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bioRxiv2017

Use of a Neural Circuit Probe to Validate in silico Predictions of Inhibitory Connections

Understanding how neuronal signals propagate in local network is an important step in understanding information processing. As a result, spike trains recorded with Multi-electrode Arrays (MEAs) have been widely used to study behaviors of neural connections. Studying the dynamics of neuronal networks requires the identification of both excitatory and inhibitory connections. The detection of excitatory relationships can robustly be inferred by characterizing the statistical relationships of neural spike trains. However, the identification of inhibitory relationships is more difficult: distinguishing endogenous low firing rates from active inhibition is not obvious. In this paper, we propose an in silico interventional procedure that makes predictions about the effect of stimulating or inhibiting single neurons on other neurons, and thereby gives the ability to accurately identify inhibitory causal relationships. To experimentally test these predictions, we have developed a Neural Circuit Probe (NCP) that delivers drugs transiently and reversibly on individually identified neurons to assess their contributions to the neural circuit behavior. With the help of NCP, three inhibitory connections identified by our in silico modeling were validated through real interventional experiments. Together, these methods provide a basis for mapping complete neural circuits.

neuroscience↗

bioRxiv2025

A neural circuit targeting technique for investigating functional input-output organization in the nervous system

Neurons communicate information across circuits and the function of cells in these circuits is determined by both the afferent inputs they receive and the efferent outputs they send to other brain regions1,2. To study the activity and function of specific neuronal populations, transneuronal anterograde3 and retrograde4-6 viral approaches have been employed to define neural circuit elements by inputs or outputs, respectively. However, what is missing is a way to study the function of neurons based on both their inputs and outputs. Applying a combination of multiple recombinases and transneuronal anterograde/retrograde viruses, we developed a technique called input-output Projection-based INtersectional Circuit-tagging Enabled by Recombinases (PINCER) to target specific neuronal cell types and investigate functional input-output organization in neural circuits. We show the logic and application of this technique with in vivo calcium imaging and optogenetic approaches to reveal the distinct functions and neural dynamics of connectivity defined neuronal populations in the amygdala for emotional processing. Specifically, PINCER allowed the parsing of valence and salience functions of the amygdala to reveal an input-output cell type selectively mediating aversive memory formation. This technique allows neuroscientists to identify novel subclasses of cells based on their combinatorial input-output anatomical connectivity, providing a tool for fine dissection of the functional properties of neural circuits.

neuroscience↗

bioRxiv2023

Semantic Representation of Neural Circuit Knowledge in Caenorhabditis elegans

In modern biology, new knowledge is generated quickly, making it challenging for researchers to efficiently acquire and synthesise new information from the large volume of primary publications. To address this problem, computational approaches that generate machine-readable representations of scientific findings in the form of knowledge graphs have been developed. These representations can integrate different types of experimental data from multiple papers and biological knowledge bases in a unifying data model, providing a complementary method to manual review for interacting with published knowledge. The Gene Ontology Consortium (GOC) has created a semantic modelling framework that extends individual functional gene annotations to structured descriptions of causal networks representing biological processes (Gene Ontology Causal Activity Modelling, or GO-CAM). In this study, we explored whether the GO-CAM framework could represent knowledge of the causal relationships between environmental inputs, neural circuits and behavior in the model nematode C. elegans (C. elegans Neural Circuit Causal Activity Modelling (CeN- CAM)). We found that, given extensions to several relevant ontologies, a wide variety of author statements from the literature about the neural circuit basis of egg-laying and carbon dioxide (CO2) avoidance behaviors could be faithfully represented with CeN-CAM. Through this process, we were able to generate generic data models for several categories of experimental results. We also discuss how semantic modelling may be used to functionally annotate the C. elegans connectome. Thus, Gene Ontology-based semantic modelling has the potential to support various machine-readable representations of neurobiological knowledge. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/538760v4_ufig1.gif" ALT="Figure 1"> View larger version (11K): org.highwire.dtl.DTLVardef@15ca6deorg.highwire.dtl.DTLVardef@1cf3b92org.highwire.dtl.DTLVardef@4ff138org.highwire.dtl.DTLVardef@65c908_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2019

