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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 19 recordsLinked to original sources

bioRxiv2025

Attractor-based models for sequences and pattern generation in neural circuits

Neural circuits in the brain perform a variety of essential functions, including input classification, pattern completion, and the generation of rhythms and oscillations that support functions such as breathing and locomotion. There is also substantial evidence that the brain encodes memories and processes information via sequences of neural activity. Traditionally, rhythmic activity and pattern generation have been modeled using coupled oscillators, whereas input classification and pattern completion have been modeled using attractor neural networks. Here, we present a theoretical framework that demonstrates how attractor-based networks can also generate diverse rhythmic patterns, such as those of central pattern generator circuits (CPGs). Additionally, we propose a mechanism for transitioning between patterns. Specifically, we construct a network that can step through a sequence of five different quadruped gaits. It is composed of two dynamically distinct modules: a "counter" network, that can count the number of external inputs it receives via a sequence of fixed points; and a locomotion network, that encodes five different quadruped gaits as limit cycles. A sequence of locomotive gaits is obtained by connecting the counter network with the locomotion network. Specifically, we introduce a new architecture for layering networks that produces "fusion" attractors, binding pairs of attractors from individual layers. All of this is accomplished within a unified framework of attractor-based models using threshold-linear networks.

neuroscience↗

bioRxiv2025

Endosome-associated Rab GTPases control distinct aspects of neural circuit assembly

Neural circuit assembly relies on the precise regulation of cell-surface receptors that mediate signaling and adhesion. Endocytosis controls receptor activity and availability by internalizing and routing proteins through two main pathways: recycling back to the cell-surface or delivery to lysosomes for degradation. Rab GTPases direct receptors into these distinct pathways, but their specific contributions to circuit formation remain opaque. Using clonal analyses with null alleles, we dissected the roles of Rab-mediated trafficking to early, late, and recycling endosomes across multiple stages of circuit assembly in vivo. Our approach revealed that Rab5 and Rab11 regulate extensive and largely distinct developmental events, highlighting the pivotal roles of early endosome sorting and recycling pathways in circuit assembly. As neurons mature, both the spatial distribution and abundance of specific endocytic compartments change to reflect evolving trafficking demands. Our findings underscore how distinct post-endocytic trafficking fates are necessary to build neural circuits.

cell biology↗

bioRxiv2026

Mitochondria Organize Gap Junctions to Enable Neural Circuit Scaling

Neural circuits must maintain function during dramatic growth-driven expansion. While chemical synapses can be independently reorganized in pre- and postsynaptic neurons, gap junctions physically couple partner cells, requiring coordinated spatial redistribution--a process whose mechanisms remain unknown. Using the C. elegans mechanosensory circuit, we show that gap junctions undergo stereotyped developmental reorganization, transitioning from clustered to dispersed configurations as partner neurites elongate. Forward genetic screening identified RIC-7, a mitochondrial adaptor protein, as essential for this redistribution. Loss of RIC-7 prevents gap junction dispersal, trapping partner neurites at clustered sites and severely impairing electrical transmission. Mechanistically, RIC-7 directs mitochondrial positioning to gap junctions, where mitochondria recruit the microtubule-organizing component PTRN-1 to locally enhance microtubule dynamics. Disrupting mitochondrial transport (miro-1;mtx-2 mutants) or PTRN-1 function phenocopies ric-7 defects, while metabolic dysfunction does not, establishing a non-metabolic role for mitochondria in organizing synaptic architecture. Remarkably, RIC-7 expression in one neuron alone suffices to restore gap junction and partner neurite distribution across both coupled cells. Structural modeling suggests mechanical adhesion between docked hemichannels enables this coordinated reorganization. These findings establish mitochondria as mobile organizers of synaptic spatial architecture, demonstrate that proper junction distribution is essential for electrical transmission efficacy, and reveal how cell-autonomous mechanisms can achieve bilateral structural coordination during circuit growth.

