Biology subjects

Lo, C.-C.

Publications and source records attributed to Lo, C.-C..

At least 19 recordsLinked to original sources

bioRxiv2026

High-resolution volumetric intravital imaging reveals asymmetric serotonin-dependent post-shock activity in the Drosophila brain

Volumetric intravital imaging of adult Drosophila brains remains challenging due to optical scattering and speed limitations of conventional microscopy. Here, we present V-shape Bessel-beam light-sheet microscopy (vSPIM), an upright, high-speed platform achieving subcellular resolution across large, opaque volumes in vivo. Through real-time calcium imaging in adult Drosophila, vSPIM uncovered two unprecedented phenomena: first, a complex ensemble of approximately 150 olfactory projection neuron boutons (more than 400 per hemisphere) resolved 18 distinct odor valence coding patterns within the dense mushroom body calyx, aligning with connectomic architecture. Second, vSPIM revealed a previously uncharacterized asymmetric, serotonin-dependent post-shock firing (PsF) response. This PsF activity emerged within the mushroom body and dopaminergic neurons only after repetitive aversive stimulation, distinct from canonical dopamine-driven reinforcement signals. Finally, we demonstrated the versatility of vSPIM by tracking corneal endothelial dynamics during mouse wound healing. Overall, vSPIM establishes a scalable framework for intravital imaging, bridging the gap between optical innovation and circuit physiology in adult tissues.

neuroscience↗

bioRxiv2025

Flexible Steering and Conflict Resolution: Pro-Goal/Anti-Goal Gating in Drosophila Lateral Accessory Lobes

Navigation comprises multiple processes: sensory integration, position estimation, decision, and finally, motor command generation, which is the control problem. Previous studies discovered that PFL3 neurons compute a heading-goal error and directly project to the descending neurons that generate the steering commands. Yet, this direct pathway is necessary but not sufficient for the flexible control required to pursue goal while avoiding threats and resolving conflicts. Using the Drosophila connectome and circuit modeling, we uncover a layered control architecture in the lateral accessory lobes (LAL) that arbitrates between goal pursuit and stimulus-driving overrides. First, a dorsal pathway that forms a dominant indirect "pro-goal" push-pull circuit that amplifies left-right asymmetries. Second, a faster ventral pathway forms an "anti-goal" circuit that recruits and inverts the pro-goal circuit to redirect movement, and only engages when a goal is present. The architecture also resolves two symmetry challenges: Rear Stalemate (goal 180 behind) and Front Stalemate (threat dead ahead), by rapidly amplifying tiny perturbations into decisive turns. Together, these motifs instantiate comparator, override, and gating principles in a compact neural controller, yielding testable predictions for insect motor control and design rules for bio-inspired, embodied intelligence.

neuroscience↗

bioRxiv2024

Unique Asymmetric Branching of Drosophila Neurons Optimizes Temporal Dendritic Computation

Neurons execute a versatile array of computations through a complex interplay of factors, including their morphology and synaptic architecture. Dendritic branching encodes upstream inputs into diverse spike patterns transmitted via downstream axons. While earlier studies highlighted the distinct morphologies and functions of a few representative neurons, the availability of large-scale electron microscopy and fluorescent imaging now enables comprehensive data analysis and further simulations to explore structure-function relationships more broadly. This study investigates the general morphological characteristics of diverse neuron types in the fly model. By employing the Strahler Order (SO) metric, we identified a specific bias towards asymmetry in neuronal branching and further investigated the effect of the asymmetry on computational capabilities. Specifically, symmetric branching enhances coincidence detection capability, whereas asymmetric branching increases input order-selectivity. While certain neurons exhibit extreme symmetry or asymmetry optimized for specific tasks, most neurons strike a balance between these computational strategies. This balance underscores the intricate relationship between neuronal structure and function. In contrast to the wide range of branching symmetries found in random bifurcation models, neurons across different species exhibit species-specific asymmetry, suggesting shared underlying branching mechanisms. Our findings provide a fresh perspective on the exploration of neuronal morphologies and their computational roles. Significance StatementThis study reveals a novel structure-function relationship by analyzing the asymmetric branching patterns of fly neurons using extensive morphological data. While certain neurons display extreme symmetry or asymmetry for specialized computational roles, most converge toward a characteristic degree of asymmetry to balance their computational demands, especially in larger branching structures. This suggests that neuronal branching may be governed by intrinsic principles that support both developmental and functional needs.

