Memories are assumed to be formed by sets of synapses changing their structural or functional performance. The efficacy of forming new memories declines with advancing age, but the synaptic changes underlying age-induced memory impairment remain poorly understood. Recently, we found spermidine feeding to specifically suppress age-dependent impairments in forming olfactory memories, providing a mean to search for synaptic changes involved in age-dependent memory impairment. Here, we show that a specific synaptic compartment, the presynaptic active zone (AZ), increases the size of its ultrastructural elaboration and releases significantly more synaptic vesicles with advancing age. These age-induced AZ changes, however, were fully suppressed by spermidine feeding. A genetically enforced enlargement of AZ scaffolds (four gene-copies of BRP) impaired memory formation in young animals. Thus, in the Drosophila nervous system, aging AZs seem to steer towards the upper limit of their operational range, limiting synaptic plasticity and contributing to impairment of memory formation. Spermidine feeding suppresses age-dependent memory impairment by counteracting these age-dependent changes directly at the synapse.

Graphical Abstract Highlights d Drosophila R5 network exhibits sleep-regulating compound slow-wave oscillations d Activation of circadian pathways mediates R5 multi-unit synchronization d Synchronization and compound delta oscillations require NMDAR coincidence detection d Eliminating NMDAR coincidence detection in R5 disrupts sleep In Brief Raccuglia et al. discover sleep-regulatory compound delta oscillations within the Drosophila R5 network. NMDAR coincidence detection mediates singleunit synchronization, which is the mechanistic basis for generating compound delta oscillations. Eliminating NMDAR coincidence detection, and thus compound oscillations, disrupts sleep and facilitates wakening. SUMMARYSlow-wave rhythms characteristic of deep sleep oscillate in the delta band (0.5-4 Hz) and can be found across various brain regions in vertebrates. Across phyla, however, an understanding of the mechanisms underlying oscillations and how these link to behavior remains limited. Here, we discover compound delta oscillations in the sleep-regulating R5 network of Drosophila. We find that the power of these slowwave oscillations increases with sleep need and is subject to diurnal variation. Optical multi-unit voltage recordings reveal that single R5 neurons get synchronized by activating circadian input pathways. We show that this synchronization depends on NMDA receptor (NMDAR) coincidence detector function, and that an interplay of cholinergic and glutamatergic inputs regulates oscillatory frequency. Genetically targeting the coincidence detector function of NMDARs in R5, and thus the uncovered mechanism underlying synchronization, abolished network-specific compound slow-wave oscillations. It also disrupted sleep and facilitated light-induced wakening, establishing a role for slow-wave oscillations in regulating sleep and sensory gating. We therefore propose that the synchronization-based increase in oscillatory power likely represents an evolutionarily conserved, potentially ''optimal,'' strategy for constructing sleep-regulating sensory gates.

High-throughput electron microscopy has started to reveal synaptic connectivity maps of single circuits and whole brain regions, for example, in the Drosophila olfactory system. However, efficacy, timing, and frequency tuning of synaptic vesicle release are also highly diversified across brain synapses. These features critically depend on the nanometer-scale coupling distance between voltage-gated Ca channels (VGCCs) and the synaptic vesicle release machinery. Combining light super resolution microscopy with in vivo electrophysiology, we show here that two orthogonal scaffold proteins (ELKS family Bruchpilot, BRP, and Syd-1) cluster-specific (M)Unc13 release factor isoforms either close (BRP/Unc13A) or further away (Syd-1/Unc13B) from VGCCs across synapses of the Drosophila olfactory system, resulting in different synapse-characteristic forms of short-term plasticity. Moreover, BRP/Unc13A versus Syd-1/Unc13B ratios were different between synapse types. Thus, variation in tightly versus loosely coupled scaffold protein/(M)Unc13 modules can tune synapse-type-specific release features, and "nanoscopic molecular fingerprints" might identify synapses with specific temporal features.