Identification of a Central Neural Circuit that Regulates Anxiety-induced Bone Loss

The homeostasis of bone metabolism is finely regulated by the central nervous system and recent studies have suggested that mood disorders, such as anxiety, are closely related to bone metabolic abnormalities; however, our understanding of central neural circuits regulating bone metabolism is still largely limited. In this study, we first demonstrate that confined isolation of human participants under normal gravity resulted in decreased bone density and elevated anxiety levels. We then used an established mouse model to dissect the neural circuitry regulating anxiety-induced bone loss. Combining electrophysiological, optogenetic and chemogenetic approaches, we demonstrate that GABAergic neural circuitry in ventromedial hypothalamus (VMH) modulates anxiety-induced bone loss; importantly, the GABAergic input in VMHdm arose from a specific group of somatostatin neurons in the bed nucleus of the stria terminalis (BNST), which is both indispensable for anxiety-induced bone loss and able to trigger bone loss in the absence of stressors. VGLUT2 neurons in Nucleus tractus solitaries (NTS) and peripheral sympathetic system were employed by this BNST-VMH neural circuit to regulate anxiety-induced bone loss. Overall, we uncovered new GABAergic neural circuitry from the forebrain to hypothalamus, used in the regulation of anxiety-induced bone loss, and revealed a population of somatostatin neurons in BNST not previously implicated in bone mass regulation. These findings thus identify the underlying central neural mechanism of psychiatric disorders, such as anxiety, that influences bone metabolism at the circuit level.\n\nOne Sentence SummaryIdentification of a new GABAergic neural circuit from forebrain to hypothalamus used for regulation of anxiety-induced bone loss.

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bioRxiv2025

A canonical gated neural circuit model for flexible perceptual decisions

Flexible perceptual decision-making requires rapid, context-dependent adjustments, yet the neural circuit mechanisms underlying its parsimonious representations remain unclear. Here, we propose a minimal mean-field neural circuit model that integrates sensory evidence and selects actions via distributed neuronal encoding, guided by data from a task that dissociates perceptual choice from motor response - abstract perceptual decision-making. The models nonlinear gating of action selective (AS) neurons replicates parietal cortical activity observed during task performance. Critically, recurrent excitation within the evidence integration (EI) population supports sensory evidence accumulation, working memory for sequential sampling, and reward rate optimisation. Moreover, the dynamics of EI and AS neuronal activities in the same model respectively mirror parietal neuronal activities related to sensory evidence encoding and ramping-to-threshold firing in a separate reaction-time task, while suggesting that decision readout engages both neuronal populations. The model also predicts decision interference in a novel two-stage decision version of the task, accounting for choice accuracy decrements observed in other experiments while predicting slower decisions. Together, these findings propose a minimal mean-field circuit-level mechanism unifying perceptual, memory-based, and abstract decision-making.

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bioRxiv2026

A canonical neural circuit for motion direction estimation

Abundant experimental data support the view that motion detection is a canonical neural operation, common to a wide variety of species and different stages in the visual stream. However, existing motion models lack this generality, as their predictive power tends to be restricted to some specific visual phenomena, brain regions, or animal taxa. Here, we introduce a neural circuit model that only uses cells selective to cardinal directions, with elements that can be interpreted as emulating some dendritic processes, and we show how it is able to predict a wide range of visual motion data, from basic properties to hitherto unexplained phenomena, surpassing state-of-the-art models implemented as deep neural networks with millions of parameters. The direction-selective cells that our model relies on are present in the visual systems of many classes of animals, from insects to primates, and in visual pathway structures going from the retina to the cortex, suggesting that the proposed model could serve as a canonical neural circuit for estimating motion direction.

