Understanding learning through synaptic plasticity rules in the brain is a grand challenge for neuroscience. Here we introduce a novel computational framework for inferring plasticity rules from experimental data on neural activity trajectories and behavioral learning dynamics. Our methodology parameterizes the plasticity function to provide theoretical interpretability and facilitate gradient-based optimization. For instance, we use Taylor series expansions or multilayer perceptrons to approximate plasticity rules, and we adjust their parameters via gradient descent over entire trajectories to closely match observed neural activity and behavioral data. Notably, our approach can learn intricate rules that induce long nonlinear time-dependencies, such as those incorporating postsynaptic activity and current synaptic weights. We validate our method through simulations, accurately recovering established rules, like Oja’s, as well as more complex hypothetical rules incorporating reward-modulated terms. We assess the resilience of our technique to noise and, as a tangible application, apply it to behavioral data from Drosophila during a probabilistic reward-learning experiment. Remarkably, we identify an active forgetting component of reward learning in flies that enhances the predictive accuracy of previous models. Overall, our modeling framework provides an exciting new avenue to elucidate the computational principles governing synaptic plasticity and learning in the brain.

Primary aldosteronism (PA) is the most frequent form of secondary hypertension. Over the past two decades, major advances have been made in our understanding of the disease with the identification of germline or somatic mutations in ion channels and pumps. These mutations enhance calcium signaling, the main trigger of aldosterone biosynthesis.

Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. New advances in high density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal. Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons. Here we propose a neuron tracking method that can identify the same cells independent of firing statistics, which are used by most existing methods. Our method is based on between-day non-rigid alignment of spike sorted clusters. We verified the same cell identify using measured visual receptive fields. This method succeeds on datasets separated from one to 47 days, with an 86% average recovery rate.

The central nucleus of the amygdala (CEA) is a brain region that integrates external and internal sensory information and executes innate and adaptive behaviors through distinct output pathways. Despite its complex functions, the diversity of molecularly defined neuronal types in the CEA and their contributions to major axonal projection targets have not been examined systematically. Here, we performed single-cell RNA-sequencing (scRNA-Seq) to classify molecularly defined cell types in the CEA and identified marker-genes to map the location of these neuronal types using expansion assisted iterative fluorescence in situ hybridization (EASI-FISH). We developed new methods to integrate EASI-FISH with 5-plex retrograde axonal labeling to determine the spatial, morphological, and connectivity properties of ∼30,000 molecularly defined CEA neurons. Our study revealed spatio-molecular organization of the CEA, with medial and lateral CEA associated with distinct cell families. We also found a long-range axon projection network from the CEA, where target regions receive inputs from multiple molecularly defined cell types. Axon collateralization was found primarily among projections to hindbrain targets, which are distinct from forebrain projections. This resource reports marker-gene combinations for molecularly defined cell types and axon-projection types, which will be useful for selective interrogation of these neuronal populations to study their contributions to the diverse functions of the CEA.

Methods of three-dimensional electron microscopy have been actively developed recently and open up great opportunities for morphological work. This approach is especially useful for studying microinsects, since it is possible to obtain complete series of high-resolution sections of a whole insect. Studies on the genus Megaphragma are especially important, since the unique phenomenon of lysis of most of the neuron nuclei was discovered in species of this genus. In this study we reveal the anatomical structure of the head of Megaphragma viggianii at all levels from organs to subcellular structures. Despite the miniature size of the body, most of the organ systems of M. viggianii retain the structural plan and complexity of organization at all levels. The set of muscles and the well-developed stomatogastric nervous system of this species correspond to those of larger insects, and there is also a well-developed tracheal system in the head of this species. Reconstructions of the head of M. viggianii at the cellular and subcellular levels were obtained, and of volumetric data were analyzed. A total of 689 nucleated cells of the head were reconstructed. The ultrastructure of M. viggianii is surprisingly complex, and the evolutionary benefits of such complexity are probably among the factors limiting the further miniaturization of parasitoid wasps.

A primary cilium is a thin membrane-bound extension off a cell surface that contains receptors for perceiving and transmitting signals that modulate cell state and activity. While many cell types have a primary cilium, little is known about primary cilia in the brain, where they are less accessible than cilia on cultured cells or epithelial tissues and protrude from cell bodies into a deep, dense network of glial and neuronal processes. Here, we investigated cilia frequency, internal structure, shape, and position in large, high-resolution transmission electron microscopy volumes of mouse primary visual cortex. Cilia extended from the cell bodies of nearly all excitatory and inhibitory neurons, astrocytes, and oligodendrocyte precursor cells (OPCs), but were absent from oligodendrocytes and microglia. Structural comparisons revealed that the membrane structure at the base of the cilium and the microtubule organization differed between neurons and glia. OPC cilia were distinct in that they were the shortest and contained pervasive internal vesicles only occasionally observed in neuron and astrocyte cilia. Investigating cilia-proximal features revealed that many cilia were directly adjacent to synapses, suggesting cilia are well poised to encounter locally released signaling molecules. The internal anatomy, including microtubule changes and centriole location, defined key structural features including cilium placement and shape. Together, the anatomical insights both within and around neuron and glia cilia provide new insights into cilia formation and function across cell types in the brain.