Slow-wave rhythms characteristic of deep sleep oscillate in the delta band (0.5 -4 Hz) andcan be found across various brain regions in vertebrates. Across systems it is however unclear how oscillations arise and whether they are the causal functional unit steering behavior. Here, for the first time in any invertebrate, we discover sleep-relevant delta oscillations in Drosophila.We find that slow-wave oscillations in the sleep-regulating R2 network increase with sleep need. Optical multi-unit voltage recordings reveal that single R2 neurons get synchronized by sensory and circadian input pathways. We show that this synchronization depends on NMDA receptor (NMDARs) coincidence detector function and on an interplay of cholinergic and glutamatergic inputs setting a resonance frequency. Genetically targeting the coincidence detector function of NMDARs in R2, and thus the uncovered mechanism underlying synchronization, abolished network-specific slow-wave oscillations. It also disrupted sleep and facilitated light-induced wakening, directly establishing a causal role for slow-wave oscillations in regulating sleep and sensory gating. We therefore propose that the synchronization-based increase in oscillatory power likely represents an evolutionarily conserved, potentially 'optimal', strategy for constructing sleep-regulating sensory gates. that these delta oscillations depend on multi-unit synchronization mediated through NMDA receptor (NMDAR) coincidence detection. Disrupting this synchronization and thus the emergence of compound delta oscillations disrupts sleep and alters sensory gating during sleep. We thus identify slow-wave oscillations as an electrophysiological correlate for sleep regulation in invertebrates and place these oscillatory patterns at the basis of behavior. The sleep-regulating oscillations are comparable to sleep-regulating thalamic oscillations 20-22 as well as network-specific oscillations observed during sleep deprivation in vertebrates (local sleep) 2,23,24 . Our work demonstrates that slow-wave oscillations and sleep are fundamentally interconnected across systems, potentially representing an evolutionarily conserved strategy for network mechanisms regulating internal states and sleep. Results Sleep deprivation increases network-driven delta oscillations in the sleep-regulating R2 networkExamples of rhythmic activity patterns have previously been reported in insects 9,10 .However, their source, function and interdependence with internal states (such as sleep drive), remain largely unclear. We targeted expression of the GEVI ArcLight specifically to R2 neurons in the Drosophila brain. This defined network of 10 cells per hemisphere (Fig. 1a) resides within the ellipsoid body and is involved in sleep regulation [14][15][16] and multi-sensory relay [17][18][19] .In vivo recordings of the dendritic processes (bulb) of R2 neurons (Fig. 1a) identified electrical compound activity (Fig. 1b) that oscillated at delta-band frequencies between 0.5-1.5 Hz (Fig. 1b, d) in rested flies. We sleep-deprived f...

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21Understanding how neural networks generate activity patterns and communicate with each 22 other requires monitoring the electrical activity from many neurons simultaneously. Perfectly 23 suited tools for addressing this challenge are genetically encoded voltage indicators (GEVIs) 24 because they can be targeted to specific cell types and optically report the electrical activity 25 of individual, or populations of neurons. However, analyzing and interpreting the data from 26 voltage imaging experiments is challenging because high recording speeds and properties of 27 current GEVIs yield only low signal-to-noise ratios, making it necessary to apply specific 28 analytical tools. Here, we present NOSA (Neuro-Optical Signal Analysis), a novel open 29 source software designed for analyzing voltage imaging data and identifying temporal 30 interactions between electrical activity patterns of different origin. 31In this manuscript we explain the challenges that arise during voltage imaging 32 experiments and provide hands-on analytical solutions. We demonstrate how NOSA's 33 baseline fitting, filtering algorithms and movement correction can compensate for shifts in 34 baseline fluorescence and extract electrical patterns from low signal-to-noise recordings. 35 Moreover, NOSA contains powerful features to identify oscillatory frequencies in electrical 36 patterns and extract neuronal firing characteristics. NOSA is the first open-access software to 37 provide an option for analyzing simultaneously recorded optical and electrical data derived 38 from patch-clamp or other electrode-based recordings. To identify temporal relations 39 between electrical activity patterns we implemented different options to perform cross 40 correlation analysis, demonstrating their utility during voltage imaging in Drosophila and 41mice. All features combined, NOSA will facilitate the first steps into using GEVIs and help to 42 realize their full potential for revealing cell-type specific connectivity and functional 43interactions. If you would like to test NOSA, please send an email to the lead contact. 44 45 3

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