Neural Circuits Research Articles

Neural Basis of Number Sense in Larval Zebrafish.

Number sense, the ability to discriminate the quantity of objects, is crucial for survival. To understand how neurons work together and develop to mediate number sense, we used two-photon fluorescence light sheet microscopy to capture the activity of individual neurons throughout the brain of larval Danio rerio, while displaying a visual number stimulus to the animal. We identified number-selective neurons as early as 3 days post-fertilization and found a proportional increase of neurons tuned to larger quantities after 3 days. We used machine learning to predict the stimulus from the neuronal activity and observed that the prediction accuracy improves with age. We further tested ethanol's effect on number sense and found a decrease in number-selective neurons in the forebrain, suggesting cognitive impairment. These findings are a significant step towards understanding neural circuits devoted to discrete magnitudes and our methodology to track single-neuron activity across the whole brain is broadly applicable to other fields in neuroscience.

Network Structure and Reservoir Computing in Cultured Neuronal Networks

Network Structure and Reservoir Computing in Cultured Neuronal Networks

2P-NucTag: on-demand phototagging for molecular analysis of functionally identified cortical neurons.

Neural circuits are characterized by genetically and functionally diverse cell types. A mechanistic understanding of circuit function is predicated on linking the genetic and physiological properties of individual neurons. However, it remains highly challenging to map the transcriptional properties to functionally heterogeneous neuronal subtypes in mammalian cortical circuits in vivo. Here, we introduce a high-throughput two-photon nuclear phototagging (2P-NucTag) approach optimized for on-demand and indelible labeling of single neurons via a photoactivatable red fluorescent protein following in vivo functional characterization in behaving mice. We demonstrate the utility of this function-forward pipeline by selectively labeling and transcriptionally profiling previously inaccessible 'place' and 'silent' cells in the mouse hippocampus. Our results reveal unexpected differences in gene expression between these hippocampal pyramidal neurons with distinct spatial coding properties. Thus, 2P-NucTag opens a new way to uncover the molecular principles that govern the functional organization of neural circuits.

Ultralow Energy Consumption and Fast Neuromorphic Computing Based on La0.1Bi0.9FeO3 Ferroelectric Tunnel Junctions.

Low-power and fast artificial neural network devices represent the direction in developing analogue neural networks. Here, an ultralow power consumption (0.8 fJ) and rapid (100 ns) La0.1Bi0.9FeO3/La0.7Sr0.3MnO3 ferroelectric tunnel junction artificial synapse has been developed to emulate the biological neural networks. The visual memory and forgetting functionalities have been emulated based on long-term potentiation and depression with good linearity. Moreover, with a single device, logical operations of "AND" and "OR" are implemented, and an artificial neural network was constructed with a recognition accuracy of 96%. Especially for noisy data sets, the recognition speed is faster after preprocessing by the device in the present work. This sets the stage for highly reliable and repeatable unsupervised learning.

Robustly encoding certainty in a metastable neural circuit model.

Localized persistent neural activity can encode delayed estimates 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 along 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 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 equationsfor the amplitude and position of bump solutions in response to external stimuli and noise perturbations. Analysis of reduced equationsaccurately 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 experiments showing certainty correlates with more accurate memories.

Hypothalamic-hindbrain circuit for consumption-induced fear regulation

To ensure survival, animals must sometimes suppress fear responses triggered by potential threats during feeding. However, the mechanisms underlying this process remain poorly understood. In the current study, we demonstrated that when fear-conditioned stimuli (CS) were presented during food consumption, a neural projection from lateral hypothalamic (LH) GAD2 neurons to nucleus incertus (NI) relaxin-3 (RLN3)-expressing neurons was activated, leading to a reduction in CS-induced freezing behavior in male mice. LHGAD2 neurons established excitatory connections with the NI. The activity of this neural circuit, including NIRLN3 neurons, attenuated CS-induced freezing responses during food consumption. Additionally, the lateral mammillary nucleus (LM), which received NIRLN3 projections, along with RLN3 signaling in the LM, mediated the decrease in freezing behavior. Collectively, this study identified an LHGAD2-NIRLN3-LM circuit involved in modulating fear responses during feeding, thereby enhancing our understanding of how animals coordinate nutrient intake with threat avoidance.

Microglia in physiological conditions and the importance of understanding their homeostatic functions in the arcuate nucleus.

