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bioRxiv2023

An intact oculomotor neural circuit in congenital blindness

In the past three decades, multiple studies revealed that congenital blindness is associated with functional and structural reorganization in early visual areas and its interaction with other neural systems. Among the most reproducible findings is the weaker connectivity between the visual and sensorimotor cortices, which in sighted individuals plays a role in eye-motor coordination. Here we demonstrate an important exception to this reorganization phenomena: we find that in congenitally blind individuals (as for normally sighted ones), spontaneous, non-controlled eye movements correlate with connectivity between visual and sensorimotor cortices. Furthermore, using time-lagged regression, we show that eye movements drive activity in the visual cortex, which subsequently drives sensorimotor activity. Remarkably, this phenomenon persists even though blind participants often exhibit unsynchronized eye movements and cannot sense or regulate their eye positions. These findings provide evidence of a preserved, "non-functional" connectivity in congenitally blind individuals and reveal a firm, hard-wired constraint on connectivity that remains immune to any reorganization phenomena produced by lack of visual experience and oculomotor control.

neuroscience↗

bioRxiv2022

Emergence of syntax and word prediction in an artificial neural circuit of the cerebellum

The cerebellum, interconnected with the cerebral neocortex, plays a vital role in human-characteristic cognition such as language processing, but knowledge about the underlying circuit computation of the cerebellum remains very limited. Here, to understand the computation underlying the cerebellar language processing, we developed a biologically-constrained cerebellar artificial neural network (cANN) model, which implemented the recently identified cerebello-cerebellar recurrent pathway. We found that while cANN acquires prediction of future words, another function of syntactic recognition emerges in the middle layer of the prediction circuit. The recurrent pathway of the cANN was essential for the two language functions, whereas cANN variants with further biological constraints preserved these functions. Considering the uniform structure of cerebellar circuitry across all functional domains, the single-circuit computation, which is the common basis of the two language functions, can be generalized to fundamental cerebellar functions of prediction and grammar-like rule extraction from sequences, that underpin a wide range of cerebellar motor and cognitive functions. This is a pioneering study to understand the circuit computation of human-characteristic cognition by biologically-constrained ANNs, which can lead new therapeutic interventions for human cognitive dysfunction.

neuroscience↗

bioRxiv2026

Structure, disorder, and dynamics in task-trained recurrent neural circuits

Across many brain areas, neurons produce heterogeneous, seemingly disordered responses. Yet such circuits cannot be purely random, since they must possess some structure to generate the representations and computations underlying behavior. How much structure is present in recurrent connectivity relative to disorder, and how the interaction between the two shapes population dynamics and single-neuron responses, remain incompletely understood. Recurrent neural networks trained to perform tasks have become a leading model of such circuits, but conventional training yields a single point in a vast space of task-compatible solutions, with no systematic way to explore this space and no theory of how internal representations vary within it. Without such a theory, the questions above cannot be addressed, and comparisons between trained networks and neural data are difficult to interpret. Here, we introduce a control parameter that governs the degree to which learning reshapes recurrent connectivity, interpolating between a reservoir regime and one in which recurrent weights are restructured by learning to produce task-relevant internal representations. Varying this parameter generates a family of task-compatible solutions whose internal dynamics differ in a controlled and interpretable way. We derive a dynamical mean-field theory showing that, while population-level dynamics converge to a deterministic limit, individual neurons are driven by independent samples from a single-neuron input-current distribution. When connectivity is random, this distribution is Gaussian. Recurrent restructuring drives it toward task-dependent, non-Gaussian forms. In linear networks, restructuring amplifies task-relevant frequencies. In nonlinear networks, it drives a phase transition from chaotic, high-dimensional activity to ordered, low-dimensional dynamics that generalize temporally beyond the training period. We apply the theory to a reaching task in which a recurrent network must reproduce macaque muscle activity, and find that optimally matching simultaneous motor-cortex recordings requires only a small degree of restructuring, with learned structure coexisting with random heterogeneity. These results suggest a broader picture in which large recurrent circuits are largely random but contain, to varying degrees, structured recurrent connectivity sufficient for generalizable, task-relevant representations.1

neuroscience↗

bioRxiv2026

A modular neural circuit for computing the motion of objects

Computing the direction of a moving object requires integrating motion signals from its component edges into coherent patterns. The representation of pattern motion in visual cortex has been extensively studied, yet its underlying neural circuitry remains unknown. Using high-density recordings in macaque area MT, we show that selectivity to pattern motion emerges from a functionally and anatomically distinct cortical circuit. We demonstrate that neurons specialized for encoding component and pattern motion comprise distinct cell types arranged in a hierarchical circuit in which pattern neurons integrate inputs from a range of component neurons. Furthermore, we show that component and pattern neurons are spatially segregated into modules arranged systematically across cortical columns encoding direction of motion. This architecture and circuit align with a classic solution to the problem of computing the motion of objects.

