Abstract For the brain to compute, electrical signals must propagate over the membranes of individual neurons, connecting synaptic inputs to synaptic outputs 1 . Complex neuronal morphologies coupled with the spatial organization of synaptic inputs and outputs enable diverse voltage transformations that underlie cell-type specific computations 2,3 . However, measuring these transformations in vivo has remained challenging, leaving a crucial gap in our mechanistic understanding of single neuron computation. Here, we develop ASAP7y, a genetically encoded voltage indicator with unprecedented subthreshold sensitivity and expanded excitation compatibility in both mice and flies. We leveraged ASAP7y combined with two-photon random-access microscopy to record sensory stimulus-evoked voltage dynamics with millisecond, subcellular, and subthreshold resolution along the neurites of individual neurons in Drosophila . We found remarkable heterogeneity in voltage propagation across cell-types, delineating a fundamental axis of electrical diversity. Leveraging a nanoscale EM reconstruction of the visual system 4 , we modeled the electrotonic properties of single neurons spanning 717 cell types, revealing how morphology shapes voltage transformations. Finally, we demonstrate that confined voltage propagation creates substrates for local computation, producing subcellular domains with distinct feature selectivity across multiple cell types. These results provide mechanistic insight into how critical single neuron computations arise and reveal parallel processing in single neurons.

Vesicles are critical components of neurons that package neurotransmitters and neuropeptides for their release, in order to communicate with other neurons and cells. However, due to their small size, the reconstruction of the full vesicle endowment across an entire neuronal morphology remains challenging. To achieve this, we have used, as a tool to identify and visualize vesicles, Volume Electron Microscopy (vEM), a method that has the nanoscale resolution to detect individual vesicle boundaries, content, and 3D locations. However, the large volume of vEM datasets poses a challenge in the segmentation, classification, and spatial analysis of tens of thousands of vesicles and their target cell in 3D. Here we report the development of VesiclePy, an integrated pipeline for automated segmentation, classification, proofreading, and spatial analysis of vesicles, relative to neuron masks in large-volume electron microscopy data. Our package integrates the efficiency of deep learning and the accuracy of human proofreading and provides a streamlined package in chunked processing and accurate indexing, localization, and visualization of single vesicle resolution in large vEM data. We demonstrate the viability of VesiclePy using high-pressure frozen serial EM data of Hydra vulgaris and quantify the performance of the package using ground truth manual annotations. We show that VesiclePy can process a multiterabyte serial EM dataset, efficiently annotate 53,851 vesicles from 20 complete neurons, and classify vesicles into 5 types. Each vesicle has a unique ID and 3D location for further spatial analysis in relation to neuron or non-neuronal targets nearby. Finally, by combining vesicle data and morphological information of each neuron, we can quantitatively cluster neurons into subtypes. VesiclePy is available at https://github.com/PytorchConnectomics/VesiclePy under an MIT license.

Neuroscience has long emphasized synaptic transmission and physical wiring as the substrate of brain function and behavior. However, an additional layer of connectivity - a "chemical connectome" formed by neuropeptide-GPCR signaling - has been increasingly recognized in animals such as C. elegans, Drosophila, and the cnidarian Nematostella vectensis. To further explore neuropeptide networks in basal metazoans, we analyzed the genome and transcriptome of the freshwater cnidarian Hydra vulgaris. Hydra offers unique experimental advantages: a simple nerve net, robust regenerative capacity, a well described behavioral repertoire, and tractable whole-body calcium imaging that allows mapping of neural and muscle activity, and cell type identity, in an integrated manner. This makes Hydra a powerful system to investigate how neuropeptidergic signaling shapes neuronal ensembles and behavior. Here, we identify 61 putative unique neuropeptides and 65 neuropeptide-specific G protein-coupled receptors (GPCRs). We show that different neuronal cell types display specific neuropeptide and receptor expression profiles, suggestive of defined communication pathways within Hydra´s decentralized nervous system. Network topology analysis of the neuropeptide network reveals a dense and distributed signaling architecture, with ectodermal neurons acting as centralized hubs for organism-wide coordination. Computational simulations using a simplified model of the nerve net demonstrate that this architecture can implement stable dynamical states. Our study reveals a comprehensive neuropeptidergic network in a non-bilaterian species, highlighting the evolutionary continuity and functional relevance of wireless chemical networks for complex behavior. Moreover, the distributed and recurrent connectivity we uncover suggests the existence in nervous systems of attractor neural networks implemented with chemical signaling, as opposed to synaptic wiring.

