SEARCH · Search bioRxiv

Results for “neural circuits”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9Linked to original sources

bioRxiv2022

Harnessing insect olfactory neural circuits for noninvasive detection of human cancer

There is overwhelming evidence that metabolic processes are altered in cancer cells and these changes are manifested in the volatile organic compound (VOC) composition of exhaled breath. Here, we take a novel approach of an insect olfactory neural circuit-based VOC sensor for cancer detection. We combined an in vivo antennae-attached insect brain with an electrophysiology platform and employed biological neural computation rules of antennal lobe circuitry for data analysis to achieve our goals. Our results demonstrate that three different human oral cancers can be robustly distinguished from each other and from a non-cancer oral cell line by analyzing individual cell culture VOC composition-evoked olfactory neural responses in the insect antennal lobe. By evaluating cancer vs. non-cancer VOC-evoked population neural responses, we show that olfactory neurons response-based classification of oral cancer is sensitive and reliable. Moreover, this brain-based cancer detection approach is very fast (detection time ~ 250 ms). We also demonstrate that this cancer detection technique is effective across changing chemical environments mimicking natural conditions. Our brain-based cancer detection system comprises a novel VOC sensing methodology that will spur the development of more forward engineering technologies for noninvasive detection of cancer.

bioengineering↗

bioRxiv2025

Neural circuit mechanisms to transform cerebellar population dynamics for motor control in monkeys

We identify the input-output transformations performed by the cerebellar circuit and isolate the circuit properties that implement them. Extracellular recordings exploited new technology for neuron-type identification in part of the cerebellar cortex that is causal for an oculomotor behavior whose neural substrate is well-understood in monkeys. Data demonstrate how multiple classes of interneurons in the cerebellar circuit cooperate to transform incoming mossy fiber responses into Purkinje cell outputs. Computer models suggest that temporal decomposition in the granule cell layer enables flexible reconstruction of transformed output dynamics, regulated by cerebellar plasticity, to convert eye position-like inputs into predominantly velocity-driven Purkinje cell outputs. Convergence of mossy fiber inputs with diverse direction preferences would increase input layer dimensionality and enable learning of highly flexible direction tuning. The neural circuit mechanisms suggested by our data outline a cerebellar operating principle that enables flexible input-output transformations and could generalize across cerebellar and other brain areas.

neuroscience↗

bioRxiv2025

Multi-Class Classification of Cannabis and Alcohol Use Disorder: Identifying Common and Substance-Specific Neural Circuits.

Machine learning approaches have advanced the identification of neural signatures of substance use, particularly through case-control comparisons and network-level analyses. However, most studies have focused on single substances in isolation, making it difficult to directly compare shared and distinct network computations and limiting their generalizability, given that many individuals engage in polysubstance use. Here, we introduce an explainable, connectivity-based multiclass classification framework that models distributed brain network organization to delineate both shared and substance-specific mechanisms of addiction. This design enables direct comparison among cannabis users, alcohol users, and controls, revealing network computations that uniquely differentiate each group. Using functional connectivity features from cue-induced craving tasks, we classified cannabis users (n=166), alcohol users (n=101), and healthy controls (n=238), achieving high out-of-sample accuracy (87% for cannabis, 69% for alcohol, and 73% for controls). Functional connectivity-based models consistently outperformed activation-based models, highlighting the importance of inter-regional network properties for biomarker development. Network analysis further revealed dorsal prefrontal, cingulate, and precuneus hubs consistent with meta-analytic craving networks and substance-specific connectivity motifs enhanced prefrontal coupling in cannabis users and greater insular-striatal integration in alcohol users. These results demonstrate that distinct network configurations define different substance-use profiles, advancing interpretable biomarkers for addiction neuroscience.

neuroscience↗

bioRxiv2019

Endogenous activity modulates stimulus and circuit-specific neural tuning and perception

Perception reflects not only input from the sensory periphery, but also the endogenous neural state when sensory inputs enter the brain. Whether endogenous neural states influence perception only through global mechanisms, such as arousal, or can also perception in a neural circuit and stimulus specific manner remains largely unknown. Intracranial recordings from 30 pre-surgical epilepsy patients showed that endogenous activity independently modulated the strength of trial-by-trial neural tuning of different visual category-selective neural circuits. Furthermore, the same aspect of the endogenous activity that influenced tuning in a particular neural circuit also correlated with reaction time only for trials with the category of image that circuit was selective for. These results suggest that endogenous activity may influence neural tuning and perception through circuit-specific predictive coding processes.

