Biology subjects

Reimer, J.

Publications and source records attributed to Reimer, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Explaining the pathogenesis of African swine fever using knowledge-driven regulatory network modeling

A lethal DNA virus with significant economic impact on livestock farmers worldwide, the pathogenesis of African swine fever virus (ASFV) infection is complex and continues to challenge the development of effective vaccine candidates. The requirement for high-containment conditions further complicates its study, resulting in limitations in sample size and marker assessment that challenge conventional statistical analysis. In this work we demonstrate how prior knowledge of immune biology and pathogen-host proteome interactions can be leveraged and reconciled with sparse experimental data to deliver plausible mechanistically informed hypotheses describing ASFV illness progression. We apply large-scale automated mining of literature and pathway schema together with generative artificial intelligence (AI) to create closed-loop regulatory network models consisting of 133 pathogen and host proteins linked by 676 regulatory interactions. Immune regulatory tuning of these networks is reverse engineered to explain two distinct experimentally observed illness progression trajectories in only 5 markers measured every second day over a maximum of 8 days. Comparison of network model pools specific to each progression phenotype suggest that these significantly different outcomes may arise from altered regulatory tuning of genes coding for interleukin (IL)1{beta}, tumor necrosis factor (TNF) and Forkhead box protein (FOX)O4, potentially as a result of epigenetic adaptations. Simulated challenges with individual ASFV protein confirm broadly delayed interferon (IFN)-{gamma}I response in both phenotypes, with multigene family (MGF)505-3R offering the earliest induction and only in the more severe phenotype. Paradoxically, predictions suggest that this delay is preceded by an early IL-10 induction by this same viral protein. While added model granularity and validation is needed, we propose that this proof-of-concept knowledge driven approach offers an attractive solution to mechanistic hypothesis generation in data poor environments.

Systems Biology↗

bioRxiv2026

A red-emitting, genetically encoded indicator for two-photon voltage recording in vivo

Genetically encoded voltage indicators (GEVIs) enable minimally invasive, cell-type-specific optical measurements of neuronal membrane potential with millisecond temporal resolution. Red-shifted GEVIs are especially advantageous because they permit spectral multiplexing with complementary sensors and enable all-optical circuit interrogation in combination with blue-light-activated opsins. Despite these advantages, existing red GEVIs remain poorly suited for in vivo use due to limited performance under two-photon (2P) excitation, the predominant modality for deep-tissue imaging. Here, we introduce VADER1, a red GEVI that overcomes this limitation and enables reliable spike detection in vivo under 2P illumination. Under 2P excitation, VADER1 supports extended voltage imaging with both random-access and resonant-scanning microscopy, enables recordings from neurons as deep as cortical layer 5, and allows dual-color imaging with calcium indicators. By filling a critical spectral gap, VADER1 enables integrated optical measurements of fast electrical activity alongside other neural signals and establishes a foundation for two-photon all-optical electrophysiology.

neuroscience↗

bioRxiv2026

The functional organization of retinal input to the mouse superior colliculus

The superior colliculus integrates retinal input to drive rapid, adaptive visual behavior, yet how the functional diversity of retinal ganglion cell types is represented in superior colliculus remains poorly understood. Using chronic two-photon calcium imaging of retinal ganglion cell axonal boutons in awake mice, we recorded over 200,000 boutons across superficial superior colliculus layers -- a scale that enabled systematic comparison with large-scale ex vivo retinal datasets. This revealed that the superior colliculus receives a near-complete sampling of retinal ganglion cell functional diversity. Functionally distinct response types were organized in systematic laminar gradients: not only response properties such as direction selectivity and contrast suppression, but retinal response types themselves varied systematically with depth. To probe how this organized input encodes natural scenes, we trained a "digital twin" deep network model on natural movie responses and validated its generalization to parametric stimuli, including cell type identification. Leveraging this model to generate predicted responses to looming stimuli, we identify a discrete subset of retinal response types tuned for collision detection at low angular thresholds -- a specialization embedded within a broader, non-specialized retinal population. The digital twin is made publicly available as a community resource. Together, these findings provide a comprehensive functional map of retinal drive to the superior colliculus and an in silico platform for linking retinal cell types to behaviorally relevant superior colliculus computations.