neuroscience↗

bioRxiv2021

Neural circuit dynamics of drug-context associative learning in the hippocampus

The environmental context associated with previous drug consumption serves as a potent trigger for relapse to drug use. The mechanism by which existing neural representations of context are modified to incorporate information associated with a given drug however, remains unknown. Using longitudinal calcium imaging in freely behaving mice, we reveal that drug-context associations for psychostimulants and opioids are encoded in a subset of hippocampal neurons. In these neurons, drug context pairing in a conditioned place preference task weakened their spatial coding for the nondrug-paired context, with drug-induced changes to spatial coding predictive of drug-seeking behavior. Furthermore, the dissociative drug ketamine blocked both the drug-induced changes to hippocampal coding and corresponding drug-seeking behavior. Together, this work reveals how drugs of abuse can alter the hippocampal circuit to encode drug-context associations and points to the hippocampus as a key node in the cognitive process of drug addiction and context-induced drug relapse.

neuroscience↗

bioRxiv2023

Neural circuit models for evidence accumulation through choice-selective sequences

Decision making is traditionally thought to be mediated by neurons that accumulate evidence through persistent activity. However, recent decision-making experiments in rodents have observed neurons across the brain that fire sequentially, rather than persistently, with the subset of neurons in the sequence depending on the animals choice. We developed two new candidate circuit models in which neurons are active sequentially and transfer evidence faithfully to the next active population. One model encodes evidence in the relative firing of two competing chains of neurons, and the other in the network location of a stereotyped pattern ("bump") of neural activity. Neural recordings from four brain regions during an evidence accumulation task revealed that different regions displayed evidence tuning consistent with different candidate models. This work provides a mechanistic explanation for how graded information may be precisely accumulated within and transferred between neural populations, and suggests that different brain regions may accumulate evidence through different circuit mechanisms.

neuroscience↗

bioRxiv2025

Brainstem enkephalinergic neural circuit underlying cold-induced pain relief in mice

Application of cold or cold-mimicking chemicals to injury has long been recognized as an effective means of pain relief and is widely utilized in daily life. However, underlying neural mechanisms remain elusive. Here, we identified a cold-responsive neuronal subset within lateral parabrachial nucleus (lPBN), the thermosensory relay region in the hindbrain, that mediates cold-induced analgesia in mice. Selective activation of these neurons and their projection to ventrolateral periaqueductal gray (vlPAG) increased nociceptive threshold via opioid receptor signaling in vlPAG. Conversely, ablation of these neurons attenuated analgesia induced by cold-mimicking chemicals. We further identified that these neurons express precursor gene of enkephalin, which is released into vlPAG for pain relief. Activation of cold-responsive neurons in descending pain modulation circuitry reduced spinal cord responses to noxious stimuli, suggesting the involvement of top-down pain modulation pathway. These findings propose a central mechanism underlying cold-induced pain relief, which could be a novel therapeutic target. TeaserCold-sensitive lPBN neurons release enkephalin into vlPAG for top-down analgesic action during cold-induced pain relief

neuroscience↗

bioRxiv2023

Antagonistic Neural Circuits Drive Opposing Behaviors towards the Young in Females

In many species, including mice, females show strikingly different pup-directed behaviors based on their reproductive state1,2. Naive wild female mice often kill pups while lactating females are dedicated to pup caring3,4. The neural mechanisms that mediate infanticide and its switch to maternal behaviors during motherhood remain unclear. Here, based on the hypothesis that maternal and infanticidal behaviors are supported by distinct and competing neural circuits5,6, we used the medial preoptic area (MPOA), a key site for maternal behaviors7-11, as a starting point and identified three MPOA-connected brain regions that drive differential negative pup-directed behaviors. Further functional manipulation and in vivo recording revealed that estrogen receptor alpha (Esr1) expressing cells in the principal nucleus of the bed nucleus of stria terminalis (BNSTprEsr1) are necessary, sufficient, and naturally activated during infanticide in female mice. Furthermore, MPOAEsr1 and BNSTprEsr1 neurons form reciprocal inhibition and change their excitability in opposite directions with reproductive state. The shift in balance between BNSTprEsr1 and MPOAEsr1 cell activity is likely a key mechanism for the behavioral switch during motherhood.