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↗

bioRxiv2024

Deep inverse modeling reveals dynamic-dependent invariances in neural circuit mechanisms

Neural population dynamics are shaped by many cellular, synaptic, and network properties. Not only is it important to understand how coordinated changes in circuit parameters alter neural activity, but also when dynamics are unaffected by--or invariant to--such changes. Computational modeling has revealed invariances in single neurons and small circuits that are thought to reflect their robustness against variability and perturbations. However, generalizing these insights to larger circuits in cortex and other brain areas remains challenging. A key bottleneck lies in inverse modeling of neural circuits with spiking network models, i.e., identifying parameter configurations that quantitatively match dynamics observed in neural recordings. Here, we present Automated Model Inference from Neural Dynamics (AutoMIND) for efficient discovery of invariant circuit model configurations. AutoMIND leverages a mechanistic model with adaptive spiking neurons and clustered connectivity, which displays a rich variety of spatiotemporal dynamics. Probabilistic deep generative models--trained on network simulations only--then returns many parameter configurations consistent with a given target observation of neural activity. Applied to several datasets, AutoMIND discovers circuit models of synchronous network bursting in human brain organoids across early development, as well as models capturing complex frequency profiles of Neuropixels recordings in mouse hippocampus and cortex. In each case, we obtain hundreds of configurations that compose a (nonlinear) parameter subspace in which population dynamics remain unchanged. Surprisingly, global and local geometries of the invariant subspace are not fixed, but differ for different dynamics. Together, our results shed light on dynamic-dependent invariances of circuit parameters underlying diverse population dynamics, while demonstrating the flexibility of AutoMIND for inverse modeling of neural circuits.

neuroscience↗

bioRxiv2020

Degeneracy and stability in neural circuits of neuromodulators

Degenerate neural circuits perform the same function despite being structurally different. However, it is unclear whether neural circuits with interacting neuromodulator sources can themselves be degenerate while maintaining the same neuromodulatory function. Here, we address this by computationally modelling the neural circuits of neuromodulators serotonin and dopamine, local glutamatergic and GABAergic interneurons, and their possible interactions, under reward/punishment-based conditioning tasks. The neural modelling is constrained by relevant experimental studies of the VTA or DRN system using e.g. electrophysiology, optogenetics, and voltammetry. We first show that a single parsimonious, sparsely connected neural circuit model can recapitulate several separate experimental findings that indicated diverse, heterogeneous, distributed and mixed DRN-VTA neuronal signalling in reward and punishment tasks. The inability for this model to recapitulate all observed neuronal signalling suggests potentially multiple circuits acting in parallel. Then using computational simulations and dynamical systems analysis, we demonstrate that several different stable circuit architectures can produce the same observed network activity profile, hence demonstrating degeneracy. Due to the extensive D2-mediated connections in the investigated circuits, we simulate D2 receptor agonist by increasing the connection strengths emanating from the VTA DA neurons. We found that the simulated D2 agonist can distinguish among sub-groups of the degenerate neural circuits based on substantial deviations in specific neural populations activities in reward and punishment conditions. This forms a testable model prediction using pharmacological means. Overall, this theoretical work suggests the plausibility of degeneracy within neuromodulator circuitry and has important implication for the stable and robust maintenance of neuromodulatory functions.

neuroscience↗

bioRxiv2022

Uncovering a neural circuit controlling adult quiescent neural stem cell activation in the subventricular zone

Neurogenesis and differentiation of the neural stem cells (NSCs) in the subventricular zone (SVZ) are controlled by cell-intrinsic molecular pathways that interact with extrinsic signaling cues. Here we identified a novel circuit that regulates neurogenesis and cellular proliferation in the lateral ventricle SVZ (LV-SVZ). Our results demonstrate direct glutamatergic inputs from the frontal cortex as well as local inhibitory interneurons, control the activity of distinctive cholinergic neurons in the subependymal zone (subep-ChAT+). In vivo optogenetic stimulation and inhibition in this circuit were sufficient to control local SVZ neurogenesis, LV NSCs proliferation, and SVZ cellular divisions in ventral SVZ. These findings shed light on local and distal neural circuit activity-dependent regulation of postnatal and adult SVZ neurogenesis and LV-SVZ cellular proliferation.

neuroscience↗

bioRxiv2026

Emergent Traveling Waves in Neural Circuits

Artificial neural networks achieve striking performance but do not capture a prominent neural property. Cortical activity is organized by structured spatiotemporal dynamics, traveling waves (TW), which have been implicated in a wide range of functions. Existing computational models often rely on hand-crafted connectivity and imposed dynamics, offering insight into their impact but less into how the waves naturally emerge in biological circuits. Here, we found that TWs emerge in models under biologically plausible constraints (spatially organized, directionally biased connectivity). Under an empirical neural manifold constraint, these wiring principles naturally emerge to support traveling wave dynamics in the recurrent model. We further show that wave propagation provides a robust mechanism for maintaining working memory in the presence of visual distractors. We compared these model predictions to non-human primate prefrontal cortex recordings, revealing a similar mechanism. Together, these results advance our understanding of traveling waves as a substrate for cognition and offer a framework for mechanistic accounts of cortical computation.