neuroscience↗

bioRxiv2026

Designer indicators for two-photon recording of subthreshold voltage dynamics

Subthreshold voltage dynamics are critical for neuronal information integration, yet they remain understudied in vivo due to the limitations of current tools. While genetically encoded voltage indicators (GEVIs) offer a promising alternative, their application for deep-tissue recording using two-photon (2P) microscopy--a preferred method for deep-tissue recording--has been hindered by insufficient sensitivity for detecting millivolt-scale subthreshold signals. Here, we refined our multiparametric two-photon high-throughput screening platform to develop two novel GEVIs, JEDI3sub and JEDI3hyp, tailored explicitly for subthreshold voltage detection. Through fast 2P optical recording in awake, behaving mice, we demonstrated the superior sensitivity of JEDI3 indicators compared to JEDI-2P. We also showed that JEDI3sub can track population-level subthreshold optical tuning, while JEDI3hyp reliably captured subthreshold dynamics associated with sharp-wave ripple oscillations in hippocampal PV interneurons. Finally, JEDI3hyp facilitated extended imaging of brain-state-dependent, millivolt-scale subthreshold voltage changes across deep-layer somas, fine dendritic structures, and diverse cell types. By addressing the critical gap in 2P optical recording of subthreshold voltage dynamics, JEDI3 indicators open new avenues for studying neural information processing and its alterations in health and disease.

neuroscience↗

bioRxiv2026

A versatile, positive-going voltage indicator that enables accessible two-photon recordings in vivo

Genetically encoded voltage indicators (GEVIs) enable cell-type-specific optical readout of membrane potential, but two-photon (2P) spike detection has been hampered by low signal-to-noise and ultrafast off-kinetics, restricting use to specialized microscopes. We introduce FORCE1s, a green, positive-going GEVI engineered to make robust 2P voltage imaging broadly accessible. FORCE1s brightens from a dark baseline during depolarization, reports spikes with [~]100% {Delta}F/F in awake mice, and displays repolarization kinetics that are tuned for reliable spike detection at sub-kilohertz frame rates. As a result, FORCE1s supports spike-resolved multi-cell recordings on standard resonant-scanning microscopes, and further scales to larger fields of view and neuron counts on advanced modalities. FORCE1s also enables multiplexed voltage-neurotransmitter imaging and extended recordings in freely moving mice using a compact, affordable MEMS-based 2P miniscope. Together, these advances establish FORCE1s as a community-ready tool that democratizes deep-tissue voltage imaging across platforms and experimental contexts.

neuroscience↗

bioRxiv2026

Inferring norepinephrine dynamics from partial observations reveals the temporal structure of elevations during arousal

Hemodynamic artifacts present a significant challenge for two-photon fluorescence imaging of genetically encoded reporters, particularly when the timescale of relevant measurements matches those of vascular dynamics. This is an acute challenge for sensors in which the hemodynamic artifact is of comparable magnitude to the biological signal of interest. However, standard correction methods, such as isobestic recording or repeated experiments, are often impractical. Here we introduce a tiered framework for inferring norepinephrine (NE) dynamics across varying levels of recording information. First, we verify that dual-channel recording using an inert fluorescent reporter alongside the neuromodulator indicator enables direct hemodynamic correction within the same recording session. For contexts in which a dedicated reference channel is unavailable, we trained an LSTM-based model that predicts and removes hemodynamic contributions post-hoc from the recorded NE signal and behavioral variables. Finally, we show that key features of NE dynamics can be recovered from behavioral variables alone, providing an estimate of neuromodulatory state even when fluorescence recordings are unavailable. These methods enabled simultaneous multi-spectral measurements of axonal activity and neuromodulator release via simultaneous two-photon imaging of LC noradrenergic axons and extracellular NE in the same field of view. Cortical NE signals are graded with respect to behavioral intensity, scaling with both locomotion duration and pupil dilation amplitude. As expected, axonal activity precedes increases in ambient NE levels, but NE peaks later within a run and remains elevated after axonal activity has subsided, suggesting that extracellular NE integrates LC output over time rather than tracking instantaneous LC firing. Together, these findings demonstrate that accurate hemodynamic correction is essential for interpreting NE dynamics, and reveal a clearer view of the temporal structure of cortical norepinephrine signaling.

neuroscience↗

bioRxiv2025

Leveraging Dynamic Stability to Infer Regulation in Protein-Protein Interaction Networks: A Study of Infectious Vulnerability in COPD.