Microglia are highly dynamic cells that have been mainly studied under pathological conditions. The present review discusses the possible implication of microglia as modulators of neuronal electrical responses in physiological conditions and hypothesizes how these cells might modulate hypothalamic circuits in health and during obesity. Microglial cells studied under physiological conditions are highly diverse, depending on the developmental stage and brain region. The evidence also suggests that neuronal electrical activity modulates microglial motility to control neuronal excitability. Additionally, we show that the expression of genes associated with neuron-microglia interaction is down-regulated in obese mice compared to control-fed mice, suggesting an alteration in the contact-dependent mechanisms that sustain hypothalamic arcuate-median eminence neuronal function. We also discuss the possible implication of microglial-derived signals for the excitability of hypothalamic neurons during homeostasis and obesity. This review emphasizes the importance of studying the physiological interplay between microglia and neurons to maintain proper neuronal circuit function. It aims to elucidate how disruptions in the normal activities of microglia can adversely affect neuronal health.

Rosetta stone for the population dynamics of spiking neuron networks.

Populations of spiking neuron models have densities of their microscopic variables (e.g., single-cell membrane potentials) whose evolution fully capture the collective dynamics of biological networks, even outside equilibrium. Despite its general applicability, the Fokker-Planck equationgoverning such evolution is mainly studied within the borders of the linear response theory, although alternative spectral expansion approaches offer some advantages in the study of the out-of-equilibrium dynamics. This is mainly due to the difficulty in computing the state-dependent coefficients of the expanded system of differential equations. Here, we address this issue by deriving analytic expressions for such coefficients by pairing perturbative solutions of the Fokker-Planck approach with their counterparts from the spectral expansion. A tight relationship emerges between several of these coefficients and the Laplace transform of the interspike interval density (i.e., the distribution of first-passage times). "Coefficients" like the current-to-rate gain function, the eigenvalues of the Fokker-Planck operator and its eigenfunctions at the boundaries are derived without resorting to integral expressions. For the leaky integrate-and-fire neurons, the coupling terms between stationary and nonstationary modes are also worked out paving the way to accurately characterize the critical points and the relaxation timescales in networks of interacting populations.

A neural algorithm for computing bipartite matchings

Finding optimal bipartite matchings-e.g., matching medical students to hospitals for residency, items to buyers in an auction, or papers to reviewers for peer review-is a fundamental combinatorial optimization problem. We found a distributed algorithm for computing matchings by studying the development of the neuromuscular circuit. The neuromuscular circuit can be viewed as a bipartite graph formed between motor neurons and muscle fibers. In newborn animals, neurons and fibers are densely connected, but after development, each fiber is typically matched (i.e., connected) to exactly one neuron. We cast this synaptic pruning process as a distributed matching (or assignment) algorithm, where motor neurons "compete" with each other to "win" muscle fibers. We show that this algorithm is simple to implement, theoretically sound, and effective in practice when evaluated on real-world bipartite matching problems. Thus, insights from the development of neural circuits can inform the design of algorithms for fundamental computational problems.

Cerebellar involvement in Parkinson's disease: Pathophysiology and neuroimaging.

Parkinson's disease (PD) is a neurodegenerative disease characterized by various motor and non-motor symptoms. The complexity of its symptoms suggests that PD is a heterogeneous neurological disorder. Its pathological changes are not limited to the substantia nigra-striatal system, but gradually extending to other regions including the cerebellum. The cerebellum is connected to a wide range of central nervous system regions that form essential neural circuits affected by PD. In addition, altered dopaminergic activity and α-synuclein pathology are found in the cerebellum, further suggesting its role in the PD progression. Furthermore, an increasing evidence obtained from imaging studies has demonstrated that cerebellar structure, functional connectivity, and neural metabolism are altered in PD when compared to healthy controls, as well as among different PD subtypes. This review provides a comprehensive summary of the cerebellar pathophysiology and results from neuroimaging studies related to both motor and non-motor symptoms of PD, highlighting the potential significance of cerebellar assessment in PD diagnosis, differential diagnosis, and disease monitoring.