neuroscience↗

bioRxiv2018

Fundamental bounds on learning performance in neural circuits

How does the size of a neural circuit influence its learning performance? Intuitively, we expect the learning capacity of a neural circuit to grow with the number of neurons and synapses. Larger brains tend to be found in species with higher cognitive function and learning ability. Similarly, adding connections and units to artificial neural networks can allow them to solve more complex tasks. However, we show that in a biologically relevant setting where synapses introduce an unavoidable amount of noise, there is an optimal size of network for a given task. Beneath this optimal size, our analysis shows how adding apparently redundant neurons and connections can make tasks more learnable. Therefore large neural circuits can either devote connectivity to generating complex behaviors, or exploit this connectivity to achieve faster and more precise learning of simpler behaviors. Above the optimal network size, the addition of neurons and synaptic connections starts to impede learning performance. This suggests that overall brain size may be constrained by the need to learn efficiently with unreliable synapses, and may explain why some neurological learning deficits are associated with hyperconnectivity. Our analysis is independent of specific learning rules and uncovers fundamental relationships between learning rate, task performance, network size and intrinsic noise in neural circuits.

neuroscience↗

bioRxiv2017

Inferring hidden structure in multilayered neural circuits

A central challenge in sensory neuroscience involves understanding how neural circuits shape computations across cascaded cell layers. Here we develop a computational framework to reconstruct the response properties of experimentally unobserved neurons in the interior of a multilayered neural circuit. We combine non-smooth regularization with proximal consensus algorithms to overcome difficulties in fitting such models that arise from the high dimensionality of their parameter space. Our methods are statistically and computationally efficient, enabling us to rapidly learn hierarchical non-linear models as well as efficiently compute widely used descriptive statistics such as the spike triggered average (STA) and covariance (STC) for high dimensional stimuli. For example, with our regularization framework, we can learn the STA and STC using 5 and 10 minutes of data, respectively, at a level of accuracy that otherwise requires 40 minutes of data without regularization. We apply our framework to retinal ganglion cell processing, learning cascaded linear-nonlinear (LN-LN) models of retinal circuitry, consisting of thousands of parameters, using 40 minutes of responses to white noise. Our models demonstrate a 53% improvement in predicting ganglion cell spikes over classical linear-nonlinear (LN) models. Internal nonlinear subunits of the model match properties of retinal bipolar cells in both receptive field structure and number. Subunits had consistently high thresholds, leading to sparse activity patterns in which only one subunit drives ganglion cell spiking at any time. From the model's parameters, we predict that the removal of visual redundancies through stimulus decorrelation across space, a central tenet of efficient coding theory, originates primarily from bipolar cell synapses. Furthermore, the composite nonlinear computation performed by retinal circuitry corresponds to a boolean OR function applied to bipolar cell feature detectors. Our general computational framework may aid in extracting principles of nonlinear hierarchical sensory processing across diverse modalities from limited data.\n\nAuthor SummaryComputation in neural circuits arises from the cascaded processing of inputs through multiple cell layers. Each of these cell layers performs operations such as filtering and thresholding in order to shape a circuits output. It remains a challenge to describe both the computations and the mechanisms that mediate them given limited data recorded from a neural circuit. A standard approach to describing circuit computation involves building quantitative encoding models that predict the circuit response given its input, but these often fail to map in an interpretable way onto mechanisms within the circuit. In this work, we build two layer linear-nonlinear cascade models (LN-LN) in order to describe how the retinal output is shaped by nonlinear mechanisms in the inner retina. We find that these LN-LN models, fit to ganglion cell recordings alone, identify filters and nonlinearities that are readily mapped onto individual circuit components inside the retina, namely bipolar cells and the bipolar-to-ganglion cell synaptic threshold. This work demonstrates how combining simple prior knowledge of circuit properties with partial experimental recordings of a neural circuits output can yield interpretable models of the entire circuit computation, including parts of the circuit that are hidden or not directly observed in neural recordings.

neuroscience↗

bioRxiv2018

The first principle of neural circuit and the general Circuit-Probability theory

AbstractA neural circuit model involving inductance is established to explain the neural networks behavior. A parallel resistor-inductor-capacitor (RLC) circuit was used to fit the stimulus artifacts in the electromyography (EMG) recording of cortical and pelvic electrical nerve stimulations. This parallel RLC circuit model also predicts the resonance effect in both stimulus artifacts and EMG signals. Moreover, the well-known strength-duration relationship was directly derived to be a precise format with this parallel RLC circuit model. A theoretical explanation is provided to show the inductance is generated by the coil structure of the myelin sheath and the piezoelectric effect of the plasma membrane.\n\nOne Sentence SummaryThe inductance in the neural systems is generated by the coil structure of the myelin sheath and the piezoelectric effect of the plasma membrane.