The fourth leading cause of death in the US, Chronic Obstructive Pulmonary Disease (COPD) is punctuated by frequent viral and bacterial infections causing severe acute exacerbations (AECOPD) and increased mortality. In previous work we have shown that altered immune cell signaling may confer increased and persistent susceptibility to infection. Here we continue this investigation by conducting broad-spectrum proteomic profiling of circulating white blood cells to assemble an empirical protein-protein interaction network associated with frequency of infectious exacerbation. In a novel extension of conventional cross-sectional data analyses, we translate these undirected protein-protein interactions into candidate regulatory relationships with both direction and mode of action. The latter are inferred by formulating and solving a constraint satisfaction problem (SAT) whereby predicted dynamic behaviors of any valid regulatory network must support the expected persistent nature of low and high vulnerability phenotypes. Solving this SAT problem produced a set of competing candidate protein regulatory network architectures and signalling rules that unanimously highlighted several novel candidate pathway elements involved in oxidative stress response. Analysis of the overall dynamics supported by these networks, again supported the hypothesis that progression beyond an immune tipping point may confer persistent susceptibility to infection and that this may constitute a stable phenotype or regulatory trap in COPD characterized by a reactive oxygen cascade. HIGHLIGHTSO_LIInfectious vulnerability phenotypes in COPD may represent immune regulatory traps. C_LIO_LIA 15-protein oxidative stress response interactome offers a phenotypic signature. C_LIO_LIHomeostasis in cross-sectional data can support inference of co-regulatory actions. C_LIO_LI8 of 11 documented regulatory actions recovered in candidate model majority vote C_LIO_LI17 new candidate protein co-regulatory actions proposed unanimously by all models. C_LI

systems biology↗

bioRxiv2025

Disease Severity Across Psychiatric Disorders Is Linked to Pro-Inflammatory Cytokines

ImportanceNumerous studies indicate that the traditional categorical classification of severe mental disorders (SMD), such as schizophrenia, bipolar disorders, and major depressive disorders, does not align with the underlying biology of those disorders as they frequently overlap in terms of symptoms and risk factors. ObjectiveThis study aimed to identify transdiagnostic patient clusters based on disease severity and explore the underlying biological mechanisms independently of the traditional categorical classification. DesignWe utilized data from 443 participants diagnosed with SMD of the PsyCourse Study, a longitudinal study with deep phenotyping across up to four visits. We performed longitudinal clustering to group patients based on symptom trajectories and cognitive performance. The resulting clusters were compared on cross-sectional variables, including independent measures of severity as well as polygenic risk scores, serum protein quantification, miRNA expression, and DNA methylation. ResultsWe identified two distinct clusters of patients that exhibited marked differences in illness severity but did not differ significantly in age, sex, or diagnostic proportions. We found 19 serum proteins significantly dysregulated between the two clusters. Functional enrichment pointed to a convergence of immune system dysregulation and neurodevelopmental processes. ConclusionThe observed differences in serum protein expression suggest that disease severity is associated with the convergence of immune system dysregulation and neurodevelopmental alterations, particularly involving pathways related to inflammation and brain plasticity. The identification of pro-inflammatory proteins among the differentially expressed markers underscores the potential role of systemic inflammation in the pathophysiology of SMD. These results highlight the importance of considering illness severity as a core dimension in psychiatric research and clinical practice and suggest that targeting immune-related mechanisms may offer promising new therapeutic avenues for patients with SMD. Key points QuestionCan analyzing symptom trajectories and cognitive profiles across diagnostic categories reveal clinically relevant subgroups in severe mental disorders? FindingsIn this longitudinal study of 443 individuals with severe mental disorders, two distinct clusters emerged, differing significantly in illness severity, with the more severe group displaying elevated pro-inflammatory serum proteins, suggesting an association between disease severity and inflammation. MeaningThese findings suggest that transdiagnostic clustering clarifies shared mechanisms, underscores the importance of inflammation in severe mental disorders, and highlights a promising avenue for novel therapeutic approaches.