neuroscience↗

bioRxiv2024

Automated neuropil segmentation of fluorescent images for Drosophila brains

The brain atlas, which provides information about the distribution of genes, proteins, neurons, or anatomical regions in the brain, plays a crucial role in contemporary neuroscience research. To analyze the spatial distribution of those substances based on images from different brain samples, we often need to warp and register individual brain images to a standard brain template. However, the process of warping and registration often leads to spatial errors, thereby severely reducing the accuracy of the analysis. To address this issue, we develop an automated method for segmenting neuropils in the Drosophila brain using fluorescence images from the FlyCircuit database. This technique allows future brain atlas studies to be conducted accurately at the individual level without warping and aligning to a standard brain template. Our method, LYNSU (Locating by YOLO and Segmenting by U-Net), consists of two stages. In the first stage, we use the YOLOv7 model to quickly locate neuropils and rapidly extract small-scale 3D images as input for the second stage model. This stage achieves a 99.4% accuracy rate in neuropil localization. In the second stage, we employ the 3D U-Net model to segment neuropils. LYNSU can achieve high accuracy in segmentation using a small training set consisting of images from merely 16 brains. We demonstrate LYNSU on six distinct neuropils or structure, achieving a high segmentation accuracy, which was comparable to professional manual annotations with a 3D Intersection-over-Union(IoU) reaching up to 0.869. Most notably, our method takes only about 7 seconds to segment a neuropil while achieving a similar level of performance as the human annotators. The results indicate the potential of the proposed method in high-throughput connectomics construction for Drosophila brain optical imaging.

neuroscience↗

bioRxiv2024

Hyper-Flexible Neural Networks: Rapidly Switching between Logic Operations in a Compact 4-Neuron Circuit

Biological neural circuits at various levels exhibit rapid adaptability to diverse environmental stimuli. Such fast response times imply that adaptation cannot rely solely on synaptic plasticity, which operates on a much slower timescale. Instead, circuits must be inherently hyper-flexible and receptive to switches in functionalities without changes in network structure. This biological flexibility is a fruitful mechanism for constructing artificial reconfigurable circuits, whether they are spiking or non-spiking. In this study, we demonstrate that a 4-neuron circuit can rapidly and controllably switch between 24 unique logical functions while maintaining the same set of synaptic weights. Moreover, we show that this reconfigurability works for several different underlying neuronal architectures and strikingly can be applied to a network composed of any sigmoid-shaped activation function. We conclude with proof-of-concept applications showing that we can perform standard tasks such as a full-adder, as well as event-based conditional computing, such as detecting unexpected motion.

neuroscience↗

bioRxiv2023

Hybrid Neural Networks of the Olfactory Learning Center in the Drosophila Brain

Biological signal encoding is shaped by the underlying neural circuitry. In Drosophila melanogaster, the mushroom body (MB) houses thousands of Kenyon cells (KCs) that process olfactory signals from hundreds of projection neurons (PNs). Previous studies debated the connectivity between PNs and KCs (random vs. structured). Our multiscale analysis of electron microscopic data revealed a hybrid network with diverse synaptic connection preferences and input divergence across different KC classes. Using MB connectome data, our simulation model, validated via functional imaging, accurately predicted distinct chemical sensitivities in the major KC classes. The model suggests that the hybrid network excels in detecting food odors while maintaining precise odor discrimination in different KC classes. These findings underscore the computational advantages of this hybrid network.

