Neural Circuits Research Articles

Quantifying changes in oxygen saturation of the internal jugular vein in vivo using deep neuron networks and subject-specific three-dimensional Monte Carlo models

Quantifying changes in oxygen saturation of the internal jugular vein in vivo using deep neuron networks and subject-specific three-dimensional Monte Carlo models

Reimagining Cortical Connectivity by Deconstructing Its Molecular Logic into Building Blocks.

Comprehensive maps of neuronal connectivity provide a foundation for understanding the structure of neural circuits. In a circuit, neurons are diverse in morphology, electrophysiology, gene expression, activity, and other neuronal properties. Thus, constructing a comprehensive connectivity map requires associating various properties of neurons, including their connectivity, at cellular resolution. A commonly used approach is to use the gene expression profiles as an anchor to which all other neuronal properties are associated. Recent advances in genomics and anatomical techniques dramatically improved the ability to determine and associate the long-range projections of neurons with their gene expression profiles. These studies revealed unprecedented details of the gene-projection relationship, but also highlighted conceptual challenges in understanding this relationship. In this article, I delve into the findings and the challenges revealed by recent studies using state-of-the-art neuroanatomical and transcriptomic techniques. Building upon these insights, I propose an approach that focuses on understanding the gene-projection relationship through basic features in gene expression profiles and projections, respectively, that associate with underlying cellular processes. I then discuss how the developmental trajectories of projections and gene expression profiles create additional challenges and necessitate interrogating the gene-projection relationship across time. Finally, I explore complementary strategies that, together, can provide a comprehensive view of the gene-projection relationship.

Lineage Analysis and Birthdating of Drosophila Central Complex Lineages.

From insects to humans, the nervous system generates complex behaviors mediated by distinct neural circuits that are composed of diverse cell types. During development, the spatiotemporal gene expression of the neural progenitors expands the diversity of neuronal and glial subtypes. Various neural stem cell-intrinsic and -extrinsic gene programs have been identified that are thought to play a major role in generating diverse neuronal and glial cell types. Drosophila has served as an excellent model system for discovering the fundamental principles of nervous system development and function. The sophisticated genetic tools allow us to link the origin and birth timing (the time when a particular neuron is born during development) of neuron types to unique neural stem cells (NSCs) and to a developmental time. In Drosophila, a special class of NSCs called Type II NSCs has adopted a more advanced division mode to generate lineages for the higher-order brain center, the central complex, which is an evolutionarily conserved brain region found in all insects. Type II NSCs, similar to the human outer radial glia, generate intermediate neural progenitors (INPs), which divide many times to produce about eight to 10 progeny. Both Type II NSCs and INPs express distinct transcription factors and RNA-binding proteins that have been proposed to regulate the specification of cell types populating the adult central complex. Here, we describe the recently invented lineage filtering system, called cell class-lineage intersection (CLIn), which enables the tracking and birthdating of the Type II NSC lineages. Using CLIn, one can easily generate clones of different Type II NSCs and identify not only the origins of neurons of interest but also their birth time.

Multi-scale modelling of the epileptic brain: advantages of computational therapy exploration.

Epilepsy is a complex disease spanning across multiple scales, from ion channels in neurons to neuronal circuits across the entire brain. Over the past decades, computational models have been used to describe the pathophysiological activity of the epileptic brain from different aspects. Traditionally, each computational model can aid in optimizing therapeutic interventions, therefore, providing a particular view to design strategies for treating epilepsy. As a result, most studies are concerned with generating specific models of the epileptic brain that can help us understand the certain machinery of the pathological state. Those specific models vary in complexity and biological accuracy, with system-level models often lacking biological details. Here, we review various types of computational models of epilepsy and discuss their potential for different therapeutic approaches and scenarios, including drug discovery, surgical strategies, brain stimulation, and seizure prediction. We propose that we need to consider an integrated approach with a unified modelling framework across multiple scales to understand the epileptic brain. Our proposal is based on the recent increase in computational power, which has opened up the possibility of unifying those specific epileptic models into simulations with an unprecedented level of detail. A multi-scale epilepsy model can bridge the gap between biologically detailed models, used to address molecular and cellular questions, and brain-wide models based on abstract models which can account for complex neurological and behavioral observations. With these efforts, we move toward the next generation of epileptic brain models capable of connecting cellular features, such as ion channel properties, with standard clinical measures such as seizure severity.

Controlling Algorithm of Reconfigurable Battery for State of Charge Balancing Using Amortized Q-Learning

In the context of the electrification of the mobility sector, smart algorithms have to be developed to control battery packs. Smart and reconfigurable batteries are a promising alternative to conventional battery packs and offer new possibilities for operation and condition monitoring. This work proposes a reinforcement learning (RL) algorithm to balance the State of Charge (SoC) of reconfigurable batteries based on the topologies half-bridge and battery modular multilevel management (BM3). As an RL algorithm, Amortized Q-learning (AQL) is implemented, which enables the control of enormous numbers of possible configurations of the reconfigurable battery as well as the combination of classical controlling approaches and machine learning methods. This enhances the safety mechanisms during control. As a neural network of the AQL, a Feedforward Neuronal Network (FNN) is implemented consisting of three hidden layers. The experimental evaluation using a 12-cell hybrid cascaded multilevel converter illustrates the applicability of the method to balance the SoC and maintain the balanced state during discharge. The evaluation shows a 20.3% slower balancing process compared to a conventional approach. Nevertheless, AQL shows great potential for multiobjective optimizations and can be applied as an RL algorithm for control in power electronics.