immunology↗

bioRxiv2023

Simultaneously-recorded cholinergic axons and cortical acetylcholine are highly correlated with pupil size and locomotion under spontaneous conditions

Even under spontaneous conditions and in the absence of changing environmental demands, awake animals alternate between increased or decreased periods of alertness. These changes in brain state can occur rapidly, on a timescale of seconds, and neuromodulators such as acetylcholine (ACh) are thought to play an important role in driving these spontaneous state transitions. Here, we perform the first simultaneous imaging of ACh sensors and GCaMP-expressing axons in vivo, to examine the spatiotemporal properties of cortical ACh activity and release during spontaneous changes in behavioral state. We observed a high correlation between simultaneously recorded basal forebrain axon activity and neuromodulator sensor fluorescence around periods of locomotion and pupil dilation. Consistent with volume transmission of ACh, increases in axon activity were accompanied by increases in local ACh levels that fell off with the distance from the nearest axon. GRAB-ACh fluorescence could be accurately predicted from axonal activity alone, providing the first validation that neuromodulator axon activity is a reliable proxy for nearby neuromodulator levels. Deconvolution of fluorescence traces allowed us to account for the kinetics of the GRAB-ACh sensor and emphasized the rapid clearance of ACh for smaller transients outside of running periods. Finally, we trained a predictive model of ACh fluctuations from the combination of pupil size and running speed; this model performed better than using either variable alone, and generalized well to unseen data. Overall, these results contribute to a growing understanding of the precise timing and spatial characteristics of cortical ACh during fast brain state transitions.

neuroscience↗

bioRxiv2023

Physiological accuracy in simulating refractory cardiac tissue: the volume-averaged bidomain model vs. the cell-based EMI model

The refractory period of cardiac tissue can be quantitatively described using strength-interval (SI) curves. The information captured in SI curves is pertinent to the design of anti-arrhythmic devices including pacemakers and implantable cardioverter defibrillators. As computational cardiac modelling becomes more prevalent, it is feasible to consider the generation of computationally derived SI curves as a supplement or precursor to curves that are experimentally derived. It is beneficial, therefore, to examine the profiles of the SI curves produced by different cardiac tissue models to determine whether some models capture the refractory period more accurately than others. In this study, we compare the unipolar SI curves of two tissue models: the current state-of-the-art bidomain model and the recently developed extracellular-membrane-intracellular (EMI) model. The EMI models resolution of individual cell structure makes it a more detailed model than the bidomain model, which forgoes the structure of individual cardiac cells in favour of treating them homogeneously as a continuum. We find that the resulting SI curves elucidate differences between the models, including that the behaviour of the EMI model is noticeably closer to the refractory behaviour of experimental data compared to that of the bidomain model. These results hold implications for future computational pacemaker simulations and shed light on the predicted refractory properties of cardiac tissue from each model. Author summaryMathematical modelling and computational simulation of cardiac activity have the potential to greatly enhance our understanding of heart function and improve the precision of cardiac medicine. The current state-of-the-art model is the bidomain model, which considers a volume average of cardiac activity. Although the bidomain model has had success in several applications, in other situations, its approach may obscure critical details of heart function. The extracellular-membrane-intracellular (EMI) model is a recently developed model of cardiac tissue that addresses this limitation. It models cardiac cells individually; therefore, it offers significantly greater physiological accuracy than bidomain simulations. This increase in accuracy comes at a higher computational cost, however. To explore the benefits of one model over the other, here we compare the performance of the bidomain and EMI models in a pacing study of cardiac tissue often employed in pacemaker design. We find that the behaviour of the EMI model is noticeably closer to experimental data than the behaviour of the bidomain model. These results hold implications for future pacemaker design and improve our understanding of the two models in relation to one another.

