neurons that were earlier involved in memory acquisition. We identify specific fan-shaped body neurons that induce sleep after the learning experience and activate dopaminergic neurons for memory consolidation. Thus, we provide a direct link between sleep, neuronal reactivation of dopaminergic neurons, and memory consolidation.

The mouse has become an important model for understanding the neural basis of visual perception. Although it has long been known that mouse lens transmits ultraviolet (UV) light and mouse opsins have absorption in the UV band, little is known about how UV visual information is processed in the mouse brain. Using a custom UV stimulation system and in vivo calcium imaging, we characterized the feature selectivity of layer 2/3 neurons in mouse primary visual cortex (V1). In adult mice, a comparable percentage of the neuronal population responds to UV and visible stimuli, with similar pattern selectivity and receptive field properties. In young mice, the orientation selectivity for UV stimuli increased steadily during development, but not direction selectivity. Our results suggest that, by expanding the spectral window through which the mouse can acquire visual information, UV sensitivity provides an important component for mouse vision.