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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2025

Hierarchical Neural Circuit Theory of Normalization and Inter-areal Communication

The primate brain exhibits a hierarchical, modular architecture with conserved microcircuits executing canonical computations across reciprocally connected cortical areas. We present a hierarchical neural circuit theory with feedback connections that dynamically implements divisive normalization across its hierarchy. In a two-stage instantiation (V1 {leftrightarrow} V2), increasing feedback from V2 to V1 amplifies responses in both areas. We analytically derive power spectra (V1) and coherence spectra (V1-V2) as functions of frequency (f), and compare them with experimental observations: peaks in both spectra shift to higher frequencies with increased stimulus contrast, and power decays as 1/f4 at high frequencies. The closed-form spectra are validated against direct stochastic simulation of the full nonlinear circuit across contrasts. The theory further predicts distinctive spectral signatures of feedback and input gain modulation. Crucially, the theory offers a unified view of inter-areal communication, defined as the ability to linearly predict neural activity in one brain area from neural activity in another, with emergent features consistent with empirical observations of both communication subspaces and inter-areal coherence. It admits a low-dimensional communication subspace, where inter-areal communication is lower-dimensional than within-area communication. It further predicts that: i) increasing feedback strength enhances inter-areal communication and diminishes within-area communication, without altering the subspace dimensionality; ii) communication is strongest, and the subspace dimensionality lowest, at the frequencies at which V1-V2 coherence peaks; iii) normalization reduces the subspace dimensionality. Finally, a three-area (V1 {leftrightarrow} V4 and V1 {leftrightarrow} V5) instantiation of the theory demonstrates that differential feedback from higher to lower cortical areas dictates their dynamic functional connectivity. Altogether, our theory provides a robust and analytically tractable framework for generating experimentally-testable predictions about normalization, inter-areal communication, and functional connectivity.

neuroscience↗

bioRxiv2022

Decomposing neural circuit function into information processing primitives

Cognitive functions arise from the coordinated activity of neural populations distributed over large-scale brain networks. However, it is challenging to understand and measure how specific aspects of neural dynamics translate into operations of information processing, and, ultimately, cognitive functions. An obstacle is that simple circuit mechanisms-such as self-sustained or propagating activity and nonlinear summation of inputs-do not directly give rise to high-level functions. Nevertheless, they already implement simple transformations of the information carried by neural activity. Here, we propose that distinct neural circuit functions, such as stimulus representation, working memory, or selective attention stem from different combinations and types of low-level manipulations of information, or information processing primitives. To test this hypothesis, we combine approaches from information theory with computational simulations of canonical neural circuits involving one or more interacting brain regions that emulate well-defined cognitive functions. More specifically, we track the dynamics of information emergent from dynamic patterns of neural activity, using suitable quantitative metrics to detect where and when information is actively buffered ("active information storage"), transferred ("information transfer") or non-linearly merged ("information modification"), as possible modes of low-level processing. We find that neuronal subsets maintaining representations in working memory or performing attention-related gain modulation are signaled by their boosted involvement in operations of active information storage or information modification, respectively. Thus, information dynamics metrics, beyond detecting which network units participate in cognitive processing, also promise to specify how and when they do it, i.e., through which type of primitive computation, a capability that may be exploited for the parsing of actual experimental recordings.

neuroscience↗

bioRxiv2026

Gene regulatory co-option drives birdsong neural circuit specialization

As animals evolve complex motor skills, they acquire more diverse supporting motor circuits in their nervous systems. Yet the molecular mechanisms driving motor circuit evolution remain poorly understood. Birdsong, a learned complex motor skill with parallels to human speech, is controlled by a dedicated neural circuit - the song system - that is distinguished from nearby sensorimotor regions by molecular, physiological, and connectivity specializations. By profiling gene expression and chromatin accessibility in the songbird brain, we found that each projection neuron type in the song system has a molecularly similar sister neuron type in adjacent non-song regions; these sister neurons lack specialized gene expression and are transcriptionally similar to neurons in the chicken brain. The gene regulatory networks (GRNs) controlled by transcription factors MAFB and EMX2, typically active in fast-spiking interneurons and astrocytes, are specifically active in song-dedicated extratelencephalic projection neurons. Furthermore, the heterologous expression of MAFB or EMX2 in chicken projection neurons was sufficient to drive expression programs characteristic of song neurons. These results support a model in which song-dedicated neurons emerged from ancestral neural types in part through the co-option of GRNs active in other cellular contexts, providing a genetic mechanism underlying the evolution of birdsong.

neuroscience↗

bioRxiv2023

Neural circuit mechanisms underlying context-specific halting in Drosophila.