Morning resting hypothalamus-dorsal striatum connectivity predicts individual differences in diurnal sleepiness accumulation

While the significance of obtaining restful sleep at night and maintaining daytime alertness is well recognized for human performance and overall well-being, substantial variations exist in the development of sleepiness during diurnal waking periods. Despite the established roles of the hypothalamus and striatum in sleep-wake regulation, the specific contributions of this neural circuit in regulating individual sleep homeostasis remain elusive. This study utilized resting-state functional magnetic resonance imaging (fMRI) and mathematical modeling to investigate the role of hypothalamus-striatum connectivity in subjective sleepiness variation in a cohort of 71 healthy adults under strictly controlled in-laboratory conditions. Mathematical modeling results revealed remarkable individual differences in subjective sleepiness accumulation patterns measured by the Karolinska Sleepiness Scale (KSS). Brain imaging data demonstrated that morning hypothalamic connectivity to the dorsal striatum significantly predicts the individual accumulation of subjective sleepiness from morning to evening, while no such correlation was observed for the hypothalamus-ventral striatum connectivity. These findings underscore the distinct roles of hypothalamic connectivity to the dorsal and ventral striatum in individual sleep homeostasis, suggesting that hypothalamus-dorsal striatum circuit may be a promising target for interventions mitigating excessive sleepiness and promoting alertness.

Analysis of laser beam welding with superimposed 445 and 1070 nm wavelength lasers on copper by in situ synchrotron diagnostics

In laser welding, precision and reproducibility are fundamentally dependent on temporal and spatial processes of energy input. Induced by the dynamics of the melt pool, pressure equilibria in the vapor capillary, and solidification behavior, different weld seam qualities are achieved. To obtain the lowest possible defect frequency, new tailored joining strategies need to be investigated using multibeam and multiwavelength approaches. To improve the quality by influencing the process dynamics, a dual-beam approach is investigated that superimposes a stationary laser beam with a wavelength of 445 nm with a spatially modulated laser beam with a wavelength of 1070 nm. The aim is to utilize ∼10 times higher absorption of a 445 nm diode laser on copper with the high focusability of a 1070 nm fiber laser. In this context, the influence of the relative positions of the two beams to each other on the weld seam quality is investigated, while one of the beams moves either in front, behind, or coaxial to the other beam following the path of a line weld. The main objective is to observe how the laser beams influence each other and how the capillary depth and porosity vary for different parameters. To visualize the process dynamics, the welding experiments on copper are performed at the Deutsches Elektronen-Synchrotron DESY by means of in situ phase contrast videography. Quality-determining weld properties like the distribution of pores or process fluctuations are then extracted automatically from the image sequences by means of a trained neuronal network.

Modeling seizure networks in neuron-glia cultures using microelectrode arrays.

Epilepsy is a common neurological disorder, affecting over 65 million people worldwide. Unfortunately, despite resective surgery, over 30 of patients with drug-resistant epilepsy continue to experience seizures. Retrospective studies considering connectivity using intracranial electrocorticography (ECoG) obtained during neuromonitoring have shown that treatment failure is likely driven by failure to consider critical components of the seizure network, an idea first formally introduced in 2002. However, current studies only capture snapshots in time, precluding the ability to consider seizure network development. Over the past few years, multiwell microelectrode arrays have been increasingly used to study neuronal networks in vitro. As such, we sought to develop a novel in vitro MEA seizure model to allow for study of seizure networks. Specifically, we used 4-aminopyridine (4-AP) to capture hyperexcitable activity, and then show increased network changes after 2days of chronic treatment. We characterize network changes using functional connectivity measures and a novel technique using dimensionality reduction. We find that 4-AP successfully captures persistently elevated mean firing rate and significant changes in underlying connectivity patterns. We believe this affords a robust in vitro seizure model from which longitudinal network changes can be studied, laying groundwork for future studies exploring seizure network development.

Cerebellar activity in PINK1 knockout rats during volitional gait.