neuroscience↗

bioRxiv2026

A neural circuit for olfactory motion detection

Movement is a defining feature of dynamic environments and a key variable encoded by sensory systems. In vision, neural mechanisms that extract motion direction have been studied extensively across species, revealing circuits that compare signals across space and time. Odor plumes likewise carry motion information in their spatiotemporal structure, which walking fruit flies detect and exploit to improve navigation (Brudner et al., 2025; Kadakia et al., 2022), independent of wind sensing. However, how olfactory circuits compute odor motion remains unknown. Here we identify circuitry in the Drosophila antennal lobe that transforms bilateral odor inputs into direction-selective signals that are propagated to higher brain regions. These direction-selective signals were widespread across most measured olfactory channels, spanning pheromones, food odors, and aversive cues. Directional responses were elicited by odor traces with naturalistic temporal statistics and tuned to inter-antennal delays of {approx}40 ms. Connectomic, physiological, and behav-ioral analyses identified one GABAergic inhibitory neuron in the antennal lobe as a key mediator of direction selectivity. Measurements of dynamics in direction-selective neurons revealed ipsilateral excitation and delayed contralateral inhibition. In a minimal data-driven model, this mechanism was sufficient to generate the observed direction-selective responses. These results establish motion detection as a circuit-level olfactory computation and reveal how an early sensory circuit transforms spatiotemporal chemical signals into a neural representation of odor motion to guide navigation.

neuroscience↗

bioRxiv2020

Uncovering the Organization of Neural Circuits with Generalized Phase Locking Analysis

Despite the considerable progress of in vivo neural recording techniques, inferring the biophysical mechanisms underlying large scale coordination of brain activity from neural data remains challenging. One obstacle is the difficulty to link high dimensional functional connectivity measures to mechanistic models of network activity. We address this issue by investigating spike-field coupling (SFC) measurements, which quantify the synchronization between, on the one hand, the action potentials produced by neurons, and on the other hand a mesoscopic "field" signals, reflecting subthreshold activities at possibly multiple recording sites. As the number of recording sites gets large, the amount of pairwise SFC measurements becomes overwhelmingly challenging to interpret. We develop Generalized Phase Locking Analysis (GPLA) as a dimensionality reduction of this multivariate SFC. GPLA describes the dominant coupling between field activity and neural ensembles across space and frequencies, thereby providing rich yet interpretable information. In particular, we show that GPLA features are biophysically interpretable when used in conjunction with appropriate network models, such that we can identify the influence of underlying circuit properties on these features. We demonstrate the statistical benefits and interpretability of this approach in various computational models and Utah array recordings. The results suggest that GPLA used jointly with biophysical modeling can help uncover the contribution of recurrent microcircuits to the spatio-temporal dynamics observed in multi-channel experimental recordings.

neuroscience↗

bioRxiv2022

Neural circuits underlying habituation of visually evoked escape behaviors in larval zebrafish

Larval zebrafish that are exposed repeatedly to dark looming stimuli will quickly habituate to these aversive signals and cease to respond with their stereotypical escape swims. A dark looming stimulus can be separated into two independent components: one that is characterized by an overall spatial expansion, where overall luminance is maintained at the same level, and a second, that represents an overall dimming within the whole visual field in the absence of any motion energy. Using specific stimulation patterns that isolate these independent components, we first extracted the behavioral algorithms that dictate how these separate information channels interact with each other and across the two eyes during the habituation process. Concurrent brain wide imaging experiments then permitted the construction of circuit models that suggest the existence of three separate neural pathways. The first is a looming channel which responds specifically to concentrically expanding edges and relays that information to the brain stem escape network to generate directed escapes. The second is a dimming specific channel that serves to support and amplify the looming pathway. While the looming channel operates in a purely monocular fashion where stimuli are relayed exclusively to the contralateral hemisphere, dimming responses are processed in both monocular and binocular pathways. Finally, we identify a third, separate and largely monocular, dimming channel that appears to specifically inhibit escape responses when activated. We propose that, unlike the first two channels, this third channel is under strong contextual modulation and that it is primarily responsible for the incremental silencing of successive dark looming evoked escapes.

neuroscience↗

bioRxiv2024

Real-cyber hybrid neural network for predicting neural circuits in mouse decision-making

