Biology subjects

Rivlin, P.

Publications and source records attributed to Rivlin, P..

8 recordsLinked to original sources

bioRxiv2026

Adult Clock Neuron Somatic Neuropeptide Release and Cytonemes Regulate Sleep

Drosophila s-LNv clock neurons promote nighttime sleep by releasing the neuropeptide sNPF to activate sNPF receptors (sNPF-Rs) on l-LNv clock neurons. Behavior is controlled by synaptic transmission, but s-LNv and l-LNv neurons are not connected directly by chemical synapses. To investigate the basis of sNPF/sNPF-R communication between LNv neurons, the spread of sNPF was imaged in the adult brain. We report the daily midmorning burst of sNPF released from s-LNv terminals does not reach l-LNv neurons or s-LNv somata. Instead, sNPF released by the s-LNv soma late at night in response to sleep-promoting IP3 signaling reaches l-LNv somata, but not their terminals. In addition to communication by neuropeptide diffusion, analysis of fly connectomes revealed that adult s-LNv and l-LNv neurons form non-synaptic direct contacts mediated by cytonemes. Remarkably, genetically perturbing LNv neuron cytonemes alters sleep latency, but not nighttime sleep, the target of somatic sNPF release, or circadian behavior, which depends on PDF neuropeptide released by LNv terminals. Therefore, three distinct aspects of adult rhythmic behavior are produced by terminals, the soma and cytonemes, with the latter possibly acting via contacts that are not currently annotated in the connectome.

neuroscience↗

bioRxiv2026

Connectome quality converges predictably to reveal optimal stopping points during proofreading

Volumetric electron microscopy (EM) has become a critical approach to generating high-resolution reconstructions of brain tissue. As the size of EM volumes increase, use of automated image segmentation within the reconstruction pipeline has become essential, although it generates errors that need correction. The proofreading and correcting of these errors has since become the dominant cost driver in the pipeline, but precisely estimating the sufficient number of proofreading edits to enable meaningful scientific analyses of the reconstructed neuronal networks remains a challenge. We present a fast, computationally inexpensive way to estimate the progress of a connectomic proofreading effort without requiring a priori knowledge of ground truth. We show that simple global graph invariants converge predictably to asymptotic limits with increasing numbers of proofreading edits, informing a quantitative "pencils down" criterion for proofreading completeness. We illustrate our method on two datasets in different stages of proofreading progress, a zebrafish spinal cord and the hemibrain Drosophila melanogaster dataset. Our method reduces the uncertainty associated with the planning and prioritization of proofreading activities and enables data owners to accurately predict and budget the amount of proofreading necessary for their scientific questions.

neuroscience↗

bioRxiv2026

Physical contact reveals a hidden layer of cortical architecture

Neurons interact at synapses, but they also communicate through physical contact and proximity, including diffusion, glia-mediated interactions, and ephaptic coupling. Standard connectomes map synapses, but cannot capture the full set of cell-cell contacts that can support these pathways. Here we extract contactomes from two large mouse visual cortex volumes at nanoscale resolution and quantify every cell-cell contact, the shared surface area of each contact, and the relationship between contact and synaptic connectivity. We find that contactomes are 5 - 10x denser than synaptic graphs, revealing that neurons physically contact a much larger set of potential neighbors than they synaptically connect to. We further find that most nearby potential neighbors are already in physical contact, indicating that local structural change would add few new candidate synaptic partners. Finally, we find that astrocytes form a single large syncytium-like network that spans the tissue and directly contacts nearly all neurons, and that glial processes lie within a micron or two of almost every synapse, indicating that synapses reside within a pervasive glia-shaped microenvironment. Together, these results show that physical contact forms a distinct layer of brain architecture that extends far beyond the synaptic connectome.