physiology↗

bioRxiv2023

Towards a Foundation Model of the Mouse Visual Cortex

The complexity of neural circuits makes it challenging to decipher the brains algorithms of intelligence. Recent break-throughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding of the brains computational objectives and neural coding. However, these models struggle to generalize beyond their training distribution, limiting their utility. The emergence of foundation models, trained on vast datasets, has introduced a new AI paradigm with remarkable generalization capabilities. We collected large amounts of neural activity from visual cortices of multiple mice and trained a foundation model to accurately predict neuronal responses to arbitrary natural videos. This model generalized to new mice with minimal training and successfully predicted responses across various new stimulus domains, such as coherent motion and noise patterns. It could also be adapted to new tasks beyond neural prediction, accurately predicting anatomical cell types, dendritic features, and neuronal connectivity within the MICrONS functional connectomics dataset. Our work is a crucial step toward building foundation brain models. As neuroscience accumulates larger, multi-modal datasets, foundation models will uncover statistical regularities, enabling rapid adaptation to new tasks and accelerating research.

neuroscience↗

bioRxiv2023

Bipartite invariance in mouse primary visual cortex

A primary goal of sensory systems is to extract robust and meaningful features that are invariant to variations in the sensory input. Characterizing these invariances at the neuronal level is crucial for understanding how the visual system supports generalization, but the high-dimensional nature of ecological stimuli poses major challenges. Consequently, our understanding of how the brain represents invariances has historically depended on a few examples, such as phase invariance to grating stimuli in V1 complex cells. Here, we leverage the inception loop paradigm --iterating between large-scale recordings, deep learning neuronal predictive models, and in silico experiments with in vivo verification--to characterize neuronal invariances in mouse V1. Using a neuronal predictive model, we synthesized Diverse Exciting Inputs (DEIs) that strongly drive target neurons while differing substantially in image space. These DEIs revealed a novel bipartite invariance: one portion of the receptive field encodes shift-invariant, high-frequency textures, while the other encodes a fixed, low-frequency spatial pattern. This subfield division aligned with object boundaries defined by spatial frequency differences in highly activating stimuli, suggesting bi-partite invariance contributes to segmentation. Our analysis of computational models and anatomical data from the MICrONS dataset revealed a hierarchical organization of excitatory neurons in mouse V1 Layers 2/3: We found that postsynaptic neurons exhibited greater invariance than their presynaptic inputs, while neurons with lower invariance formed more connections. These findings suggest a synaptic-level hierarchy that progressively increases neural invariance within the primary visual cortex. Intriguingly, similar high-low frequency bipartite patterns strongly activate certain units in artificial neural networks, suggesting that universal visual representations govern both biological and artificial systems, potentially aiding in the extraction of visual features from complex backgrounds.

neuroscience↗

bioRxiv2023

Pattern completion and disruption characterize contextual modulation in mouse visual cortex

Vision is fundamentally context-dependent, with neuronal responses influenced not just by local features but also by surrounding contextual information. In the visual cortex, studies using simple grating stimuli indicate that congruent stimuli--where the center and surround share the same orientation--are more inhibitory than when orientations are orthogonal, potentially serving redundancy reduction and predictive coding. Understanding these center-surround interactions in relation to natural image statistics is challenging due to the high dimensionality of the stimulus space, yet crucial for deciphering the neuronal code of real-world sensory processing. Utilizing large-scale recordings from mouse V1, we trained convolutional neural networks (CNNs) to predict and synthesize surround patterns that either optimally suppressed or enhanced responses to center stimuli, confirmed by in v ivo experiments. Contrary to the notion that congruent stimuli are suppressive, we found that surrounds that completed patterns based on natural image statistics were facilitatory, while disruptive surrounds were suppressive. Applying our CNN image synthesis method in macaque V1, we discovered that pattern completion within the near surround occurred more frequently with excitatory than with inhibitory surrounds, suggesting that our results in mice are conserved in macaques. Further, experiments and model analyses confirmed previous studies reporting the opposite effect with grating stimuli in both species. Using the MICrONS functional connectomics dataset, we observed that neurons with similar feature selectivity formed excitatory connections regardless of their receptive field overlap, aligning with the pattern completion phenomenon observed for excitatory surrounds. Finally, our empirical results emerged in a normative model of perception implementing Bayesian inference, where neuronal responses are modulated by prior knowledge of natural scene statistics. In summary, our findings identify a novel relationship between contextual information and natural scene statistics and provide evidence for a role of contextual modulation in hierarchical inference.