neuroscience↗

bioRxiv2017

Associations between memory traces emerge in a generic neural circuit model through STDP

Memory traces and associations between them are fundamental for cognitive brain function. Neuron recordings suggest that distributed assemblies of neurons in the brain serve as memory traces for spatial information, real-world items, and concepts. How-ever, there is conflicting evidence regarding neural codes for associated memory traces. Some studies suggest the emergence of overlaps between assemblies during an association, while others suggest that the assemblies themselves remain largely unchanged and new assemblies emerge as neural codes for associated memory items. Here we study the emergence of neural codes for associated memory items in a generic computational model of recurrent networks of spiking neurons with a data-constrained rule for spike-timing-dependent plasticity (STDP). The model depends critically on two parameters, which control the excitability of neurons and the scale of initial synaptic weights. By modifying these two parameters, the model can reproduce both experimental data from the human brain on the fast formation of associations through emergent overlaps between assemblies, and rodent data where new neurons are recruited to encode the associated memories. Hence our findings suggest that the brain can use both of these two neural codes for associations, and dynamically switch between them during consolidation.

neuroscience↗

bioRxiv2026

Glioblastoma Invasion Remodels Neural Circuits and Drives Persistent GABAergic Dysfunction in Human Brain Organoids

BackgroundGlioblastoma (GBM) is characterized by neurological dysfunction caused by tumor cells that interact with and alter neuronal circuits. However, the specific neuronal populations and molecular mechanisms most susceptible to GBM invasion remain poorly understood. MethodsWe created a human tumor-brain organoid model by combining U87 glioblastoma cells with iPSC-derived neural organoids. This system enabled us to study tumor-neural interactions over an extended period under standard temozolomide (TMZ) treatment. We used single-cell transcriptomics to monitor cell-type-specific responses. ResultsOur model recapitulated the diffuse infiltration observed in patients, leading to extensive structural remodeling and a profound loss of neuronal and glial populations. Single-cell analysis revealed that TMZ suppressed proliferative and biosynthetic programs but enriched for stress-responsive, mesenchymal-like, and therapy-adapted tumor states. Notably, GABAergic neurons exhibited the greatest transcriptional vulnerability, with [~]36% (7,499 of 20,659) of genes differentially expressed. Invasion triggered endoplasmic reticulum stress and shut down metabolic, respiratory, synaptic, and ion-homeostatic pathways. Crucially, SLC12A5-expressing GABAergic neurons plummeted from 31% to 12%, accompanied by a sharp decline in KCC2 protein expression. While TMZ partially rescued neuronal metabolic and electron transport chain function, it failed to restore SLC12A5/KCC2 expression or inhibitory signaling. ConclusionsGBM invasion leads to a continued imbalance of chloride in GABAergic networks, and this disruption remains even after undergoing tumor-targeted chemotherapy. This human iPSC-derived tumor-brain organoid platform provides a reliable and scalable system for studying complex tumor-neural interactions and exploring therapeutic approaches that aim to eliminate the tumor while preserving neural function.

cancer biology↗

bioRxiv2019

How neural circuits achieve and use stable dynamics

1The brain consists of many interconnected networks with time-varying activity. There are multiple sources of noise and variation yet activity has to eventually converge to a stable state for its computations to make sense. We approached this from a control-theory perspective by applying contraction analysis to recurrent neural networks. This allowed us to find mechanisms for achieving stability in multiple connected networks with biologically realistic dynamics, including synaptic plasticity and time-varying inputs. These mechanisms included anti-Hebbian plasticity, synaptic sparsity and excitatory-inhibitory balance. We leveraged these findings to construct networks that could perform functionally relevant computations in the presence of noise and disturbance. Our work provides a blueprint for how to construct stable plastic and distributed networks.