neuroscience↗

bioRxiv2023

Global inhibition in head-direction neural circuits: a systematic comparison between connectome-based spiking neural circuit models

The recent discovery of the head-direction (HD) system in fruit flies has provided unprecedented insights into the neural mechanisms of spatial orientation. Despite the progress, the neural substance of global inhibition, an essential component of the HD circuits, remains controversial. Some studies suggested that the ring neurons provide global inhibition, while others suggested the {Delta}7 neurons. In the present study, we provide evaluations from the theoretical perspective by performing systematic analyses on the computational models based on the ring-neuron (R models) and {Delta}7-neurons (Delta models) hypotheses with modifications according to the latest connectomic data. We conducted four tests: robustness, persistency, speed, and dynamical characteristics. We discovered that the two models led to a comparable performance in general, but each excelled in different tests. The R Models were more robust, while the Delta models were better in the persistency test. We also tested a hybrid model that combines both inhibitory mechanisms. While the performances of the R and Delta models in each test are highly parameter-dependent, the Hybrid model performed well in all tests with the same set of parameters. Our results suggest the possibility of combined inhibitory mechanisms in the HD circuits of fruit flies.

neuroscience↗

bioRxiv2021

Protein loop modeling and refinement using deep learning models

Loops in proteins play essential roles in protein functions and interactions. The structural characterization of loops is challenging because of their conformational flexibility and relatively poor conservation in multiple sequence alignments. Many experimental and computational approaches have been carried out during the last few decades for loop modeling. Although the latest AlphaFold2 achieved remarkable performance in protein structure predictions, the accuracy of loop regions for many proteins still needs to be improved for downstream applications such as protein function prediction and structure based drug design. In this paper, we proposed two novel deep learning architectures for loop modeling: one uses a combined convolutional neural network (CNN)-recursive neural network (RNN) structure (DeepMUSICS) and the other is based on refinement of histograms using a 2D CNN architecture (DeepHisto). In each of the methods, two types of models, conformation sampling model and energy scoring model, were trained and applied in the loop folding process. Both methods achieved promising results and worth further investigations. Since multiple sequence alignments (MSA) were not used in our architecture, the energy scoring models have less bias from MSA. We believe the methods may serve as good complements for refining AlphaFold2 predicted structures.

bioinformatics↗

bioRxiv2021

Attractor Decision Network with Selective Inhibition

The ability to decide swiftly and accurately in an urgent scenario is crucial for an organisms survival. The neural mechanisms underlying the perceptual decision and trade-off between speed and accuracy have been extensively studied in the past few decades. Among several theoretical models, the attractor neural network model has successfully captured both behavioral and neuronal data observed in many decision experiments. However, a recent experimental study revealed additional details that were not considered in the original attractor model. In particular, the study shows that the inhibitory neurons in the posterior parietal cortex of mice are as selective to decision making results as the excitatory neurons, whereas the previous attractor model assumes the inhibitory neurons to be unselective. In this study, we investigate the attractor model with selective inhibition (selective model), which can be considered as a general case of the previous attractor model (unselective model). Our model reproduces several behavioral and neurophysiological observations. To analyze the dynamics of the selective model, we reduce it and show that selectivity adds a time-varying component to the energy landscape. This time dependence of the energy landscape allows the selective model to integrate information carefully in initial stages, then quickly converge to an attractor once the choice is clear. This results in the selective model having a more efficient speed-accuracy trade-off that is unreachable by unselective models.

neuroscience↗

bioRxiv2021

Discovery of an Antarctic ascidian-associated uncultivated Verrucomicrobia that encodes antimelanoma palmerolide biosynthetic capacity