neuroscience↗

bioRxiv2023

Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex

Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.

neuroscience↗

bioRxiv2022

It takes neurons to understand neurons: Digital twins of visual cortex synthesize neural metamers

Metamers, images that are perceived as equal, are a useful tool to study representations of natural images in biological and artificial vision systems. We synthesized metamers for the mouse visual system by inverting a deep encoding model to find an image that matched the observed neural activity to the original presented image. When testing the resulting images in physiological experiments we found that they most closely reproduced the neural activity of the original image when compared to other decoding methods, even when tested in a different animal whose neural activity was not used to produce the metamer. This demonstrates that deep encoding models do capture general characteristic properties of biological visual systems and can be used to define a meaningful perceptual loss for the visual system.

neuroscience↗

bioRxiv2022

Quantitative Census of Local Somatic Features in Mouse Visual Cortex

Mammalian neocortex contains a highly diverse set of cell types. These types have been mapped systematically using a variety of molecular, electrophysiological and morphological approaches. Each modality offers new perspectives on the variation of biological processes underlying cell type specialization. Cellular scale electron microscopy (EM) provides dense ultrastructural examination and an unbiased perspective into the subcellular organization of brain cells, including their synaptic connectivity and nanometer scale morphology. It also presents a clear challenge for analysis to identify cell-types in data that contains tens of thousands of neurons, most of which have incomplete reconstructions. To address this challenge, we present the first systematic survey of the somatic region of all cells within a cubic millimeter of cortex using quantitative features obtained from EM. This analysis demonstrates a surprising sufficiency of the perisomatic region to identify cell-types, including types defined primarily based on their connectivity patterns. We then describe how this classification facilitates cell type specific connectivity characterization and locating cells with rare connectivity patterns in the dataset.

neuroscience↗

bioRxiv2021

Behavioral state tunes mouse vision to ethological features through pupil dilation

Sensory processing changes with behavioral context to increase computational flexibility. In the visual system, active behavioral states enhance sensory responses but typically leave the preferred stimuli of neurons unchanged. Here we find that behavioral state does modulate stimulus selectivity in mouse visual cortex in the context of colored natural scenes. Using population imaging, behavior, pharmacology, and deep neural networks, we identified a shift of color selectivity towards ultraviolet stimuli exclusively caused by pupil dilation, resulting in a dynamic switch from rod to cone photoreceptors, extending their role beyond night and day vision. This facilitated the detection of ethological stimuli, such as aerial predators against the twilight sky. In contrast to previous studies that have used pupil dilation as an indirect measure of brain state, our results suggest that the brain uses pupil dilation itself to differentially recruit rods and cones on short timescales to tune visual representations to behavioral demands.

neuroscience↗

bioRxiv2021

Petascale neural circuit reconstruction: automated methods

3D electron microscopy (EM) has been successful at mapping invertebrate nervous systems, but the approach has been limited to small chunks of mammalian brains. To scale up to larger volumes, we have built a computational pipeline for processing petascale image datasets acquired by serial section EM, a popular form of 3D EM. The pipeline employs convolutional nets to compute the nonsmooth transformations required to align images of serial sections containing numerous cracks and folds, detect neuronal boundaries, label voxels as axon, dendrite, soma, and other semantic categories, and detect synapses and assign them to presynaptic and postsynaptic segments. The output of neuronal boundary detection is segmented by mean affinity agglomeration with semantic and size constraints. Pipeline operations are implemented by leveraging distributed and cloud computing. Intermediate results of the pipeline are held in cloud storage, and can be effortlessly viewed as images, which aids debugging. We applied the pipeline to create an automated reconstruction of an EM image volume spanning four visual cortical areas of a mouse brain. Code for the pipeline is publicly available, as is the reconstructed volume.

neuroscience↗