neuroscience↗

bioRxiv2021

Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits

Adaptive sequential behavior is a hallmark of human cognition. In particular, humans can learn to produce precise spatiotemporal sequences given a certain context. For instance, musicians can not only reproduce learned action sequences in a context-dependent manner, they can also quickly and flexibly reapply them in any desired tempo or rhythm without overwriting previous learning. Existing neural network models fail to account for these properties. We argue that this limitation emerges from the fact that sequence information (i.e., the position of the action) and timing (i.e., the moment of response execution) are typically stored in the same neural network weights. Here, we augment a biologically plausible recurrent neural network of cortical dynamics to include a basal ganglia-thalamic module which uses reinforcement learning to dynamically modulate action. This "associative cluster-dependent chain" (ACDC) model modularly stores sequence and timing information in distinct loci of the network. This feature increases computational power and allows ACDC to display a wide range of temporal properties (e.g., multiple sequences, temporal shifting, rescaling, and compositionality), while still accounting for several behavioral and neurophysiological empirical observations. Finally, we apply this ACDC network to show how it can learn the famous "Thunderstruck" song intro and then flexibly play it in a "bossa nova" rhythm without further training.

neuroscience↗

bioRxiv2020

Achieving stable dynamics in neural circuits

1The brain consists of many interconnected networks with time-varying, partially autonomous activity. There are multiple sources of noise and variation yet activity has to eventually converge to a stable, reproducible state (or sequence of states) for its computations to make sense. We approached this problem from a control-theory perspective by applying contraction analysis to recurrent neural networks. This allowed us to find mechanisms for achieving stability in multiple connected networks with biologically realistic dynamics, including synaptic plasticity and time-varying inputs. These mechanisms included inhibitory Hebbian plasticity, excitatory anti-Hebbian plasticity, synaptic sparsity and excitatory-inhibitory balance. Our findings shed light on how stable computations might be achieved despite biological complexity.

neuroscience↗

bioRxiv2020

Neural Circuit Dynamics for Sensory Detection

We consider the question of how sensory networks enable the detection of sensory stimuli in a combinatorial coding space. We are specifically interested in the olfactory system, wherein recent experimental studies have reported the existence of rich, enigmatic response patterns associated with stimulus onset and offset. This study aims to identify the functional relevance of such response patterns, i.e., what benefits does such neural activity provide in the context of detecting stimuli in a natural environment. We study this problem through the lens of normative, optimization-based modeling. Here, we define the notion of a low dimensional latent representation of stimulus identity, which is generated through action of the sensory network. The objective of our optimization framework is to ensure high fidelity tracking of a nominal representation in this latent space in an energy efficient manner. It turns out that the optimal motifs emerging from this framework possess morphological similarity with prototypical onset and offset responses observed in vivo. Furthermore, this objective can be exactly achieved by a network with reciprocal excitatory-inhibitory competitive dynamics, similar to interactions between principal neurons (PNs) and local neurons (LNs) in the early olfactory system of insects. The derived model also makes several predictions regarding maintenance of robust latent representations in the presence of confounding background information and tradeoffs between the energy of sensory activity and resultant behavioral measures such as speed and accuracy of stimulus detection. Significance StatementA key area of study in olfactory coding involves understanding the transformation from high-dimensional sensory stimulus to low-dimensional decoded representation. Here, we treat not only the dimensionality reduction of this mapping but also its temporal dynamics, with specific focus on stimuli that are temporally continuous. We examine through optimization-based synthesis how sensory networks can track representations without prior assumption of discrete trial structure. We show that such tracking can be achieved by canonical network architectures and dynamics, and that the resulting responses resemble observations from neurons in the insect olfactory system. Thus, our results provide hypotheses regarding the functional role of olfactory circuit activity at both single neuronal and population scales.

systems biology↗

bioRxiv2025

Locus Coeruleus to Medial Prefrontal Cortex Noradrenergic Neural Circuit Modulates States of Consciousness during Sevoflurane Anesthesia in Mice