Walking is a complex motor program involving coordinated and distributed activity across the brain and the spinal cord. Halting appropriately at the correct time is a critical but often overlooked component of walking control. While recent studies have delineated specific genetically defined neuronal populations in the mouse brainstem that drive different types of halting1-3, the underlying neural circuit mechanisms responsible for overruling the competing walking-state neural activity to generate context-appropriate halting, remain unclear. Here, we elucidate two fundamental mechanisms by which Drosophila implement context-appropriate halting. The first mechanism ("walk-OFF" mechanism) relies on GABAergic neurons that inhibit specific descending walking commands in the brain, while the second mechanism ("brake" mechanism) relies on excitatory cholinergic neurons in the nerve-cord that lead to an active arrest of stepping movements. Using connectome-informed models4-6 and functional studies, we show that two neuronal types that deploy the "walk-OFF" mechanism inhibit distinct populations of walking-promotion neurons, leading to differential halting of forward-walking or steering. The "brake" neurons on the other hand, override all walking commands by simultaneously inhibiting descending walking promoting pathways and increasing the resistance at the leg-joints leading to an arrest of leg movements in the stance phase of walking. We characterized two ethologically relevant behavioral contexts in which the distinct halting mechanisms were used by the animal in a mutually exclusive manner: the "walk-OFF" pathway was engaged for halting during feeding, and the "brake" pathway was engaged for halting during grooming. Furthermore, this knowledge of the neural targets and mechanisms for halting, allowed us to use connectomics to predict novel halting pathways that could be relevant in other behavioral contexts.

neuroscience↗

bioRxiv2020

Dorsal raphe nuclei to anterior cingulate cortex 5-HTergic neural circuit is implicated in consolation-like behaviors and sociability in mandarin voles

Consolation is a common response to the distress of others in humans and some social animals, but the neural mechanisms underlying this behavior are not well characterized. By using socially monogamous mandarin voles, we found that optogenetic or chemogenetic inhibition of 5-HTergic neurons in the dorsal raphe nucleus (DR) or optogenetic inhibition of 5-HT terminals in the anterior cingulate cortex (ACC) significantly decreased allogrooming time in the consolation test and reduced sociability in the three-chamber test. The release of 5-HT within the ACC and the activity of DR neurons were significantly increased during allogrooming, sniffing and social approaching. Finally, we found that the activation of 5-HT1A receptors in the ACC was sufficient to reverse consolation and sociability deficits induced by the chemogenetic inhibition of 5-HTergic neurons in the DR. Our study provided first direct evidence that DR-ACC 5-HTergic neural circuit is implicated in consolation-like behaviors and sociability.

neuroscience↗

bioRxiv2023

Neural circuits underlying context-dependent competition between defensive actions in Drosophila larva

To ensure their survival, animals must be able to respond adaptively to threats within their environment. However, the precise neural circuit mechanisms that underlie such flexible defensive behaviors remain poorly understood. Using neuronal manipulations, machine-learning-based behavioral detection, Electron Microscopy (EM) connectomics and calcium imaging in Drosophila larva, we have mapped the second-order interneurons differentially involved in the competition between different defensive actions and the main pathways to the motor side putatively involved in inhibiting startle-type behaviors and promoting escape behaviors in a context dependent manner. We found that mechanosensory stimulation modulates the nociceptive escape sequences and inhibits C-shape bends and Rolls in favor of startle-like behaviors. This suggests a competition between mechanosensory-induced startle responses and escape behaviors. Structural and functional connectivity revealed that the second order interneurons receive their main input from projection neurons that integrate mechanosensory and nociceptive stimuli. The analysis of their postsynaptic connectivity in EM revealed that they make indirect connections to the pre-motor and motor neurons. Finally, we identify a pair of descending neurons that could promote modulate the escape sequence and promote startle behaviors. Altogether, these results characterize the pathways involved in the Startle and Escape competition, modulated by the sensory context.