Preclinical models of Parkinson's disease are imperative to gain insight into the neural circuits that contribute to gait dysfunction in advanced stages of the disease. A PTEN-induced putative kinase 1 knockout early-onset model of Parkinson's disease may be a useful rodent model to study the effects of neurotransmitter degeneration caused by a loss of PTEN-induced putative kinase 1 function on brain activity during volitional gait. The goal of this study was to measure changes in neural activity at the cerebellar vermis at 8 months of age. It was found that gait deficits, except run speed, were not significantly different from age-matched wild-type controls, as previously reported. PTEN-induced putative kinase 1 knockout (n = 4) and wild-type (n = 4) rats were implanted with a micro-electrocorticographic array placed over cerebellar vermis Lobules VI (a-c) and VII. Local field potential recordings were obtained during volitional gait across a runway. Power spectral analysis and coherence analysis were used to quantify network oscillatory activity in frequency bands of interest. Cerebellar vermis power was hypoactive in the beta (VIb, VIc and VII) and alpha (VII) bands at cerebellar vermis Lobules VIb, VIc and VII in PTEN-induced putative kinase 1 knockout rats compared with wild-type controls during gait (P < 0.05). These results suggest that gait improvement in PTEN-induced putative kinase 1 knockout rats at 8 months may be a compensatory mechanism attributed to movement corrections caused by a decreased inhibition of the alpha band of cerebellar vermis Lobule VII and beta band of Lobules VIb, VIc and VII. The PTEN-induced putative kinase 1 knockout model may be a valuable tool for understanding the circuit mechanisms underlying gait dysfunction in patients with early-onset Parkinson's disease with a functional loss of PTEN-induced putative kinase 1. Future studies investigating the cerebellar vermis as a potential biomarker and therapeutic target for the treatment of gait dysfunction in Parkinson's disease are warranted.

Control electromechanical arms by using a neural circuit

Control electromechanical arms by using a neural circuit

Synchronization evaluation of memristive photosensitive neurons in multi-neuronal systems

At the forefront of brain-computer interface (BCI) technology exploration, the synchronization phenomenon in neural networks demonstrates significant potential for application. This paper focuses on the memory characteristics and nonlinear dynamic behavior of memristors, an emerging electronic component. By innovatively integrating memristors, phototubes, and FitzHugh-Nagumo (FHN) circuits, we construct a memristive photosensitive neuron (MPN) model, aiming to explore its unique role in neural network synchronization. Utilizing the memristors' inherent memory capabilities and nonlinear properties, the MPN model mimics the dynamic behavior of biological neurons. Experiments show that synchronization is highly sensitive to the memristor's initial values, revealing intricate synchronization regulation mechanisms. Furthermore, we find that chaotic electrical currents, as environmental disturbances, have dual effects—either promoting or inhibiting MPN network synchronization—depending on specific parametric conditions. To simulate a realistic biological neural network and achieve efficient coupling among multiple MPN units, this paper adopts a Hopfield network structure. The results indicate that this structure significantly enhances the synchronization stability of the system, reduces sensitivity to initial conditions, and mitigates the adverse effects of chaotic currents. This research not only enhances our understanding of neural network synchronization but also provides novel theoretical support and technical pathways for the development of BCI technology, indicating broad application prospects in neuroscience, rehabilitative medicine, and other fields.

Neural response to monetary incentives in acquired adolescent depression after mild traumatic brain injury: Stage 2 Registered Report.

Depression is a common consequence of traumatic brain injury. Separately, spontaneous depression-arising without brain injury-has been linked to abnormal responses in motivational neural circuitry to the anticipation or receipt of rewards. It is unknown if post-injury and spontaneously occurring depression share similar phenotypic profiles. This issue is compounded by the fact that nearly all examinations of these psychiatric sequelae are post hoc: there are rarely any prospective assessments of mood and neural functioning before and after a brain injury. In this Stage 2 Registered Report, we used the Adolescent Brain Cognitive Development Consortium dataset to examine if a disruption in functional neural responses to rewards is present in patients with depression after a mild traumatic brain injury. Notably, this study provides an unparalleled opportunity to examine the trajectory of neuropsychiatric symptoms longitudinally within-subjects. This allowed us to isolate mild traumatic brain injury-specific variance independent from pre-existing functioning. Here, we focus on a case-control comparison between 43 youth who experienced a mild traumatic brain injury between MRI visits, and 43 well-matched controls. Contrary to pre-registered predictions (https://osf.io/h5uba/), there was no statistically credible increase in depression in mild traumatic brain injury cases relative to controls. Mild traumatic brain injury was associated with subtle changes in motivational neural circuit recruitment during the anticipation of incentives on the Monetary Incentive Delay paradigm. Specifically, changes in neural recruitment appeared to reflect a failure to deactivate 'task-negative' brain regions (ventromedial prefrontal cortex), alongside blunted recruitment of 'task-positive' regions (anterior cingulate, anterior insula and caudate), during the anticipation of reward and loss in adolescents following mild brain injuries. Critically, these changes in brain activity were not correlated with depressive symptoms at either visit or depression change scores before and after the brain injury. Increased time since injury was associated with a recovery of cognitive functioning-driven primarily by processing speed differences-but depression did not scale with time since injury. These cognitive changes were also uncorrelated with neural changes after mild traumatic brain injury. This report provides evidence that acquired depression may not be observed as commonly after a mild traumatic brain injury in late childhood and early adolescence, relative to findings in adult cases. Several reasons for these differing findings are considered, including sampling enrichment in retrospective cohort studies, under-reporting of depressive symptoms in parent-report data, and neuroprotective factors in childhood and adolescence.