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bioRxiv2022

The Serotonergic Neural Circuit Between The Dorsal Raphe Nucleus And The Basolateral Amygdala Is Implicated In Modulating The Arousal from Sevoflurane Anesthesia

Although some advancements concerning the arousal involved in mediating the delayed emergency from general anesthesia, which will lead to the serious complications, had been made, the role by arousal in modulating in delayed emergency still remains to be unclear. In our models, based on our previous working that activation of the 5-Hydroxytryptamine (5-HT) neurons in the dorsal raphe nucleus (DRN) by optogenetics can significantly reduce the emergency time by activating arousal pathway, we further test whether the serotonergic neural circuit between the DRN and the basolateral amygdala (BLA) is implicated in modulating the arousal from the sevoflurane anesthesia and the emergency time of sevoflurane anesthesia by the pharmacological, optogenetics and fiber photometry. Our findings showed that whether the serotonergic neural circuit between the DRN and the basolateral amygdala (BLA) plays a key role in modulating the arousal from the sevoflurane anesthesia and the emergency time of sevoflurane anesthesia. Based on the serotonergic neural circuit, the 5-HT 1 A receptor is of great significance to mediate the arousal and the emergency time of the sevoflurane anesthesia. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/511924v2_ufig1.gif" ALT="Figure 1"> View larger version (58K): org.highwire.dtl.DTLVardef@34621eorg.highwire.dtl.DTLVardef@115ae09org.highwire.dtl.DTLVardef@54eb12org.highwire.dtl.DTLVardef@11515ff_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure1C_FLOATNO C_FIG

neuroscience↗

bioRxiv2020

Convergence of distinct subpopulations of mechanosensory neurons onto a neural circuit that elicits grooming

Diverse subpopulations of mechanosensory neurons detect different mechanical forces and influence behavior. How these subpopulations connect with central circuits to influence behavior remains an important area of study. We previously discovered a neural circuit that elicits grooming of the Drosophila melanogaster antennae that is activated by an antennal mechanosensory chordotonal organ, the Johnstons organ (JO) (Hampel et al., 2015). Here, we describe anatomically and physiologically distinct JO mechanosensory neuron subpopulations and define how they interface with the circuit that elicits antennal grooming. We show that the subpopulations project to distinct zones in the brain and differ in their responses to mechanical stimulation of the antennae. Each subpopulation elicits grooming through direct synaptic connections with a single interneuron in the circuit, the dendrites of which span the different mechanosensory afferent projection zones. Thus, distinct JO subpopulations converge onto the same neural circuit to elicit a common behavioral response.

neuroscience↗

bioRxiv2021

Long-term precision editing of neural circuits using engineered gap junction hemichannels

The coordination of activity between brain cells is a key determinant of neural circuit function; nevertheless, approaches that selectively regulate communication between two distinct cellular components of a mammalian circuit remain sparse. To address this gap, we developed a novel class of gap junctions by selectively engineering two connexin proteins found in Morone americana (white perch fish): connexin34.7 (Cx34.7) and connexin35 (Cx35). By iteratively exploiting protein mutagenesis, a novel in vitro assay of connexin docking, and computational modeling of connexin hemichannel interactions, we uncovered a pattern of structural motifs that contribute to hemichannel docking compatibility. Targeting these motifs, we designed Cx34.7 and Cx35 hemichannels that dock with each other, but not with themselves, nor with other major connexins expressed in the mammalian central nervous system. We validated these hemichannels in vivo using C. elegans and mice, demonstrating that they can facilitate communication across neural circuits composed of pairs of distinct cell types and modify behavior accordingly. Thus, we establish a potentially translational approach, Long-term integration of Circuits using connexins (LinCx), for context-precise circuit-editing with unprecedented spatiotemporal specificity in mammals.

neuroscience↗

bioRxiv2018

ORGaNICs: A Canonical Neural Circuit Computation

A theory of cortical function is proposed, based on a family of recurrent neural circuits, called ORGaNICs (Oscillatory Recurrent GAted Neural Integrator Circuits). Here, the theory is applied to working memory and motor control. Working memory is a cognitive process for temporarily maintaining and manipulating information. Most empirical neuroscience research on working memory has measured sustained activity during delayed-response tasks, and most models of working memory are designed to explain sustained activity. But this focus on sustained activity (i.e., maintenance) ignores manipulation, and there are a variety of experimental results that are difficult to reconcile with sustained activity. ORGaNICs can be used to explain the complex dynamics of activity, and ORGaNICs can be use to manipulate (as well as maintain) information during a working memory task. The theory provides a means for reading out information from the dynamically varying responses at any point in time, in spite of the complex dynamics. When applied to motor systems, ORGaNICs can be used to convert spatial patterns of premotor activity to temporal profiles of motor control activity: different spatial patterns of pre-motor activity evoke different motor control dynamics. ORGaNICs offer a novel conceptual framework; Rethinking cortical computation in these terms should have widespread implications, motivating a variety of experiments.