neuroscience↗

bioRxiv2025

Scanning and active sampling behaviours emerge from conserved insect neural circuits

Navigating insects often pause and rotate to sample their surroundings, behaviours termed scanning. These and other active sampling behaviours embody navigational uncertainty, and are key for spatial learning, yet their neural basis remains unclear and existing models impose scanning behaviours rather than explaining its emergence. Here, we show that desert ants scanning dynamics can emerge spontaneously from the same conserved neural circuits used for goal-directed navigation, without requiring a specialized scanning module. We built a biologically grounded model combining central complex (CX) steering and lateral accessory lobe (LAL) oscillators, and added a downstream stochastic inhibition of forward speed. This minimal system produced diverse, realistic scan dynamics; saccades, fixations and reversals, whose features were qualitatively compared to high-speed video recordings of Melophorus bagoti scanning. Detailed analysis of these natural scans confirmed model predictions, including how scan structure depends on oscillator phase, goal-heading deviation, and navigational uncertainty. Furthermore, the model reveals that simple modulation of forward speed unifies a broad range of behaviours across ant species, from dashes to smooth oscillatory trajectories to pirouettes and voltes. Crucially, this model suggests a distributed control principle where forward speed acts as a single adjustable parameter, for both individuals and through evolution, to regulate the balance between goal-driven exploitation and information-seeking exploration.

animal behavior and cognition↗

bioRxiv2024

Neural Circuit Underlying Individual differences in Visual Escape Habituation

Emotions, like fear, are internal states enabling organisms to effectively confront environmental threats. When repeatedly exposed to predators, individuals show divergent adaptive responses. However, the neural circuit mechanisms underlying individual differences in to repeated threats remain largely unknown. Here, we identify two distinct types of visual escape--consistent escape (T1) and rapid habituation (T2) -- with unambiguous arousal states to repetitive threat stimuli. We systematically investigate distinct pathways originating from the superior colliculus (SC) and insula that project to the basolateral amygdala (BLA), with relay stations in the mediodorsal thalamus (MD) and ventral tegmental area (VTA), mediating T1 and T2 behavioral types. Additionally, we identify the MD as a critical hub integrating SC and insula inputs, projecting to the BLA and contributing to reduced arousal and attenuated defensive behaviors against looming stimuli. Our findings offer significant insights into the mechanisms of internal states, arousal modulation, and behavioral adaptability. O_FIG O_LINKSMALLFIG WIDTH=178 HEIGHT=200 SRC="FIGDIR/small/611366v1_ufig1.gif" ALT="Figure 1"> View larger version (77K): org.highwire.dtl.DTLVardef@164df34org.highwire.dtl.DTLVardef@17a01e6org.highwire.dtl.DTLVardef@112de44org.highwire.dtl.DTLVardef@1be6aea_HPS_FORMAT_FIGEXP M_FIG Graph Abstract C_FIG In briefWe identify two distinct visual escape behaviors--consistent escape (T1) and rapid habituation (T2)--linked to unique SC and insula pathways projecting to the BLA via MD and VTA. The MD acts as a hub integrating sensory inputs to modulate arousal and defensive responses. These findings advance our understanding of the neural mechanisms driving internal states, arousal modulation, and behavioral adaptability. Highlights[bullet] We identify two distinct visual escape behaviors with unambiguous arousal states--consistent escape (T1) and rapid habituation (T2) --to repeated looming stimuli. [bullet]The SC-VTA-BLA pathway mediated T1 behavioral type, while the SC-MD-BLA pathway mediated T2 type. [bullet]MD as a central hub integrating SC and insula inputs to modulate arousal and defensive behavioral adaptation.