A major challenge in neuroscience is to elucidate how the brain processes sensory input to generate behavior, especially given the difficulty to measure neural activity across the whole brain. To address this limitation, previous studies have used artificial neural networks (ANNs) and modeled decision-making circuits in the brain. Here, we developed a real-cyber hybrid neural network (HNN) that directly input the activity of real neurons to an ANN, imposing biological constraints on the model. Using spike inputs from cortical or subcortical regions, the HNN predicted body movements of head-fixed mice during a task better than the ANN did. This improvement arose not only from the added mouse neuronal activity, but from the activity generated within the HNN. The generated activity potentially included unrecorded neuronal activity from mice, which was difficult for the ANN. We propose that HNNs develop brain-like activity to predict animal movements and compensate for unrecorded neural activity.

neuroscience↗

bioRxiv2025

A Neural Circuit Framework for Economic Choice: From Building Blocks of Valuation to Compositionality in Multitasking

Value-guided decisions are a cornerstone of cognition, yet the underlying circuit-level mechanisms remain elusive. We used reinforcement learning to train recurrent neural network models endowed with Dales law on a battery of economic choice tasks, which revealed a two-stage computational framework. First, value estimation occurs at input level where learned weights store subjective preferences and approximate the nonlinear multiplication of reward magnitude and probability to yield expected values. This feedforward mechanism enables generalization to novel choice options. Second, option values are compared within the recurrent network, where specific connectivity patterns mediate robust winner-take-all decisions, with both excitatory and inhibitory neurons exhibiting value and choice selectivity. By training a single network on multiple tasks, we show compositional representations combining a shared computational schema with specialized neural modules. Reproducing key neurophysiological findings from the primate orbitofrontal cortex, our model unifies value computation, comparison, and generalization into a coherent framework with testable predictions. IN BRIEFBattista et al. use biologically plausible RNNs to uncover circuit mechanisms of economic choice. They propose a two-stage framework where feedforward inputs compute offer values and recurrent inhibition drives comparison. This architecture explains how the brain generalizes preferences and multitasks using compositional neural codes, offering a unified theory of decisionmaking. HIGHLIGHTSO_LIBiologically plausible RNN reveals a two-stage economic choice framework C_LIO_LIInput weights store preferences and multiply features to compute offer value C_LIO_LICompetitive recurrent inhibition mediates winner-take-all comparison C_LIO_LIMultitasking relies on compositional shared and specialized neural modules C_LI

neuroscience↗

bioRxiv2019

Neural circuit mechanism underlying the feeding controlled by insula-central amygdala pathway

Central nucleus of amygdala (CeA) contains distinct populations of neurons that play opposing roles in feeding. The circuit mechanism of how CeA neurons process information sent from their upstream inputs to regulate feeding is still unclear. Here we show that activation of the neural pathway projecting from insular cortex neurons to CeA suppresses food intake. Surprisingly, we find that the inputs from insular cortex form excitatory connections with similar strength to all types of CeA neurons. To reconcile this puzzling result, and previous findings, we developed a conductance-based dynamical systems model for the CeA neuronal network. Computer simulations showed that both the intrinsic electrophysiological properties of individual CeA neurons and the overall synaptic organization of the CeA circuit play a functionally significant role in shaping the CeA neural dynamics. We successfully identified a specific CeA circuit structure that reproduces the desired circuit output consistent with existing experimentally observed feeding behaviors.

neuroscience↗

bioRxiv2018

Heparan sulfate molecules mediate synapse formation and function of male mating neural circuits in C. elegans

The nervous system regulates complex behaviors through a network of neurons interconnected by synapses. How specific synaptic connections are genetically determined is still unclear. Male mating is the most complex behavior in C. elegans. It is composed of sequential steps that are governed by more than 3,000 chemical connections. Here we show that heparan sulfates (HS) play a role in the formation and function of the male neural network. Cell-autonomous and non-autonomous 3-O sulfation by the HS modification enzyme HST-3.1/HS 3-O-sulfotransferase, localized to the HSPG glypicans LON-2/glypican and GPN-1/glypican, was specifically required for response to hermaphrodite contact during mating. Loss of 3-O sulfation resulted in the presynaptic accumulation of RAB-3, a molecule that localizes to synaptic vesicles, disrupting the formation of synapses in a component of the mating circuits. We also show that neural cell adhesion protein neurexin promotes and neural cell adhesion protein neuroligin inhibits formation of the same set of synapses in a parallel pathway. Thus, neural cell adhesion proteins and extracellular matrix components act together in the formation of synaptic connections.\n\nAuthor SummaryThe formation of the nervous system requires the function of several genetically-encoded proteins to form complex networks. Enzymatically-generated modifications of these proteins play a crucial role during this process. These authors analyzed the role of heparan sulfates in the process of synaptogenesis in the male tail of C. elegans. A modification of heparan sulfate is required for the formation of specific synapses between neurons by acting cell-autonomously and non-autonomously. Could it be that heparan sulfates and their diverse modifications are a component of the specification factor that neurons use to make such large numbers of connections unique?

