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.

All brain functions in animals rely upon neuronal connectivity that is established during early development. Although the activity-dependent mechanisms are deemed important for brain development and adult synaptic plasticity, the precise cellular and molecular mechanisms remain however, largely unknown. This lack of fundamental knowledge regarding developmental neuronal assembly owes its existence to the complexity of the mammalian brain as cell-cell interactions between individual neurons cannot be investigated directly. Here, we used individually identified synaptic partners from Lymnaea stagnalis to interrogate the role of neuronal activity patterns over an extended time period during various growth time points and synaptogenesis. Using intracellular recordings, microelectrode arrays, and time-lapse imaging, we identified unique patterns of activity throughout neurite outgrowth and synapse formation. Perturbation of voltage-gated Ca channels compromised neuronal growth patterns which also invoked a protein kinase A mediated pathway. Our findings underscore the importance of unique activity patterns in regulating neuronal growth, neurite branching, and synapse formation, and identify the underlying cellular and molecular mechanisms.

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.

We explore the hypothesis that the neuronal spike generation mechanism is an analog-to-digital converter, which rectifies low-pass filtered summed synaptic currents and encodes them into spike trains linearly decodable in post-synaptic neurons. To digitally encode an analog current waveform, the sampling rate of the spike generation mechanism must exceed its Nyquist rate. Such oversampling is consistent with the experimental observation that the precision of the spike-generation mechanism is an order of magnitude greater than the cut-off frequency of dendritic low-pass filtering. To achieve additional reduction in the error of analog-to-digital conversion, electrical engineers rely on noise-shaping. If noise-shaping were used in neurons, it would introduce correlations in spike timing to reduce low-frequency (up to Nyquist) transmission error at the cost of high-frequency one (from Nyquist to sampling rate). Using experimental data from three different classes of neurons, we demonstrate that biological neurons utilize noise-shaping. We also argue that rectification by the spike-generation mechanism may improve energy efficiency and carry out de-noising. Finally, the zoo of ion channels in neurons may be viewed as a set of predictors, various subsets of which are activated depending on the statistics of the input current.