The Antarctic marine ecosystem harbors a wealth of biological and chemical innovation that has risen in concert over millennia since the isolation of the continent and formation of the Antarctic circumpolar current. Scientific inquiry into the novelty of marine natural products produced by Antarctic benthic invertebrates led to the discovery of a bioactive macrolide, palmerolide A, that has specific activity against melanoma and holds considerable promise as an anticancer therapeutic. While this compound was isolated from the Antarctic ascidian Synoicum adareanum, its biosynthesis has since been hypothesized to be microbially mediated, given structural similarities to microbially-produced hybrid non-ribosomal peptide-polyketide macrolides. Here, we describe a metagenome-enabled investigation aimed at identifying the biosynthetic gene cluster (BGC) and palmerolide A-producing organism. A 74 Kbp candidate BGC encoding the multi-modular enzymatic machinery (hybrid Type I-trans-AT polyketide synthase-non-ribosomal peptide synthetase and tailoring functional domains) was identified and found to harbor key features predicted as necessary for palmerolide A biosynthesis. Surveys of ascidian microbiome samples targeting the candidate BGC revealed a high correlation between palmerolide-gene targets and a single 16S rRNA gene variant (R=0.83 - 0.99). Through repeated rounds of metagenome sequencing followed by binning contigs into metagenome-assembled genomes, we were able to retrieve a near-complete genome (10 contigs) of the BGC-producing organism, a novel verrucomicrobium within the Opitutaceae family that we propose here as Candidatus Synoicihabitans palmerolidicus. The refined genome assembly harbors five highly similar BGC copies, along with structural and functional features that shed light on the host-associated nature of this unique bacterium. ImportancePalmerolide A has potential as a chemotherapeutic agent to target melanoma. We interrogated the microbiome of the Antarctic ascidian, Synoicum adareanum, using a cultivation-independent high-throughput sequencing and bioinformatic strategy. The metagenome-encoded biosynthetic machinery predicted to produce palmerolide A was found to be associated with the genome of a member of the S. adareanum core microbiome. Phylogenomic analysis suggests the organism represents a new deeply-branching genus, Candidatus Synoicihabitans palmerolidicus, in the Opitutaceae family of the Verrucomicrobia phylum. The Ca. S. palmerolidicus 4.29 Mb genome encodes a repertoire of carbohydrate-utilizing and transport pathways enabling its ascidian-associated lifestyle. The palmerolide-producers genome also contains five distinct copies of the large palmerolide biosynthetic gene cluster that may provide structural complexity of palmerolide variants.

microbiology↗

bioRxiv2021

Palmerolide PKS-NRPS gene clusters characterized from the microbiome of an Antarctic ascidian

Complex interactions exist between microbiomes and their hosts. Increasingly, defensive metabolites that have been attributed to host biosynthetic capability are now being recognized as products of host-associated microbes. These unique metabolites often have bioactivity targets in human disease and can be purposed as pharmaceuticals. Polyketides are a complex family of natural products that often serve as defensive metabolites for competitive or pro-survival purposes for the producing organism, while demonstrating bioactivity in human diseases as cholesterol lowering agents, anti-infectives, and anti-tumor agents. Marine invertebrates and microbes are a rich source of polyketides. Palmerolide A, a polyketide isolated from the Antarctic ascidian Synoicum adareanum, is a vacuolar-ATPase inhibitor with potent bioactivity against melanoma cell lines. The biosynthetic gene clusters (BGCs) responsible for production of secondary metabolites are encoded in the genomes of the producers as discrete genomic elements. A candidate palmerolide BGC was identified from a S. adareanum microbiome-metagenome based on a high degree of congruence with a chemical structure-based retrobiosynthetic prediction. Protein family homology analysis, conserved domain searches, active site and motif identification were used to identify and propose the function of the [~]75 kb trans-acyltransferase (AT) polyketide synthase-non-ribosomal synthase (PKS-NRPS) domains responsible for the stepwise synthesis of palmerolide A. Though PKS systems often act in a predictable co-linear sequence, this BGC includes multiple trans-acting enzymatic domains, a non-canonical condensation termination domain, a bacterial luciferase-like monooxygenase (LLM), and is found in multiple copies within the metagenome-assembled genome (MAG). Detailed inspection of the five highly similar pal BGC copies suggests the potential for biosynthesis of other members of the palmerolide chemical family. This is the first delineation of a biosynthetic gene cluster from an Antarctic microbial species, recently proposed as Candidatus Synoicohabitans palmerolidicus. These findings have relevance for fundamental knowledge of PKS combinatorial biosynthesis and could enhance drug development efforts of palmerolide A through heterologous gene expression.