How exactly does general anesthesia achieve its effects? It is so widely used in surgeries and medical treatments to enhance comfort for nearly one hundred years, yet its precise mechanisms underlying the loss and recovery of consciousness still remain unclear. Utilizing a variety of research methods-pharmacological, optogenetic, chemogenetic, fiber photometry, and gene knockdown approaches-our study has shed light on the significant modulatory function of locus coeruleus noradrenergic neurons during the transition from sevoflurane anesthesia to awakening. Furthermore, the activation of the LC-mPFC circuit has been found to have a substantial arousal effect, with 1-AR playing a crucial role in this process. Additionally, GABAA-R has been identified as the key binding site for sevoflurane within the locus coeruleus. These findings collectively offer novel insights into the neural network mechanisms underlying general anesthesia, advancing our understanding of this complex medical phenomenon.

neuroscience↗

bioRxiv2020

Neural circuit mechanisms of sexual receptivity in Drosophila females

Choosing a mate is one of the most consequential decisions a female will make during her lifetime. This is particularly true for species in which females either mate repeatedly with the same partner or mate infrequently but use the sperm from a single copulation to fertilize eggs over an extended period of time. Drosophila melanogaster uses the latter strategy. Here, we characterize the neural circuitry that implements mating decisions in the female brain. A female fly signals her mating choice by opening her vaginal plates to allow a courting male to copulate1,2. Vaginal plate opening (VPO) occurs in response to the male courtship song and is dependent upon the female's mating status. We sought to understand how these exteroceptive (song) and interoceptive (mating status) inputs are integrated to control VPO. We show that VPO is triggered by a pair of female-specific descending neurons, the vpoDNs. The vpoDNs receive excitatory input from vpoEN auditory neurons, which are tuned to specific features of the melanogaster song. The song responses of vpoDNs, but not vpoENs, are attenuated upon mating, accounting for the reduced receptivity of mated females. This modulation is mediated by pC1 neurons, which encode the females mating status3,4 and also provide excitatory input to vpoDNs. The vpoDNs thus directly integrate the external and internal signals to control the mating decisions of Drosophila females.

neuroscience↗

bioRxiv2026

Learning to select computations in recurrent neural circuits

Two hallmarks of biological computation are its flexibility and efficiency. These features are often attributed to cognitive control processes that balance external utility against computational cost. However, how the brain could implement such adaptive control remains unknown. Here, we provide one possible answer by combining the computational theory of rational meta-reasoning with a meta-learning algorithm recently proposed as a model of prefrontal cortex. This yields a recurrent neural network model that learns to select computations. In simple choice tasks, the model approximates the algorithms and representations of optimal symbolic models and reproduces neural dynamics observed in macaque orbitofrontal cortex. In multi-step planning tasks, the model replicates key behavioral signatures of human planning strategies and captures human neural dynamics associated with step-by-step mental simulation. Our framework unifies meta-reasoning and meta-learning by showing that learning to reason can be understood as learning to learn from information generated by ones own cognitive operations, providing a mechanistic account of how adaptive control of thought can be implemented in neural systems.

neuroscience↗

bioRxiv2025

A hardwired neural circuit for temporal difference learning

The neurotransmitter dopamine plays a major role in learning by acting as a teaching signal to update the brains predictions about rewards. A leading theory proposes that this process is analogous to a reinforcement learning algorithm called temporal difference (TD) learning, and that dopamine acts as the error term within the TD algorithm (TD error). Although many studies have demonstrated similarities between dopamine activity and TD errors1-5, the mechanistic basis for dopaminergic TD learning remains unknown. Here, we combined large-scale neural recordings with patterned optogenetic stimulation to examine whether and how the key steps in TD learning are accomplished by the circuitry connecting dopamine neurons and their targets. Replacing natural rewards with optogenetic stimulation of dopamine axons in the nucleus accumbens (NAc) in a classical conditioning task gradually generated TD error-like activity patterns in dopamine neurons by specifically modifying the task-related activity of NAc neurons expressing the D1 dopamine receptor (D1 neurons). In turn, patterned optogenetic stimulation of NAc D1 neurons in naive animals drove dopamine neuron spiking according to the TD error of the stimulation pattern, indicating that TD computations are hardwired into this circuit. The transformation from D1 neurons to dopamine neurons could be described by a biphasic linear filter, with a rapid positive and delayed negative phase, that effectively computes a temporal difference. This finding suggests that the time horizon over which the TD algorithm operates--the temporal discount factor--is set by the balance of the positive and negative components of the linear filter, pointing to a circuit-level mechanism for temporal discounting. These results provide a new conceptual framework for understanding how the computations and parameters governing animal learning arise from neurobiological components.

neuroscience↗