Hydra vulgaris is one of the few cnidarian species that live in freshwater environments. To understand this adaptation, we studied Hydra's mechanisms of osmoregulation. Behavioral imaging showed that Hydra accumulates water in its gastric cavity over time and periodically excretes it through the mouth. Comparative genetic analysis revealed unique aquaporin water-channel expression in Hydra's endodermal epithelium, where ultrastructural data demonstrated small clear vesicles near the gastric cavity, suggesting a potential water-release pathway. We further found that endodermal rhythmic potential 2 (RP2) neurons increase their activity before water excretion, until a threshold activity level is reached, when the mouth opens, and their activity abruptly declines. The ramping of RP2 activity, well modeled by spike-count and leaky-integration algorithms with a 30 s integration time window, leads to the specific activation of epithelial muscle cells near the mouth region before mouth opening. Confirming a causal role of RP2 in the behavior, two-photon activation of RP2 neurons induces water excretion, while ablating RP2 neurons alters it. Consistent with this, GLWamide peptides, synthesized by RP2 neurons, induce water excretion. We conclude that activation of RP2 neurons and subsequent release of GLWamide-family peptides promote water excretion and propose a circuit model for the temporal integration and sequential unfolding of this osmoregulatory cycle. Our work demonstrates that neural integration algorithms and peptide-based signaling can be used by simple nervous systems to coordinate a behavioral and physiological program.

Dendritic spines are the principal postsynaptic targets of excitatory synapses, yet the network logic governing their organization and contribution to cortical computation remains unclear. Here, we combine the MICrONS ultrastructural reconstruction of mouse visual cortex with matched in vivo two-photon calcium imaging to explore the network roles of dendritic spines. Structurally, we show that spine density predicts the number and diversity of both excitatory and inhibitory presynaptic partners, supporting the long-standing "connectivity and diversity" hypothesis. Beyond expanding input space, spines exhibit distinct network-level principles: excitatory axons preferentially target spines with increasing axonal distance from the soma; highly broadcasting ("hub") neurons preferentially innervate spines; and neurons sharing presynaptic partners preferentially route their own outputs onto spines, revealing an input-to-spine/output-to-spine wiring correlations. Functionally, we find that these same structural motifs are found in neuronal ensembles, defined as groups of neurons with correlated calcium activity. Ensemble members are interconnected at more than twice the rate of spatially matched controls; these synapses connecting them are routed almost exclusively onto dendritic spines; and their shared presynaptic partners are almost exclusively inhibitory neurons that themselves also preferentially target spines. We suggest that local recurrent spine-targeted excitation effectively binds ensemble members into a coactive subnetwork, while shared spine-directed inhibition gates, synchronizes, and stabilizes their collective activity, providing a circuit-level architecture that may support attractor dynamics, pattern completion, and other cortical computations.

The integration of synaptic inputs is a fundamental function of neurons. In the traditional model, excitatory inputs are summed at the soma to generate action potentials. However, how synaptic inputs are integrated by dendrites in vivo remains poorly explored. We used intravital two-photon dendritic imaging with a genetically encoded voltage indicator (accelerated sensor of action potentials 5) together with somatic whole-cell patch clamp recordings to investigate how synaptic depolarizations are transferred to the soma in pyramidal neurons of the mouse somatosensory cortex. We studied the integration of synaptic inputs under spontaneous and sensory-evoked conditions, as well as following electrical and optogenetic stimulation. In all cases, while multiple inputs evoked measurable depolarizations in the cell body, isolated synaptic potentials were strongly attenuated. Our results suggest that isolated synaptic inputs have a minimal contribution to somatic depolarization, whereas coincident inputs within short temporal windows are more effective, indicating a regime of dendritic integration that favors coincident or clustered neuronal activity in cortical networks.

Wavefunction tuning in semiconductor nanocrystals has the potential to increase electric field sensitivity by optimizing the polarizability of the exciton. Here we studied the luminescence of spherical quantum wells (SQWs) with on a thin layer of CdSe grown between a CdS core and shell nanocrystal. The photoluminescence intensity and emission spectrum are studied in the presence of an applied electric field. We show that the evolution of the photoluminescence can be rationally controlled by the architecture. Furthermore, we demonstrate that embedding QDs in fusogenic liposomes is a viable strategy for delivering QDs to the cell membrane of HEK cells. Together, these findings can help optimize voltage sensitive probes for optical voltage imaging applications.

Temporal information processing is critical for brain function, supporting neural computations such as novelty detection, adaptation, and temporal normalization. Its disruption is implicated in schizophrenia. We present a protocol for analyzing ongoing neuronal network activity using binwise decoding, trial-to-trial variability analysis, and estimation of network-intrinsic timescales (INTs). We apply these techniques to identify slow dynamics that encode the memory of recent stimuli in neuronal populations in the mouse auditory cortex and in artificial neural networks trained on a novelty-detection task.For complete details on the use and execution of this protocol, please refer to Shymkiv et al.1