biochemistry↗

bioRxiv2020

Augmenting Flexibility: Mutual Inhibition Between Inhibitory Neurons Expands Functional Diversity

Rapid, flexible response to an ever-changing environment is critical for an organisms survival. Recently, multicellular recordings have shown that this rapid, flexible switching between activity patterns is present in neural microcircuits. However, the underlying neural mechanism is not clear. Strikingly, we show in a neural circuit model that mutually inhibitory connections are crucial for rapid and flexible switching between distinct functions without synaptic plasticity. Here, we develop a theoretical framework to explain how inhibitory recurrent circuits give rise to this flexibility and show that mutual inhibition doubles the number of cusp bifurcations in small neural circuits. As a concrete example, we study a commonly observed class of functional motifs we call Coupled Recurrent Inhibitory and Recurrent Excitatory Loops (CRIRELs). These CRIRELs have the advantage of being both multifunctional and controllable, performing a plethora of unique functions. Finally, we demonstrate how mutual inhibition maximizes storage capacity for larger networks.

neuroscience↗

bioRxiv2020

Identification of Neuronal Polarity by Node-Based Machine Learning

Identifying the directions of signal flows in neural networks is one of the most important stages for understanding the intricate information dynamics of a living brain. Using a dataset of 213 projection neurons distributed in different regions of a Drosophila brain, we develop a powerful machine learning algorithm: node-based polarity identifier of neurons (NPIN). The proposed model is trained by nodal information only and includes both Soma Features (which contain spatial information from a given node to a soma) and Local Features (which contain morphological information of a given node). After including the spatial correlations between nodal polarities, our NPIN provided extremely high accuracy (>96.0%) for the classification of neuronal polarity, even for complex neurons with more than two dendrite/axon clusters. Finally, we further apply NPIN to classify the neuronal polarity of the blowfly, which has much less neuronal data available. Our results demonstrate that NPIN is a powerful tool to identify the neuronal polarity of insects and to map out the signal flows in the brains neural networks. Availability of data and materialThe FlyCircuit database (http://www.flycircuit.tw/) is provided by the National Center for High-Performance Computing. Code availabilityWe provide an online version of NPIN to be used or tested by other research groups at the following address: https://npin-for-drosophila.herokuapp.com/

neuroscience↗

bioRxiv2020

PanGIA: A Metagenomics Analytical Framework for RoutineBiosurveillance and Clinical Pathogen Detection