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bioRxiv2021

Economic Choices under Simultaneous or Sequential Offers Rely on the Same Neural Circuit

A series of studies in which monkeys chose between two juices offered in variable amounts identified in the orbitofrontal cortex (OFC) different groups of neurons encoding the value of individual options (offer value), the binary choice outcome (chosen juice) and the chosen value. These variables capture both the input and the output of the choice process, suggesting that the cell groups identified in OFC constitute the building blocks of a decision circuit. Several lines of evidence support this hypothesis. However, in previous experiments offers were presented simultaneously, raising the question of whether current notions generalize to when goods are presented or are examined in sequence. Recently, Ballesta and Padoa-Schioppa (2019) examined OFC activity under sequential offers. An analysis of neuronal responses across time windows revealed that a small number of cell groups encoded specific sequences of variables. These sequences appeared analogous to the variables identified under simultaneous offers, but the correspondence remained tentative. Thus in the present study we examined the relation between cell groups found under sequential versus simultaneous offers. We recorded from the OFC while monkeys chose between different juices. Trials with simultaneous and sequential offers were randomly interleaved in each session. We classified cells in each choice modality and we examined the relation between the two classifications. We found a strong correspondence - in other words, the cell groups measured under simultaneous offers and under sequential offers were one and the same. This result indicates that economic choices under simultaneous or sequential offers rely on the same neural circuit. Significance StatementResearch in the past 20 years has shed light on the neuronal underpinnings of economic choices. A large number of results indicates that decisions between goods are formed in a neural circuit within the orbitofrontal cortex (OFC). In most previous studies, subjects chose between two goods offered simultaneously. Yet, in daily situations, goods available for choice are often presented or examined in sequence. Here we recorded neuronal activity in the primate OFC alternating trials under simultaneous and under sequential offers. Our analyses demonstrate that the same neural circuit supports choices in the two modalities. Hence current notions on the neuronal mechanisms underlying economic decisions generalize to choices under sequential offers.

neuroscience↗

bioRxiv2018

Identification of peripheral neural circuits that regulate heart rate using optogenetic and viral vector strategies

Heart rate is under the precise control of the autonomic nervous system. However, the wiring of peripheral neural circuits that regulate heart rate is poorly understood. Here, we developed a clearing-imaging-analysis pipeline to visualize innervation of intact hearts in 3D and employed a multi-technique approach to map parasympathetic and sympathetic neural circuits that control heart rate in mice. We anatomically and functionally identify cholinergic neurons and noradrenergic neurons in an intrinsic cardiac ganglion and the stellate ganglia, respectively, that project to the sinoatrial node. We also report that the heart rate response to optogenetic versus electrical stimulation of the vagus nerve displays different temporal characteristics and that vagal afferents enhance parasympathetic and reduce sympathetic tone to the heart via central mechanisms. Our findings provide new insights into neural regulation of heart rate, and our methodology to study cardiac circuits can be readily used to interrogate neural control of other visceral organs.

neuroscience↗

bioRxiv2026

NeuroGraphBench: Interacting with Drosophila Connectomes at Scale for Exploring the Functional Logic of Neural Circuits