neuroscience↗

bioRxiv2024

Connectome-driven neural inventory of a complete visual system

Vision provides animals with detailed information about their surroundings, conveying diverse features such as color, form, and movement across the visual scene. Computing these parallel spatial features requires a large and diverse network of neurons, such that in animals as distant as flies and humans, visual regions comprise half the brains volume. These visual brain regions often reveal remarkable structure-function relationships, with neurons organized along spatial maps with shapes that directly relate to their roles in visual processing. To unravel the stunning diversity of a complex visual system, a careful mapping of the neural architecture matched to tools for targeted exploration of that circuitry is essential. Here, we report a new connectome of the right optic lobe from a male Drosophila central nervous system FIB-SEM volume and a comprehensive inventory of the flys visual neurons. We developed a computational framework to quantify the anatomy of visual neurons, establishing a basis for interpreting how their shapes relate to spatial vision. By integrating this analysis with connectivity information, neurotransmitter identity, and expert curation, we classified the [~]53,000 neurons into 727 types, about half of which are systematically described and named for the first time. Finally, we share an extensive collection of split-GAL4 lines matched to our neuron type catalog. Together, this comprehensive set of tools and data unlock new possibilities for systematic investigations of vision in Drosophila, a foundation for a deeper understanding of sensory processing.

neuroscience↗

bioRxiv2024

Recurrent connections enable point attractor dynamics and dimensionality reduction in a connectome-constrained model of the insect learning center

The learning center in the insect, the mushroom body (MB) with its predominant population of Kenyon Cells (KCs), is a widely studied model system to investigate neural processing principles, both experimentally and theoretically. While many computational models of the MB have been studied, the computational role of recurrent connectivity between KCs remains inadequately understood. Dynamical point attractors are a candidate theoretical framework where recurrent connections in a neural network can enable a discrete set of stable activation patterns. However, given that detailed, full recurrent connectivity patterns in biological neuron populations are mostly unknown, how theoretical models are substantiated by specific networks found in biology has not been clear. Leveraging the recent release of the full synapse-level connectivity of the MB in the fly, we performed a series of analyses and network model simulations to investigate the computational role of the recurrent KC connections, especially their significance in attractor dynamics. Structurally, the recurrent excitation (RE) connections are highly symmetric and balanced with feedforward input. In simulations, RE facilitates dimensionality reduction and allows a small set of self-sustaining point attractor states to emerge. To further quantify the possible range of network properties mediated by RE, we systematically explored the dynamical regimes enabled by changing recurrent connectivity strength. Finally, we establish connections between our findings and potential functional or behavioral implications. Overall, our work provides quantitative insights into the possible functional role of the recurrent excitatory connections in the MB by quantifying the point attractor network dynamics within a full synapse-level connectome-constrained highly recurrent network model. These findings advance our understanding of how biological neural networks may utilize point attractor dynamics. Author summaryPoint attractor neural networks are widely used theoretical models of associative memory, where recurrent connections between neurons enable a discrete set of stable activation patterns that can recover a full pattern based on partial cues. The detailed full recurrent connectivity patterns in biological neuron populations are largely unknown, however, raising questions about the precise correspondence between theoretical point attractor models and neural networks found in biology. Recent breakthroughs have unveiled the synapse-level connectivity of all neurons within the learning center of an insect, including recurrent connections between the primary neuron type--a crucial component with an elusive computational role. In this work, we perform analyses of these recurrent connectivity patterns and simulate neural network models that have these biologically constrained neural network patterns. We find that these recurrent connections are highly symmetric and balanced with input to the memory center. In simulations, we find that these recurrent connections perform dimensionality reduction and enable a small set of point attractor states. We additionally characterize how the strength of these recurrent connections affects network properties and downstream behavioral consequences. Overall, this work advances an understanding of the insect learning center as well as the relationship between theoretical and biological recurrent networks.

neuroscience↗

bioRxiv2023

A Novel Semi-automated Proofreading and Mesh Error Detection Pipeline for Neuron Extension