neuroscience↗

bioRxiv2024

Robustly encoding certainty in a metastable neural circuit model

Localized persistent neural activity has been shown to serve delayed estimation of continuous variables. Common experiments require that subjects store and report the feature value (e.g., orientation) of a particular cue (e.g., oriented bar on a screen) after a delay. Visualizing recorded activity of neurons according to their feature tuning reveals activity bumps whose centers wander stochastically, degrading the estimate over time. Bump position therefore represents the remembered estimate. Recent work suggests that bump amplitude may represent estimate certainty reflecting a probabilistic population code for a Bayesian posterior. Idealized models of this type are fragile due to the fine tuning common to constructed continuum attractors in dynamical systems. Here we propose an alternative metastable model for robustly supporting multiple bump amplitudes by extending neural circuit models to include quantized nonlinearities. Asymptotic projections of circuit activity produce low-dimensional evolution equations for the amplitude and position of bump solutions in response to external stimuli and noise perturbations. Analysis of reduced equations accurately characterizes phase variance and the dynamics of amplitude transitions between stable discrete values. More salient cues generate bumps of higher amplitude which wander less, consistent with the experimental finding that greater certainty correlates with more accurate memories.

neuroscience↗

bioRxiv2020

Electro-optical mechanically flexible coaxial microprobes for minimally invasive interfacing with intrinsic neural circuits

Central to advancing our understanding of neural circuits is the development of minimally invasive, multi-modal interfaces capable of simultaneously recording and modulating neural activity. Recent devices have focused on matching the mechanical compliance of tissue to reduce inflammatory responses1,2. However, reductions in the size of multi-modal interfaces are needed to further improve biocompatibility and long-term recording capabilities1. Here we demonstrate a multi-modal coaxial microprobe design with a minimally invasive footprint (8-12 m diameter over millimeter lengths) that enables efficient electrical and optical interrogation of neural networks. In the brain, the probes allowed robust electrical measurement and optogenetic stimulation. Scalable fabrication strategies can be used with various electrical and optical materials, making the probes highly customizable to experimental requirements, including length, diameter, and mechanical properties. Given their negligible inflammatory response, these probes promise to enable a new generation of readily tunable multi-modal devices for minimally invasive interfacing with neural circuits.

neuroscience↗

bioRxiv2023

Olfactory learning modulates a neural circuit mediating innate odor-guided behavior in Drosophila

Behavior is often categorized as being innate or learned, with the specific circuits being assigned to one of these categories. In Drosophila, neural circuits mediating an innate behavioral response are considered as being "hard-wired", as activation of these neuronal pathways leads to stereotyped behaviors. However, only a limited number of studies assessed whether innate behaviors and their underlying neural circuits are plastic or show experience-dependent modulation. Here, we show that experience modulates second-order olfactory neurons involved in innate behavioral responses. We focus on the neural circuit defined by multiglomerular projection neurons (mPNs) that target the lateral horn, a structure relevant in the genesis of innate behavior. We show that mPNs, coding for odor attraction, are bidirectionally modulated after olfactory associative learning: when an olfactory stimulus is paired with an aversive electric shock, the activity of these neurons is decreased, while when the odor is paired with a sucrose-reward they are potentiated. We further show that this modulation requires glutamate and serotonin signaling, and that downstream third-order neurons are consequently affected. The bidirectional nature of the plasticity in these neurons is reflected in behavior: silencing mPN activity leads to odor avoidance, while artificial activation induces approach. While output from the mPNs is not required in aversive olfactory conditioning, silencing these neurons during retrieval of appetitive memories leads to a significant memory impairment. Artificially activating these neurons during odor presentation is sufficient to generate a 3 h appetitive memory. Our study in flies shows that a neural circuit coding for innate odor attraction can contribute to learned behavior, is modulated by olfactory learning and can provide reward-like reinforcement.