Drosophila connectomic datasets provide increasingly comprehensive maps of neuronal morphology and synaptic connectivity, offering an unprecedented opportunity to explore the structural organization of its neural circuits. This calls for designing automated tools to interact with connectomic datasets at scale for efficiently exploring structural features embedded in the vast amount of data. Yet the central challenge remains the understanding of the functional logic of neural circuits. In order to understand how elements of the functional logic may emerge from this structural organization, it is critical to (i) characterize the objects in the natural environment in which brain circuits operate, and (ii) formulate how brain circuits represent and process the defined objects in the natural environment. To develop and demonstrate a methodology for these requirements, we focus on the Drosophila looming-evoked escape pathway. We modeled the trajectory of looming objects that are on a collision course (direct-hits) or pass-by the fly (near-misses): their projected images on the retina can be characterized by the solid angle (angular size) and elevation. We then analyzed the pathway's morphology across the OpticLobe, Hemibrain, and FlyWire connectome datasets. By abstracting their sub-neuronal structure and retinotopic organization, we constructed an executable circuit model that maps each structural element to a processing block. We demonstrate that this model separates direct hits from near misses well before the angular size could tell them apart. To accelerate the connectomic analysis step, we developed a Python toolset with an agentic, code-free workspace interface called NeuroGraphBench (NGB). NGB provides four composable morphology-analysis primitives and an AI agent that composes them to interactively respond to natural-language queries aided by visualization on an interactive 3D canvas. Thus, NGB automates tedious and repetitive tasks to enable faster and scalable connectomic exploration, keeping human reasoning, instead of writing code, at the center of an open-ended research inquiry.

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bioRxiv2024

Reversal of Obesity by Enhancing Slow-wave Sleep via a Prokineticin Receptor Neural Circuit

Obese subjects often exhibit hypersomnia accompanied by severe sleep fragmentation, while emerging evidence suggests that poor sleep quality promotes overeating and exacerbates diet-induced obesity (DIO). However, the neural circuit and signaling mechanism underlying the reciprocal control of appetite and sleep is yet not elucidated. Here, we report a neural circuit where prokineticin receptor 2 (PROKR2)-expressing neurons within the parabrachial nucleus (PBN) of the brainstem received direct projections from neuropeptide Y receptor Y2 (NPY2R)-expressing neurons within the lateral preoptic area (LPO) of the hypothalamus. The RNA-Seq results revealed Prokr2 in the PBN is the most regulated GPCR signaling gene that is responsible for comorbidity of obesity and sleep dysfunction. Furthermore, those NPY2RLPO neurons are minimally active during NREM sleep and maximally active during wakefulness and REM sleep. Activation of the NPY2RLPO[->]PBN circuit or the postsynaptic PROKR2PBN neurons suppressed feeding of a high-fat diet and abrogated morbid sleep patterns in DIO mice. Further studies showed that genetic ablation of the PROKR2 signaling within PROKR2PBN neurons alleviated the hyperphagia and weight gain, and restored sleep dysfunction in DIO mice. We further discovered pterostilbene, a plant-derived stilbenoid, is a powerful anti-obesity and sleep-improving agent, robustly suppressed hyperphagia and promoted reconstruction of a healthier sleep architecture, thereby leading to significant weight loss. Collectively, our results unveil a neural mechanism for the reciprocal control of appetite and sleep, through which pterostilbene, along with a class of similarly structured compounds, may be developed as effective therapeutics for tackling obesity and sleep disorders.

neuroscience↗

bioRxiv2024

A neural circuit for context-dependent multimodal signaling in Drosophila

Many animals, including humans, produce multimodal displays by combining acoustic with visual or vibratory signals [1-4]. However, the neural circuits that coordinate the production of multiple signals in a context-dependent manner are unknown. Multimodal behaviors could be produced by parallel circuits that independently integrate the external cues that trigger each signal. We find that multimodal signals in Drosophila are driven by a single circuit that integrates external sensory cues with internal motivational state and circuit dynamics. Drosophila males produce air-borne song and substrate-borne vibration during courtship and previous studies have identified neurons that drive courtship and singing, but the contexts and circuits that drive vibrations and coordinate multimodal signaling were not known [5-11]. We show that males produce song and vibration in distinct, largely non-overlapping contexts and that brain neurons that drive song also drive vibrations with cell-type specific dynamics and via separate pre-motor pathways. This circuit also coordinates multimodal signaling with ongoing behavior, namely locomotion, to drive vibrations only when the males vibrations can reach the female. A shared circuit facilitates the control of signal dynamics by external cues and motivational state through shared mechanisms like recurrence and mutual inhibition. A proof-of-concept circuit model shows that these motifs are sufficient to explain the behavioral dynamics. Our work shows how simple motifs can be combined in a single neural circuit to select and coordinate multiple behaviors.