neuroscience↗

bioRxiv2017

Altered topology of neural circuits in congenital prosopagnosia

Using a novel fMRI-based inter-subject functional correlation (ISFC) approach, which isolates stimulus-locked inter-regional correlation patterns, we compared the cortical topology of the neural circuit for face processing in participants with congenital prosopagnosia (CP) and matched controls. Whereas the anterior temporal lobe served as the major network hub for face processing in controls, this was not the case for the CPs. Instead, this group evinced hyper-connectivity in posterior regions of the visual cortex, mostly associated with the lateral occipital and the inferior temporal cortices. Moreover, the extent to which the network organization was atypical differed as a function of the severity of the face recognition deficit. These results offer new insights into the perturbed cortical topology in CP, which may serve as the underlying neural basis of the behavioral deficits typical of this disorder. The approach adopted here has the potential to uncover altered topologies in other neurodevelopmental disorders, as well.\n\nSignificance StatementCongenital prosopagnosia (CP; face blindness), a developmental deficit in face recognition, is thought to affect up to 3% of the population. Understanding its neural basis is challenging as there is no obvious deficit on conventional structural or functional MRI scans. Using an innovative, fMRI-based inter-subject correlation approach geared towards tracking inter-regional stimulus-locked brain activation, the present study uncovers marked topological differences in a distributed brain network of higher-order visual regions in CP relative to controls. Alteration in topology also differs as a function of the severity of the deficit. These findings shed new light on the neural perturbations underlying CP, and the analytic approach we have adopted may have utility in elucidating the neural basis of other neurodevelopmental disorders such as dyslexia or amusia.

neuroscience↗

bioRxiv2024

A recurrent neural circuit in Drosophila deblurs visual inputs

A critical goal of vision is to detect changes in light intensity, even when these changes are blurred by the spatial resolution of the eye and the motion of the animal. Here we describe a recurrent neural circuit in Drosophila that compensates for blur and thereby selectively enhances the perceived contrast of moving edges. Using in vivo, two-photon voltage imaging, we measured the temporal response properties of L1 and L2, two cell types that receive direct synaptic input from photoreceptors. These neurons have biphasic responses to brief flashes of light, a hallmark of cells that encode changes in stimulus intensity. However, the second phase was often much larger than the first, creating an unusual temporal filter. Genetic dissection revealed that recurrent neural circuitry strongly shapes the second phase of the response, informing the structure of a dynamical model. By applying this model to moving natural images, we demonstrate that rather than veridically representing stimulus changes, this temporal processing strategy systematically enhances them, amplifying and sharpening responses. Comparing the measured responses of L2 to model predictions across both artificial and natural stimuli revealed that L2 tunes its properties as the model predicts in order to deblur images. Since this strategy is tunable to behavioral context, generalizable to any time-varying sensory input, and implementable with a common circuit motif, we propose that it could be broadly used to selectively enhance sharp and salient changes.

neuroscience↗

bioRxiv2024

Divergent neural circuits for proprioceptive and exteroceptive sensing of the Drosophila leg

Somatosensory neurons provide the nervous system with information about mechanical forces originating inside and outside the body. Here, we use connectomics from electron microscopy to reconstruct and analyze neural circuits downstream of the largest somatosensory organ in the Drosophila leg, the femoral chordotonal organ (FeCO). The FeCO has been proposed to support both proprioceptive sensing of the flys femur-tibia joint and exteroceptive sensing of substrate vibrations, but it was unknown which sensory neurons and central circuits contribute to each of these functions. We found that different subtypes of FeCO sensory neurons feed into distinct proprioceptive and exteroceptive pathways. Position- and movement-encoding FeCO neurons connect to local leg motor control circuits in the ventral nerve cord (VNC), indicating a proprioceptive function. In contrast, signals from the vibration-encoding FeCO neurons are integrated across legs and transmitted to mechanosensory regions in the brain, indicating an exteroceptive function. Overall, our analyses reveal the structure of specialized circuits for processing proprioceptive and exteroceptive signals from the fly leg. These findings are consistent with a growing body of work in invertebrate and vertebrate species demonstrating the existence of specialized limb mechanosensory pathways for sensing external vibrations.

neuroscience↗

bioRxiv2025

Canonical recurrent neural circuits: A unified sampling machine for static and dynamic inference