Retinal Müller glia function as injury-induced stem-like cells in zebrafish but not mammals. However, insights gleaned from zebrafish have been applied to stimulate nascent regenerative responses in the mammalian retina. For instance, microglia/macrophages regulate Müller glia stem cell activity in the chick, zebrafish, and mouse. We previously showed that post-injury immunosuppression by the glucocorticoid dexamethasone accelerated retinal regeneration kinetics in zebrafish. Similarly, microglia ablation enhances regenerative outcomes in the mouse retina. Targeted immunomodulation of microglia reactivity may therefore enhance the regenerative potential of Müller glia for therapeutic purposes. Here, we investigated potential mechanisms by which post-injury dexamethasone accelerates retinal regeneration kinetics, and the effects of dendrimer-based targeting of dexamethasone to reactive microglia. Intravital time-lapse imaging revealed that post-injury dexamethasone inhibited microglia reactivity. The dendrimer-conjugated formulation: (1) decreased dexamethasone-associated systemic toxicity, (2) targeted dexamethasone to reactive microglia, and (3) improved the regeneration enhancing effects of immunosuppression by increasing stem/progenitor proliferation rates. Lastly, we show that the gene rnf2 is required for the enhanced regeneration effect of D-Dex. These data support the use of dendrimer-based targeting of reactive immune cells to reduce toxicity and enhance the regeneration promoting effects of immunosuppressants in the retina.

The endoplasmic reticulum (ER) and the Golgi apparatus are the first sorting stations along the secretory pathway of mammalian cells and have a crucial role in protein quality control and cellular homeostasis. While machinery components mediating ER-to-Golgi transport have been mapped, it is unclear how exchange between the two closely juxtaposed organelles is coordinated in living cells. Here, using gene editing to tag machinery components, live-cell confocal and stimulated emission depletion (STED) super-resolution microscopy, we show that ER-to-Golgi transport occurs via a dynamic network of tubules positive for the small GTPase ARF4. swCOPI machinery is tightly associated to this network and moves with tubular-vesicular structures. Strikingly, the ARF4 network appears to be continuous with the ER and ARF4 tubules remodel around static ER exit sites (ERES) defined by COPII machinery. We were further able to dissect the steps of ER-to-Golgi transport with functional trafficking assays. A wave of cargo released from the ER percolates through peripheral and Golgi-tethered ARF4 structures before filling the cis-Golgi. Perturbation via acute degradation of ARF4 shows an active regulatory role for the GTPase and COPI in anterograde transport. Our data supports a model in which anterograde ER-to-Golgi transport occurs via an ARF4 tubular-vesicular network directly connecting the ER and Golgi-associated pre-cisternae.

Animal brains are complex organs composed of thousands of interconnected neurons. Characterizing the network properties of these brains is a requisite step towards understanding mechanisms of computation and information flow. With the completion of the Flywire project, we now have access to the connectome of a complete adult Drosophila brain, containing 130,000 neurons and millions of connections. Here, we present a statistical summary and data products of the Flywire connectome, delving into its network properties and topological features. To gain insights into local connectivity, we computed the prevalence of two- and three-node network motifs, examined their strengths and neurotransmitter compositions, and compared these topological metrics with wiring diagrams of other animals. We uncovered a population of highly connected neurons known as the “rich club” and identified subsets of neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions. The freely available data and neuron populations presented here will serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

How memories are used by the brain to guide future action is poorly understood. In olfactory associative learning in Drosophila, multiple compartments of the mushroom body act in parallel to assign valence to a stimulus. Here, we show that appetitive memories stored in different compartments induce different levels of upwind locomotion. Using a photoactivation screen of a new collection of split-GAL4 drivers and EM connectomics, we identified a cluster of neurons postsynaptic to the mushroom body output neurons (MBONs) that can trigger robust upwind steering. These UpWind Neurons (UpWiNs) integrate inhibitory and excitatory synaptic inputs from MBONs of appetitive and aversive memory compartments, respectively. After training, disinhibition from the appetitive-memory MBONs enhances the response of UpWiNs to reward-predicting odors. Blocking UpWiNs impaired appetitive memory and reduced upwind locomotion during retrieval. Photoactivation of UpWiNs also increased the chance of returning to a location where activation was initiated, suggesting an additional role in olfactory navigation. Thus, our results provide insight into how learned abstract valences are gradually transformed into concrete memory-driven actions through divergent and convergent networks, a neuronal architecture that is commonly found in the vertebrate and invertebrate brains.