neuroscience↗

bioRxiv2017

Three-Dimensional Two-Photon Optogenetics And Imaging Of Neural Circuits In Vivo

We demonstrate a holographic system for simultaneous three-dimensional (3D) two-photon stimulation and imaging of neural activity in the mouse neocortex in vivo with cellular resolution. Dual two-photon excitation paths are implemented with independent 3D targeting for calcium imaging and precision optogenetics. We validate the usefulness of the microscope by photoactivating local pools of interneurons in awake mice visual cortex in 3D, which suppress the nearby pyramidal neurons response to visual stimuli.

neuroscience↗

bioRxiv2024

Discovering plasticity rules that organize and maintain neural circuits

Intrinsic dynamics within the brain can accelerate learning by providing a prior scaffolding for dynamics aligned with task objectives. Such intrinsic dynamics should self-organize and self-sustain in the face of fluctuating inputs and biological noise, including synaptic turnover and cell death. An example of such dynamics is the formation of sequences, a ubiquitous motif in neural activity. The sequence-generating circuit in zebra finch HVC provides a reliable timing scaffold for motor output in song and demonstrates a remarkable capacity for unsupervised recovery following perturbation. Inspired by HVC, we seek a local plasticity rule capable of organizing and maintaining sequence-generating dynamics despite continual network perturbations. We adopt a meta-learning approach introduced by Confavreux et al, which parameterizes a learning rule using basis functions constructed from pre- and postsynaptic activity and synapse size, with tunable time constants. Candidate rules are simulated within initially random networks, and their fitness is evaluated according to a loss function that measures the fidelity with which the resulting dynamics encode time. We use this approach to introduce biological noise, forcing meta-learning to find robust solutions. We first show that, in the absence of perturbation, meta-learning identifies a temporally asymmetric generalization of Ojas rule that reliably organizes sparse sequential activity. When synaptic turnover is introduced, the learned rule incorporates an additional form of homeostasis, better maintaining sequential dynamics relative to other previously proposed rules. Additionally, inspired by recent findings demonstrating plasticity in synapses from inhibitory interneurons in HVC, we explore the role of inhibitory plasticity in sequence-generating circuits. We find that learned plasticity adjusts both excitation and inhibition in response to manipulations, outperforming rules applied only to excitatory connections. We demonstrate how plasticity acting on both excitatory and inhibitory synapses can better shape excitatory cell dynamics to scaffold timing representations.

neuroscience↗

bioRxiv2019

Nonlinear stimulus representations in neural circuits with approximate excitatory-inhibitory balance

Balanced excitation and inhibition is widely observed in cortical recordings. How does this balance shape neural computations and stimulus representations? This problem is often studied using computational models of neuronal networks in a dynamically balanced state. However, these balanced network models predict a linear relationship between stimuli and population responses, in contrast to the nonlinearity of cortical computations. We show that every balanced network architecture admits some stimuli that break the balanced state and these breaks in balance push the network into a "semi-balanced state" characterized by excess inhibition to some neurons, but an absence of excess excitation. The semi-balanced state is unavoidable in networks driven by multiple stimuli, consistent with experimental data, has a direct mathematical relationship to artificial neural networks, and permits nonlinear stimulus representations and nonlinear computations.

neuroscience↗

bioRxiv2018

Neural Circuit Mechanism of Decision Uncertainty and Change-of-Mind

Decision-making is often accompanied by a degree of confidence on whether a choice is correct. Decision uncertainty, or lack in confidence, may lead to change-of-mind. Studies have identified the behavioural characteristics associated with decision confidence or change-of-mind, and their neural correlates. Although several theoretical accounts have been proposed, there is no neural model that can compute decision uncertainty and explain its effects on change-of-mind. We propose a neuronal circuit model that computes decision uncertainty while accounting for a variety of behavioural and neural data of decision confidence and change-of-mind, including testable model predictions. Our theoretical analysis suggests that change-of-mind occurs due to the presence of a transient uncertainty-induced choice-neutral stable steady state and noisy fluctuation within the neuronal network. Our distributed network model indicates that the neural basis of change-of-mind is more distinctively identified in motor-based neurons. Overall, our model provides a framework that unifies decision confidence and change-of-mind.