Metagenomics is emerging as an important tool in biosurveillance, public health, and clinical applications. However, ease-of-use for execution and data analysis remains a barrier-of-entry to the adoption of metagenomics in applied health and forensics settings. In addition, these venues often have more stringent requirements for reporting, accuracy, and precision than the traditional ecological research role of the technology. Here, we present PanGIA (Pan-Genomics for Infectious Agents), a novel bioinformatics analysis platform for hosting, processing, analyzing, and reporting shotgun metagenomics data of complex samples suspected of containing one or more pathogens. PanGIA was developed to address gaps that often preclude clinicians, medical technicians, forensics personnel, or other non-expert end-users from the routine application of metagenomics for pathogen identification. Though primarily designed to detect pathogenic microorganisms within clinical and environmental metagenomics data, PanGIA also serves as an analytical framework for microbial community profiling and comparative metagenomics. To provide statistical confidence in PanGIAs taxonomic assignments, the system provides two independent estimations of probability for species and strain level detection. First, PanGIA integrates coverage data with uniqueness information mapped across each reference genome for a stand-alone determination of confidence for each query sequence at each taxonomy level. Second, if a negative-control sample is provided, PanGIA compares this sample with a corresponding experimental unknown sample and determines a measure of confidence associated with detection above background. An integrated graphical user interface allows interactive interrogation and enables users to summarize multiple sample results by confidence score, normalized read abundance, reference genome linear coverage, depth-of-coverage, RPKM, and other metrics to detect specific organisms-of-interest. Comparison testing of the PanGIA algorithm against a number of recent k-mer, read-mapping, and marker-gene based taxonomy classifiers across various real-world datasets with spiked targets shows superior mean positive predictive value, sensitivity, and specificity. PanGIA can process a five million paired-end read dataset in under 1 hour on commodity computational hardware. The source code and documentation are publicly available at https://github.com/LANL-Bioinformatics/PanGIA or https://github.com/mriglobal/PanGIA. The database for PanGIA can be downloaded from ftp://bioinformatics.mriglobal.org/. The full GUI-based PanGIA analysis environment is available in a Docker container and can be installed from https://hub.docker.com/r/poeli/pangia/.

bioinformatics↗

bioRxiv2020

Dissecting the microbiome of a polyketide-producing ascidian across the Anvers Island archipelago, Antarctica

Polar marine ecosystems hold the potential for bioactive compound biodiscovery, based on their untapped macro- and microorganismal diversity. Characterization of polar benthic marine invertebrate-associated microbiomes is limited to few studies. This study was motivated by our interest in better understanding the microbiome structure and composition of the ascidian, Synoicum adareanum, in which the bioactive macrolide that has specific activity to melanoma, palmerolide A (PalA), was found. PalA bears structural resemblance to a combined nonribosomal peptide polyketide, that has similarities to microbially-produced macrolides. We conducted a spatial survey to assess both PalA levels and microbiome composition in S. adareanum in a region of the Antarctic Peninsula near Anvers Island (64{degrees} 46'S, 64{degrees} 03'W). PalA was ubiquitous and abundant across a collection of 21 ascidians (3 subsamples each) sampled from seven sites across the Anvers Island archipelago. The microbiome composition (V3-V4 16S rRNA gene sequence variants) of these 63 samples revealed a core suite of 21 bacteria, 20 of which were distinct from regional bacterioplankton. Co-occurrence analysis yielded several potentially interacting subsystems and, although the levels of PalA detected were not found to correlate with specific sequence variants, the core members appeared to occur in a preferred optimum and tolerance range of PalA levels. Taking these results together with an analysis of biosynthetic potential of related microbiome taxa indicates a core microbiome with substantial promise for natural product biosynthesis that likely interact with the host and with each other.

microbiology↗

bioRxiv2019

Coordination between stabilizing circuits and updating circuits in spatial orientation working memory