AO_SCPLOWBSTRACTC_SCPLOWThe brain lives in an ever-changing world and needs to infer the dynamic evolution of latent states from noisy sensory inputs. Exploring how canonical recurrent neural circuits in the brain realize dynamic inference is a fundamental question in neuroscience. Nearly all existing studies on dynamic inference focus on deterministic algorithms, whereas cortical circuits are intrinsically stochastic, with accumulating evidence suggesting that they employ stochastic Bayesian sampling algorithms. Nevertheless, nearly all circuit sampling studies focused on static inference with fixed posterior over time instead of dynamic inference, leaving a gap between circuit sampling and dynamic inference. To bridge this gap, we study the sampling-based dynamic inference in a canonical recurrent circuit model with excitatory (E) neurons and two types of inhibitory interneurons: parvalbumin (PV) and somatostatin (SOM) neurons. We find that the canonical circuit unifies Langevin and Hamiltonian sampling to infer either static or dynamic latent states with various moving speeds. Remarkably, switching sampling algorithms and adjusting models internal latent moving speed can be realized by modulating the gain of SOM neurons without changing synaptic weights. Moreover, when the circuit employs Hamiltonian sampling, its sampling trajectories oscillate around the true latent moving state, resembling the decoded spatial trajectories from hippocampal theta sequences. Our work provides overarching connections between the canonical circuit with diverse interneurons and sampling-based dynamic inference, deepening our understanding of the circuit implementation of Bayesian sampling.

neuroscience↗

bioRxiv2020

Distributed Sampling-based Bayesian Inference in Coupled Neural Circuits

The brain performs probabilistic inference to interpret the external world, but the underlying neuronal mechanisms remain not well understood. The stimulus structure of natural scenes exists in a high-dimensional feature space, and how the brain represents and infers the joint posterior distribution in this rich, combinatorial space is a challenging problem. There is added difficulty when considering the neuronal mechanics of this representation, since many of these features are computed in parallel by distributed neural circuits. Here, we present a novel solution to this problem. We study continuous attractor neural networks (CANNs), each representing and inferring a stimulus attribute, where attractor coupling supports sampling-based inference on the multivariate posterior of the high-dimensional stimulus features. Using perturbative analysis, we show that the dynamics of coupled CANNs realizes Langevin sampling on the stimulus feature manifold embedded in neural population responses. In our framework, feedforward inputs convey the likelihood, reciprocal connections encode the stimulus correlational priors, and the internal Poisson variability of the neurons generate the correct random walks for sampling. Our model achieves high-dimensional joint probability representation and Bayesian inference in a distributed manner, where each attractor network infers the marginal posterior of the corresponding stimulus feature. The stimulus feature can be read out simply with a linear decoder based only on local activities of each network. Simulation experiments confirm our theoretical analysis. The study provides insight into the fundamental neural mechanisms for realizing efficient high-dimensional probabilistic inference.

neuroscience↗

bioRxiv2023

Overlap and divergence of neural circuits mediating distinct behavioral responses to sugar

A single sensory cue can elicit diverse behavioral responses. For example, the taste of sugar robustly promotes feeding1, 2 but also influences other behaviors, such as altering locomotor patterns to maximize food-finding3, 4 or conferring a rewarding value onto associated contexts or cues.5-7 Here, we investigate how sweet taste elicits multiple appetitive behaviors in Drosophila. Are different sugar-evoked behaviors coordinately regulated? At what point does the sugar circuit diverge into different pathways that drive distinct behaviors? We first established an optogenetic paradigm to study the effects of sugar taste on locomotion, spatial preference, and associative learning. We then tested how different sugar-evoked behaviors were modulated by internal and external factors, including hunger, diet, or the presence of an aversive taste. Different behaviors were generally modulated in similar ways, but we also observed some differences that reveal selective modulation of specific behavioral pathways. Finally, we investigated where the sugar taste circuit diverges into different behavioral pathways. A recent study identified a sensory-motor circuit comprising five layers of neurons that drives the initiation of feeding in response to sugar.8 By individually manipulating each of these neurons, we show that circuits mediating different innate responses to sugar are partially overlapping and begin to diverge at the level of second- and third-order neurons, whereas circuits for innate versus learned behaviors may diverge at the first synapse. Connectomic analyses reveal distinct subcircuits that mediate different behaviors. Together, these studies provide insight into how neural circuits are organized to elicit diverse behavioral responses to a single stimulus.