neuroscience↗

bioRxiv2024

Synergistic Neural Circuits for Novelty and Goal-Directed Behavior

The ability to adapt to a dynamic world relies on detecting, learning, and responding to environmental changes. The detection of novelty serves as a critical indicator of such changes, priming mechanisms to detect and respond to goal-relevant information. However, neural regions that support novelty detection (hippocampus) and goal-directed behavior (dopaminergic midbrain [VTA] and prefrontal cortex [PFC]) have yet to be described as a sequential process that unfolds over time. Using a forward-prediction functional magnetic resonance imaging (fMRI) model, we explored interactions between the hippocampus, VTA, and PFC in humans performing a novelty-imbued target-detection task. Hippocampal novelty activation predicted subsequent VTA target activation, enhancing readiness to detect goal-relevant information. Concurrently, goal-directed PFC activation modulated VTA target activation, refining focus on behaviorally significant cues. These circuits function both synergistically and independently, promoting subsequent hippocampal activity during target trials. This work provides new insights into how distributed circuits coordinate to optimize adaptive behavior. Significance StatementSurviving in dynamic environments requires coordinated neural mechanisms to detect, learn from, and respond to change. However, neural regions that support novelty detection and goal-oriented behavior have yet to be described as a sequential process that unfolds over time. Using a novel forward-prediction functional magnetic resonance imaging (fMRI) model, we show that hippocampal activation during novelty predicts ventral tegmental area (VTA) readiness to process goal-relevant information. Concurrently, goal-directed prefrontal cortex activity modulates VTA responses, sharpening focus on behaviorally significant cues. Furthermore, these synergistic and independent circuits enhance hippocampal sensitivity for future adaptive responses, offering novel insights into integration of brain mechanisms critical for learning, motivation, and executive function.

neuroscience↗

bioRxiv2026

A neural circuit mechanism for abstract free choice

Making abstract free choices, such as arbitrary decisions between comparable options formed independently of specific motor plans, is central to everyday behavior. Yet, the neural mechanisms underlying free choice remain poorly understood. Using a delayed-response paradigm (N=80), we dissociated abstract goal selection (color choice) from later motor response mapping. We found that freely chosen goals elicit slower responses and lower goal-switching costs than instructed goals. We developed a hierarchical attractor network model in which symmetry-breaking attractor dynamics drive free goal selection, and conjunctive representations translate these abstract goals into motor output. Freely chosen goals converged on less extreme fixed points than instructed ones, accounting for their slower, more variable responses. Consistent with our model, human EEG activity (N=30) sequentially encoded goal, conjunctive, and response representations, with conjunctive representations specifically coupled to behavioral variability. Our findings offer plausible circuit-level mechanisms for how abstract free choices are made and translated into action. HighlightsO_LIFree-instructed differences persist when goal selection and response mapping are dissociated C_LIO_LIAbstract free choices elicit reduced goal-switching costs relative to instructed ones C_LIO_LISymmetry-breaking attractor dynamics explain behavioral signatures of free choice C_LIO_LIEEG signals sequentially encode abstract goal, conjunctive, and response representations C_LIO_LIConjunctive representations, but not goal or response, were coupled to behavior C_LI

neuroscience↗

bioRxiv2017

Investigating circadian rhythmicity in pain sensitivity using a neural circuit model for spinal cord processing of pain

Primary processing of painful stimulation occurs in the dorsal horn of the spinal cord. In this article, we introduce mathematical models of the neural circuitry in the dorsal horn responsible for processing nerve fiber inputs from noxious stimulation of peripheral tissues and generating the resultant pain signal. The differential equation models describe the average firing rates of excitatory and inhibitory interneuron populations, as well as the wide dynamic range (WDR) neurons whose output correlates with the pain signal. The temporal profile of inputs on the different afferent nerve fibers that signal noxious and innocuous stimulation and the excitability properties of the included neuronal populations are constrained by experimental results. We consider models for the spinal cord circuit in isolation and when top-down inputs from higher brain areas that modulate pain processing are included. We validate the models by replicating experimentally observed phenomena of A fiber inhibition of pain and wind-up. We then use the models to investigate mechanisms for the observed phase shift in circadian rhythmicity of pain that occurs with neuropathic pain conditions. Our results suggest that changes in neuropathic pain rhythmicity can occur through dysregulation of inhibition within the dorsal horn circuit.

synthetic biology↗