AO_SCPLOWBSTRACTC_SCPLOWThe immense scale and complexity of neuronal electron microscopy (EM) datasets pose significant challenges in data processing, validation, and interpretation, necessitating the development of efficient, automated, and scalable error-detection methodologies. This paper proposes a novel approach that employs mesh processing techniques to identify potential error locations near neuronal tips. Error detection at tips is a particularly important challenge since these errors usually indicate that many synapses are falsely split from their parent neuron, injuring the integrity of the connectomic reconstruction. Additionally, we draw implications and results from an implementation of this error detection in a semi-automated proofreading pipeline. Manual proofreading is a laborious, costly, and currently necessary method for identifying the errors in the machine learning based segmentation of neural tissue. This approach streamlines the process of proofreading by systematically highlighting areas likely to contain inaccuracies and guiding proofreaders towards potential continuations, accelerating the rate at which errors are corrected.

neuroscience↗

bioRxiv2019

En bloc preparation of Drosophila brains enables high-throughput FIB-SEM connectomics

Deriving the detailed synaptic connections of the entire nervous system has been a long term but unrealized goal of the nascent field of connectomics. For Drosophila, in particular, three sample preparation problems must be solved before the requisite imaging and analysis can even begin. The first is dissecting the brain, connectives, and ventral nerve cord (roughly comparable to the brain, neck, and spinal cord of vertebrates) as a single contiguous unit. Second is fixing and staining the resulting specimen, too large for previous techniques such as flash freezing, so as to permit the necessary automated segmentation of neuron membranes. Finally the contrast must be sufficient to support synapse detection at imaging speeds that enable the entire connectome to be collected. To address these issues, we report three major novel methods to dissect, fix, dehydrate and stain this tiny but complex nervous system in its entirety; together they enable us to uncover a Focused Ion-Beam Scanning Electron Microscopy (FIB-SEM) connectome of the entire Drosophila brain. They reliably recover fixed neurons as round profiles with darkly stained synapses, suitable for machine segmentation and automatic synapse detection, for which only minimal human intervention is required. Our advanced procedures use: a custom-made jig to microdissect both regions of the central nervous system, dorsal and ventral, with their connectives; fixation and Durcupan embedment, followed by a special hot-knife slicing protocol to reduce the brain to dimensions suited to FIB; contrast enhancement by heavy metals; together with a progressive lowering of temperature protocol for dehydration. Collectively these optimize the brains morphological preservation, imaging it at a usual resolution of 8nm per voxel while simultaneously speeding the formerly slow rate of FIB-SEM. With these methods we could recently obtain a FIB-SEM image stack of the Drosophila brain eight times faster than hitherto, at approximately the same rate as, but without the requirement to cut, nor imperfections in, EM serial sections.

neuroscience↗

bioRxiv2017

Stochastic Protein Labeling Enables Long-term Single Molecule Observation In Vivo

Our ability to unambiguously image and track individual molecules in live cells is limited by packing of multiple copies of labeled molecules within the resolution limit. Here we devise a universal genetic strategy to precisely control copy number of fluorescently labeled molecules in a cell. This system has a dynamic titration range of >10,000 fold, enabling sparse labeling of proteins expressed at different abundance levels. Combined with photostable labels, this system extends the duration of automated single-molecule tracking by 2 orders of magnitude. We demonstrate long-term imaging of synaptic vesicle dynamics in cultured neurons as well as in intact zebrafish. We found axon initial segment utilizes a waterfall mechanism gating synaptic vesicle transport polarity by promoting anterograde transport processivity. Long-time observation also reveals that transcription factor hops between clustered binding sites in spatially-restricted sub-nuclear regions, suggesting that topological structures in the nucleus shape local gene activities by a sequestering mechanism. This strategy thus greatly expands the spatiotemporal length scales of live-cell single-molecule measurements, enabling new experiments to quantitatively understand complex control of molecular dynamics in vivo.

biophysics↗