neuroscience↗

bioRxiv2021

Distinct neural circuits establish the same chemosensory behavior in C. elegans

Animals frequently exhibit the same behavior under different environmental or physiological conditions. To what extent these behaviors are generated by similar vs. distinct mechanisms is unclear. Moreover, the circumstances under which divergent neural mechanisms establish the same behavior, and the molecular signals that regulate the same behavior across conditions, are poorly understood. We show that in C. elegans, distinct neural mechanisms mediate the same chemosensory behavior at two different life stages. Both dauer larvae and starved adults are attracted to carbon dioxide (CO2), but CO2 attraction is mediated by distinct sets of interneurons at the two life stages. Some interneurons mediate CO2 response only in dauers, some show CO2-evoked activity in adults and dauers but contribute to CO2 response only in adults, and some show CO2-evoked activity that opposes CO2 attraction in adults but promotes CO2 attraction in dauers. We also identify a novel role for insulin signaling in establishing life-stage-specific CO2 responses by modulating interneuron activity. Further, we show that a combinatorial code of both shared and life-stage-specific molecular signals regulate CO2 attraction. Our results identify a mechanism by which the same chemosensory behavior can be generated by distinct neural circuits, revealing an unexpected complexity to chemosensory processing.

neuroscience↗

bioRxiv2018

Overlapping and unique neural circuits support perceptual decision making and confidence

The period of making a perceptual decision is often followed by a period of confidence generation where one rates the likely accuracy of the initial decision. However, it remains unclear whether the same or different neural circuits are engaged during periods of perceptual decision making and confidence generation. To address this question, we conducted two functional MRI experiments in which we dissociated the periods related to perceptual decision making and confidence report by either separating their respective regressors or asking for confidence ratings only in the second half of the experiment. We found that perceptual decision making and confidence reports gave rise to activations in large and mostly overlapping brain circuits including frontal, parietal, posterior, and cingulate regions with the results being remarkably consistent across the two experiments. Further, the confidence report period activated a number of unique regions, whereas there was no evidence for the decision period activating unique regions not involved in the confidence period. We discuss the possible reasons for this overlap and explore their implications about theories of perceptual decision making and confidence generation.

neuroscience↗

bioRxiv2024

Neural Circuit Mechanisms Underlying Dominance Traits and Social Competition

The survival of animals often hinges on their dominance status, established through repeated social competitions. The dorsomedial prefrontal cortex (dmPFC) plays a pivotal role in regulating these competitions, yet the formation of intrinsic traits like grit and aggressiveness, crucial for competitive outcomes, remains poorly understood. In this study, we constructed a dmPFC circuit model based on experimental recordings to replicate the characteristic activities of dmPFC neurons during various behavioral patterns observed in the dominance tube test. Our findings reveal that the dmPFC circuit supports bistable behavior states--effortful and passive--depending on external conditions. This bistability is essential for understanding how animals adapt their behaviors in social competitions, thereby influencing the establishment of social hierarchies. Our results indicate that increased self-excitation in pyramidal neurons within the dmPFC enhances the robustness of effortful behaviors, akin to perseverance, but reduces flexibility in responding to rapid external changes. This suggests that dominance status benefits more from perseverance than from increased aggression. Additionally, our study shows that when rapid responses to external signals are necessary, the basal activity in dmPFC neurons can be reconfigured to enhance flexibility, albeit at higher energy costs. This research advances our understanding of the neural basis of social behavior and provides a framework for further exploration into how neural circuits contribute to complex behavioral traits, offering insights into the neural dynamics underlying social dominance. This research also opens avenues for investigating psychiatric and neurological disorders where these mechanisms may be disrupted.

biophysics↗

bioRxiv2023

Restructuring of an asymmetric neural circuit during associative learning

Asymmetric brain function is common across the animal kingdom and involved in language processing, and likely in learning and memory. What regulates asymmetric brain function remains elusive. Here, we show that the nematode Caenorhabditis elegans restructures an asymmetric salt sensing neural circuit during associative learning. Worms memorize and prefer the salt concentration at which they were raised in the presence of food through a left-biased network architecture. When conditioned at elevated salt concentrations, animals change the left-biased to a right-biased network, which explains the changed salt-seeking behavior. The changes in circuit architecture require new synapse formation induced through asymmetric, paracrine insulin-signaling. Therefore, experience-dependent changes in asymmetric network architecture rely on paracrine insulin signaling and are fundamental to learning and behavior.

neuroscience↗