Hippocampal storage and recall of neocortical "What"-"Where" representations.

A key question for understanding the function of the hippocampus in memory is how information is recalled from the hippocampus to the neocortex. This was investigated in a neuronal network model of the hippocampal system in which "What" and "Where" neuronal firing rate vectors were applied to separate neocortical modules, which then activated entorhinal cortex "What" and "Where" modules, then the dentate gyrus, then CA3, then CA1, then the entorhinal cortex, and then the backprojections to the neocortex. A rate model showed that the whole system could be trained to recall "Where" in the neocortex from "What" applied as a retrieval cue to the neocortex, and could in principle be trained up towards the theoretical capacity determined largely by the number of synapses onto any one neuron divided by the sparseness of the representation. The trained synaptic weights were then imported into an integrate-and-fire simulation of the same architecture, which showed that the time from presenting a retrieval cue to a neocortex module to recall the whole memory in the neocortex is approximately 100 ms. This is sufficiently fast for the backprojection synapses to be trained onto the still active neocortical neurons during storage of the episodic memory, and this is needed for recall to operate correctly to the neocortex. These simulations also showed that the long loop neocortex-hippocampus-neocortex that operates continuously in time may contribute to complete recall in the neocortex; but that this positive feedback long loop makes the whole dynamical system inherently liable to a pathological increase in neuronal activity. Important factors that contributed to stability included increased inhibition in CA3 and CA1 to keep the firing rates low; and temporal adaptation of the neuronal firing and of active synapses, which are proposed to make an important contribution to stabilizing runaway excitation in cortical circuits in the brain.

Agrypnia excitata: a human model to explore the derailment of sleep-wake cycle integrated control.

The commemoration of the 70th anniversary of rapid eye movement sleep discovery offers a unique possibility to reassess the peculiar organic condition of agrypnia excitata. Agrypnia excitata is characterized by a severe loss of sleep leading to a complete derangement of physiological sleep-wake cycle and body homeostasis. Agrypnia excitata is a definite clinico-neurophysiological condition characterized by: (1) slow-wave sleep loss with disruption of sleepwake cycle; (2) a 24-hr motor and autonomic overactivity; and (3) peculiar episodes of oneiric stupor. Agrypnia excitata may happen within different pathophysiologies, such as delirium tremens, Morvan's syndrome and fatal familial insomnia, suggesting some general reflections on the composition and function of the cerebral neuronal network generating wake and sleep behaviour and regulating body homeostasis, with a focus on rapid eye movement sleep.

Involvement of basolateral amygdala-rostral anterior cingulate cortex in mechanical allodynia and anxiety-like behaviors and potential mechanisms of electroacupuncture.

Chronic pain is highly associated with anxiety. Electroacupuncture (EA) is effective in relieving pain and anxiety. Currently, little is known about the neural mechanisms underlying the comorbidity of chronic pain and anxiety and the EA mechanism. This study investigated a potential neural circuit underlying the comorbid and EA mechanisms. Spared nerve injury (SNI) surgery established the chronic neuropathic pain mouse model. The neural circuit was activated or inhibited using the chemogenetic method to explore the relationship between the neural circuit and mechanical allodynia and anxiety-like behaviors. EA combined with the chemogenetic method was used to explore whether the effects of EA were related to this neural circuit. EA attenuated mechanical allodynia and anxiety-like behaviors in SNI mice, which may be associated with the activity of CaMKII neurons in the basolateral amygdala (BLA). Inhibition of BLACaMKII-rACC induced mechanical allodynia and anxiety-like behaviors in sham mice. Activation of the BLACaMKII-rACC alleviated neuropathic pain and anxiety-like behaviors in SNI mice. The analgesic and anxiolytic effects of 2 Hz EA were antagonized by the inhibition of the BLACaMKII-rACC. BLACaMKII-rACC mediates mechanical allodynia and anxiety-like behaviors. The analgesic and anxiolytic effects of 2 Hz EA may be associated with the BLACaMKII-rACC.