Spatial orientation memory plays a crucial role in animal navigation. Recent studies of tethered Drosophila melanogaster (fruit fly) in a virtual reality setting showed that the head direction is encoded in the form of an activity bump, i.e. localized neural activity, in the torus-shaped ellipsoid body (EB). However, how this system is involved in orientation working memory is not well understood. We investigated this question using free moving flies (Drosophila melanogaster) in a spatial orientation memory task by manipulating two EB subsystems, C and P circuits, which are hypothesized for stabilizing and updating the activity bump, respectively. To this end, we suppressed or activated two types of inhibitory ring neurons (EIP and P) which innervate EB, and we discovered that manipulating the two inhibitory neuron types produced distinct behavioral deficits, suggesting specific roles of the inhibitory neurons in coordinating the stabilization and updating functions of the EB circuits. We further elucidate the neural mechanisms underlying such control circuits using a connectome-constrained spiking neural network model. Significance statementHead-direction (HD) system has been discovered in rodents for decades. But the detailed neural circuit mechanisms underlying the HD system were only described recently by studies of fruit flies on the similar HD system. However, how this fruit fly HD system involves in orientation memory was not well investigated. The present study addresses this question by investigating free moving flies in a spatial orientation working memory task. By combining neural functional experiments and neural circuit modelling, the study shows how disrupting either of the two subcircuits, one stabilizing and the other updating the neural activity, in the HD system leads to different behavioral impairments. The result suggests specific roles of the HD subcircuits in the spatial orientation working memory. Visual Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=138 SRC="FIGDIR/small/819185v2_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@101cb04org.highwire.dtl.DTLVardef@b7ac8borg.highwire.dtl.DTLVardef@a10a41org.highwire.dtl.DTLVardef@a8691e_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2019

The Fruit Fly Brain Observatory: From Structure to Function

The fruit fly is a key model organism for studying the activity of interconnected brain circuits. A large scattered global research community of neurobiologists and neurogeneticists, computational and theoretical neuroscientists, and computer scientists and engineers has been developing a vast trove of experimental and modeling data that has yet to be distilled into new knowledge and understanding of the functional logic of the brain. Developing open shared models, modelling tools and data repositories that can be accessed from anywhere in the world is the necessary engine for accelerating our understanding of how the brain works.\n\nTo that end we developed the Fruit Fly Brain Observatory (FFBO), the next generation open-source platform to support open, collaborative Drosophila neuroscience research. FFBO provides a (i) hub for storing and integrating fruit fly brain research data from multiple data sources worldwide, (ii) unified repository of tools and methods to build, emulate and compare fruit fly brain models in health and disease, and (iii) an open framework for fruit fly brain data processing and model execution. FFBO provides access to application tools for visualizing, configuring, simulating and analyzing computational models of brain circuits of the (i) cell type map, (ii) connectome, (iii) synaptome, and (iv) activity map using intuitive queries in plain English. Tools are provided to extract the function inherent in these structural maps. All applications can be accessed with any modern browser.

neuroscience↗

bioRxiv2018

A single-cell level and connectome-derived computational model of the Drosophila brain

Computer simulations play an important role in testing hypotheses, integrating knowledge, and providing predictions of neural circuit functions. While considerable effort has been dedicated into simulating primate or rodent brains, the fruit fly (Drosophila melanogaster) is becoming a promising model animal in computational neuroscience for its small brain size, complex cognitive behavior, and abundancy of data available from genes to circuits. Moreover, several Drosophila connectome projects have generated a large number of neuronal images that account for a significant portion of the brain, making a systematic investigation of the whole brain circuit possible. Supported by FlyCircuit (http://www.flycircuit.tw), one of the largest Drosophila neuron image databases, we began a long-term project with the goal to construct a whole-brain spiking network model of the Drosophila brain. In this paper, we report the outcome of the first phase of the project. We developed the Flysim platform, which 1) identifies the polarity of each neuron arbor, 2) predicts connections between neurons, 3) translates morphology data from the database into physiology parameters for computational modeling, 4) reconstructs a brain-wide network model, which consists of 20,089 neurons and 1,044,020 synapses, and 5) performs computer simulations of the resting state. We compared the reconstructed brain network with a randomized brain network by shuffling the connections of each neuron. We found that the reconstructed brain can be easily stabilized by implementing synaptic short-term depression, while the randomized one exhibited seizure-like firing activity under the same treatment. Furthermore, the reconstructed Drosophila brain was structurally and dynamically more diverse than the randomized one and exhibited both Poisson-like and patterned firing activities. Despite being at its early stage of development, this single-cell level brain model allows us to study some of the fundamental properties of neural networks including network balance, critical behavior, long-term stability, and plasticity.

bioinformatics↗