neuroscience↗

bioRxiv2021

Glioblastoma remodeling of neural circuits in the human brain decreases survival

Gliomas synaptically integrate into neural circuits. Prior work has demonstrated bidirectional interactions between neurons and glioma cells, with neuronal activity driving glioma growth and gliomas increasing neuronal excitability. In this study we wanted to know how glioma induced neuronal changes influence neural circuits underlying cognition and whether these interactions influence patient survival. We use intracranial brain recordings during lexical retrieval language tasks in awake humans in addition to site specific tumor tissue biopsies and cell biology experiments. We find that gliomas remodel functional neural circuitry such that task-relevant neural responses activate tumor-infiltrated cortex, beyond cortical excitation normally recruited in the healthy brain. Site-directed biopsies from functionally connected regions within the tumor are enriched for a glioblastoma subpopulation that exhibits a distinct synaptogenic and neuronotrophic phenotype. Tumor cells from functionally connected regions secrete the synaptogenic factor thrombospondin-1, which contributes to the differential neuron-glioma interactions observed in functionally connected tumor regions compared to tumor regions with less functional connectivity. The degree of functional connectivity between glioblastoma and the normal brain negatively impacts both patient survival and language task performance. These data demonstrate that high-grade gliomas functionally remodel neural circuits in the human brain, which both promotes tumor proliferation and impairs cognition.

cancer biology↗

bioRxiv2026

Magnetogenetic control of endogenous calcium signaling via ROS-sensitive ion channels reveals astrocytic regulation of neural circuit activity

Astrocytes exhibit complex calcium signaling proposed to detect, integrate, and modulate neuronal activity; however, their causal contribution to neural circuit function remains unresolved. This is partly due to the lack of tools that recapitulate physiological astrocyte calcium signaling. Here, we show that experimental magnetic control of endogenous calcium signaling in astrocytes is sufficient to modulate neural circuit dynamics. Using pmFeRIC, a magnetogenetic tool for remote, noninvasive, and on-demand activation of endogenous calcium pathways, we demonstrate that selective activation of astrocyte calcium signaling drives neuronal activity. pmFeRIC uses magnetic fields and endogenous ferritin to generate localized reactive oxygen species (ROS) at the plasma membrane, activating native ROS-sensitive ion channels, inducing calcium influx. In hippocampal circuits in vitro, magnetic activation of endogenous astrocytic calcium signaling produced robust, state-dependent changes in neuronal circuit activity, in part by increasing presynaptic release probability, postsynaptic burst firing, and glutamatergic transmission. These effects were mediated by transient receptor potential channels, amplified by intracellular calcium stores, and abolished when astrocyte calcium signaling was disrupted, establishing a causal role in neural circuit dynamics. In vivo, pmFeRIC activates endogenous calcium signaling in astrocytes with kinetics comparable to locomotion-induced activity, enabling control of astrocyte calcium dynamics that resemble physiological behavioral responses. Together, these findings establish that endogenous astrocytic calcium signaling is sufficient to modulate neural circuit function and introduce a broadly applicable platform for cell-specific, wireless, on-demand control of endogenous calcium signaling through magnetic activation of ROS-sensitive ion channels.

neuroscience↗

bioRxiv2017

Understanding Neural Circuit Development Through Theory And Models

How are neural circuits organized and tuned to achieve stable function and produce robust behavior? The organization process begins early in development and involves a diversity of mechanisms unique to this period. We summarize recent progress in theoretical neuroscience that has substantially contributed to our understanding of development at the single neuron, synaptic and network level. We go beyond classical models of topographic map formation, and focus on the generation of complex spatiotemporal activity patterns, their role in refinements of particular circuit features, and the emergence of functional computations. Aided by the development of novel quantitative methods for data analysis, theoretical and computational models have enabled us to test the adequacy of specific assumptions, explain experimental data and propose testable hypotheses. With the accumulation of larger data sets, theory and models will likely play an even more important role in understanding the development of neural circuits.

neuroscience↗

bioRxiv2020

A Global Temporal Genetic Program for Neural Circuit Formation

The assembly of neural circuits is dependent upon precise spatiotemporal expression of cell recognition molecules1-6. Factors controlling cell-type specificity have been identified7-9, but how timing is determined remains unknown. Here we describe the induction of a cascade of transcription factors by a steroid hormone (Ecdysone) in all fly visual system neurons spanning target recognition and synaptogenesis. We demonstrate through single cell sequencing that the Ecdysone pathway regulates the expression of a common set of targets required for synaptic maturation and cell-type specific targets enriched for cell surface proteins regulating wiring specificity. Transcription factors in the cascade regulate the expression of the same wiring genes in complex ways, including activation in one cell-type and repression in another. We show that disruption of the Ecdysone-pathway generates specific defects in dendritic and axonal processes and synaptic connectivity, with the order of transcription factor expression correlating with sequential steps in wiring. We also identify shared targets of a cell-type specific transcription factor and the Ecdysone pathway which regulate specificity. We propose neurons integrate a global temporal transcriptional module with cell-type specific transcription factors to generate different cell-type specific patterns of cell recognition molecules regulating wiring.

neuroscience↗