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bioRxiv2026

Neural circuits regulating social dominance implement a strategy predicted by evolutionary game theory

Social conflict is a fundamental challenge for all animals and determines access to critical resources like mates and food. Evolutionary game theory predicts that natural selection should yield competitive strategies that balance the benefits and costs of social conflict. However, whether such strategies are embedded within the neural circuits that regulate aggression remains unclear. Here, we identify a neural circuit regulating the decision to flee during fighting in male Drosophila and show that the onset of defeat is governed by a probabilistic strategy predicted by evolutionary game theory. This mechanism arises from the inhibition of Tk-GAL4FruM neurons that promote aggressive arousal in males. Inhibition is mediated by a mushroom body circuit involving PPL1 dopaminergic neurons and V2 mushroom body output neurons, both classically associated with aversive learning. Silencing this circuit disrupts the onset of defeat, while activating it induces rapid defeat. Conversely, activation of reward-encoding PAM dopaminergic neurons promotes winning, revealing a dual role for dopamine in shaping contest dynamics. Finally, we find that internal state variables such as hunger and motivation shift the defeat onset probability distribution, consistent with game theory predictions of how payoff modulates fighting persistence. Together, our results provide direct evidence that evolutionary strategies based on payoff, long described by game theory, are implemented as circuit-level computations that regulate aggression.

neuroscience↗

bioRxiv2022

Compression supports low-dimensional representations of behavior across neural circuits

Dimensionality reduction, a form of compression, can simplify representations of information to increase efficiency and reveal general patterns. Yet, this simplification also forfeits information, thereby reducing representational capacity. Hence, the brain may benefit from generating both compressed and uncompressed activity, and may do so in a heterogeneous manner across diverse neural circuits that represent low-level (sensory) or high-level (cognitive) stimuli. However, precisely how compression and representational capacity differ across the cortex remains unknown. Here we predict different levels of compression across regional circuits by using random walks on networks to model activity flow and to formulate rate-distortion functions, which are the basis of lossy compression. Using a large sample of youth (n = 1, 040), we test predictions in two ways: by measuring the dimensionality of spontaneous activity from sensorimotor to association cortex, and by assessing the representational capacity for 24 behaviors in neural circuits and 20 cognitive variables in recurrent neural networks. Our network theory of compression predicts the dimensionality of activity (t = 12.13, p < 0.001) and the representational capacity of biological (r = 0.53, p = 0.016) and artificial (r = 0.61, p < 0.001) networks. The model suggests how a basic form of compression is an emergent property of activity flow between distributed circuits that communicate with the rest of the network.

neuroscience↗

bioRxiv2020

A neural circuit for flexible control of persistent behavioral states

To adapt to their environments, animals must generate behaviors that are closely aligned to a rapidly changing sensory world. However, behavioral states such as foraging or courtship typically persist over long time scales to ensure proper execution. It remains unclear how neural circuits generate persistent behavioral states while maintaining the flexibility to select among alternative states when the sensory context changes. Here, we elucidate the functional architecture of a neural circuit controlling the choice between roaming and dwelling states, which underlie exploration and exploitation during foraging in C. elegans. By imaging ensemble-level neural activity in freely-moving animals, we identify stable, circuit-wide activity patterns corresponding to each behavioral state. Combining circuit-wide imaging with genetic analysis, we find that mutual inhibition between two antagonistic neuromodulatory systems underlies the persistence and mutual exclusivity of the opposing network states. Through machine learning analysis and circuit perturbations, we identify a sensory processing neuron that can transmit information about food odors to both the roaming and dwelling circuits and bias the animal towards different states in different sensory contexts, giving rise to context-appropriate state transitions. Our findings reveal a potentially general circuit architecture that enables flexible, sensory-driven control of persistent behavioral states.

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