Neuronal ensembles, defined as groups of coactive neurons, are physiological modules of the cerebral cortex. Calcium imaging and optogenetics have enabled mapping and manipulating ensembles with single-cell resolution in mouse visual cortex, providing evidence of their importance. Ensembles dominate cortical activity and are generated endogenously or by sensory stimulation. Ensembles are imprinted by activating neurons synchronously and can be reactivated by “pattern completion” trigger cells. Intrinsic excitability mediates ensemble coactivation and reactivation, while UP states shield ongoing ensembles from external inputs. Neurons can belong to different ensembles, forming a combinatorial system that encodes visual stimuli accurately and stably. Ensembles contain pyramidal neurons and interneurons and inhibited “offsemble” cells. Cross inhibition makes ensembles orthogonal to one another, while astrocytic activation increases ensemble occurrence. Ensembles can last for weeks, providing a substrate for long-term information storage, and they also capture the recent history of stimulus presentation, implementing short-term memory. Optogenetic manipulation of ensembles demonstrates that they are necessary and sufficient for visual discrimination and perceptual states. Ensembles are altered in mouse models of epilepsy, schizophrenia, Alzheimer’s disease, autism spectrum disorders, and medically induced loss of consciousness. An ensemble model of the cortex is proposed in which ensembles are functional units that activate each other via trigger cells and silence nondesired ensembles by cross-inhibition. This generates a map of orthogonal attractor states, forming a computationally powerful memory and processing system. Ensembles are likely involved in many brain diseases, so manipulating them could offer avenues for new therapeutics.

A major debate in the field of consciousness pertains to whether neuronal activity or rather the causal structure of neural circuits underlie the generation of conscious experience. The former position is held by theoretical accounts of consciousness based on the predictive processing framework (such as neurorepresentationalism and active inference), while the latter is posited by the integrated information theory. This protocol describes an experiment, part of a larger adversarial collaboration, that was designed to address this question through a combination of behavioral tests in mice, functional imaging, patterned optogenetics and electrophysiology. The experiment will directly test if optogenetic inactivation of a portion of the visual cortex not responding to behaviorally relevant stimuli will affect the perception of the spatial distribution of these stimuli, even when the neurons being inactivated display no or very low spiking activity, so low that it does not induce a significant effect on other cortical areas. The results of the experiment will be compared against theoretical predictions, and will provide a major contribution towards understanding what the neuronal substrate of consciousness is.

The cortex amplifies responses to novel stimuli while suppressing redundant ones. Novelty detection is necessary to efficiently process sensory information and build predictive models of the environment, and it is also altered in schizophrenia. To investigate the circuit mechanisms underlying novelty detection, we used an auditory "oddball" paradigm and two-photon calcium imaging to measure responses to simple and complex stimuli across mouse auditory cortex. Stimulus statistics and complexity generated specific responses across auditory areas. Neuronal ensembles reliably encoded auditory features and temporal context. Interestingly, stimulus-evoked population responses were particularly long lasting, reflecting stimulus history and affecting future responses. These slow cortical dynamics encoded stimulus temporal context and generated stronger responses to novel stimuli. Recurrent neural network models trained on the oddball task also exhibited slow network dynamics and recapitulated the biological data. We conclude that the slow dynamics of recurrent cortical networks underlie processing and novelty detection.

Many techniques to record and manipulate neuronal activity across large portions of the vertebrate brain are now available. However, few effective approaches enable both optical and mechanical access to the brain. Here, we present a protocol for synthesizing, implanting, and using polydimethylsiloxane (PDMS) windows as skull replacements for chronic wide-field and two-photon calcium imaging in mice. We also describe steps for performing viral injections and multi-site silicon probe implantation.

The cnidarian Hydra vulgaris has a simple nervous system with as little as a few hundred neurons distributed in two nerve nets in the ectoderm and endoderm. Using this simple neural chassis, Hydra can paradoxically perform relatively sophisticated behaviors, such as somersaulting and feeding. To understand how this simple nervous system is organized, we have performed a partial ultrastructural reconstruction of the endoderm nerve net of a small Hydra specimen. Neurons can be classified into 5 morphological subtypes, most of which connect via specialized interdigitations of their neurite tips, resembling handshakes. Neuronal processes cross the mesoglea, providing a means to coordinate the activity of the ectoderm and endoderm of the animal. Neurons have several different types of vesicles, including clear and dense core ones, which could support synaptic transmission. However, most vesicles are located far from other neurons. Moreover, neurite handshakes are mostly devoid of vesicles. We speculate that Hydra's endodermal nerve net operates as a non-synaptic circuit, using a neuroendocrine chemical network to implement its functional operations. VIDEO ABSTRACT.

Many techniques to record and manipulate neuronal activity across large portions of the vertebrate brain, such as widefield and two-photon calcium imaging, electrophysiology, and optogenetics, are now available. However, few effective approaches enable both optical and mechanical access to the brain. In this work, we offer an in-depth guide for synthesizing, implanting, and using polydimethylsiloxane (PDMS) windows as skull replacements for chronic optical neuronal imaging. Furthermore, we provide instructions to perform viral injections and multi-site silicon probe implantation. ![Graphical abstract][1] Graphical abstract ### Competing Interest Statement The authors have declared no competing interest. [1]: pending:yes

Rapid advances in neurotechnology are eroding the boundary between mental activity and data, creating urgent risks for mental privacy. This article examines the regulatory landscape and proposes a framework – grounded in user agency, data solidarity, and precaution – to strengthen protections for neural data.

This Viewpoint discusses protecting neural data privacy.