Probability-Distribution-Configurable True Random Number Generators Based on Spin-Orbit Torque Magnetic Tunnel Junctions.

The incorporation of randomness into stochastic computing can provide ample opportunities for applications such as simulated annealing, non-polynomial hard problem solving, and Bayesian neuron networks. In these cases, a considerable number of random numbers with an accurate and configurable probability distribution function (PDF) are indispensable. Preferably, these random numbers are provided at the hardware level to improve speed, efficiency, and parallelism. In this paper, how spin-orbit torque magnetic tunnel junctions (SOT-MTJs) with high barriers are suitable candidates for the desired true random number generators is demonstrated. Not only do these SOT-MTJs perform excellently in speed and endurance, but their randomness can also be conveniently and precisely controlled by a writing voltage, which makes them a well-performed Bernoulli bit. By utilizing these SOT-MTJ-based Bernoulli bits, any PDF, including Gaussian, uniform, exponential, Chi-square, and even arbitrarily defined distributions can be realized. These PDF-configurable true random number generators can then promise to advance the development of stochastic computing and broaden the applications of the SOT-MTJs.

Reward positivity affects temporal interval production in a continuous timing task.

The neural circuits of reward processing and interval timing (including the perception and production of temporal intervals) are functionally intertwined, suggesting that it might be possible for momentary reward processing to influence subsequent timing behavior. Previous animal and human studies have mainly focused on the effect of reward on interval perception, whereas its impact on interval production is less clear. In this study, we examined whether feedback, as an example of performance-contingent reward, biases interval production. We recorded EEG from 20 participants while they engaged in a continuous drumming task with different realistic tempos (1728 trials per participant). Participants received color-coded feedback after each beat about whether they were correct (on time) or incorrect (early or late). Regression-based EEG analysis was used to unmix the rapid occurrence of a feedback response called the reward positivity (RewP), which is traditionally observed in more slow-paced tasks. Using linear mixed modeling, we found that RewP amplitude predicted timing behavior for the upcoming beat. This performance-biasing effect of the RewP was interpreted as reflecting the impact of fluctuations in reward-related anterior cingulate cortex activity on timing, and the necessity of continuous paradigms to make such observations was highlighted.

SiC@NiO Core-Shell Nanowire Networks-Based Optoelectronic Synapses for Neuromorphic Computing and Visual Systems at High Temperature.

1D nanowire networks, sharing similarities of structure, information transfer, and computation with biological neural networks, have emerged as a promising platform for neuromorphic systems. Based on brain-like structures of 1D nanowire networks, neuromorphic synaptic devices can overcome the von Neumann bottleneck, achieving intelligent high-efficient sensing and computing function with high information processing rates and low power consumption. Here, high-temperature neuromorphic synaptic devices based on SiC@NiO core-shell nanowire networks optoelectronic memristors (NNOMs) are developed. Experimental results demonstrate that NNOMs attain synaptic short/long-term plasticity and modulation plasticity under both electrical and optical stimulation, and exhibit advanced functions such as short/long-term memory and "learning-forgetting-relearning" under optical stimulation at both room temperature and 200°C. Based on the advanced functions under light stimulus, the constructed 5×3 optoelectronic synaptic array devices exhibit a stable visual memory function up to 200°C, which can be utilized to develop artificial visual systems. Additionally, when exposed to multiple electronic or optical stimuli, the NNOMs effectively replicate the principles of Pavlovian classical conditioning, achieving visual heterologous synaptic functionality and refining neural networks. Overall, with abundant synaptic characteristics and high-temperature thermal stability, these neuromorphic synaptic devices offer a promising route for advancing neuromorphic computing and visual systems.

Meta predictive learning model of languages in neural circuits

Meta predictive learning model of languages in neural circuits

From Blur to Brilliance: The Ascendance of Advanced Microscopy in Neuronal Cell Biology.

The intricate network of the brain's neurons and synapses poses unparalleled challenges for research, distinct from other biological studies. This is particularly true when dissecting how neurons and their functional units work at a cell biological level. While traditional microscopy has been foundational, it was unable to reveal the deeper complexities of neural interactions. However, an imaging renaissance has transformed our capabilities. Advancements in light and electron microscopy, combined with correlative imaging, now achieve unprecedented resolutions, uncovering the most nuanced neural structures. Maximizing these tools requires more than just technical proficiency. It is crucial to align research aims, allocate resources wisely, and analyze data effectively. At the heart of this evolution is interdisciplinary collaboration, where various experts come together to translate detailed imagery into significant biological insights. This review navigates the latest developments in microscopy, underscoring both the promise of and prerequisites for bending this powerful tool set to understanding neuronal cell biology.

Fast learning without synaptic plasticity in spiking neural networks

Spiking neural networks are of high current interest, both from the perspective of modelling neural networks of the brain and for porting their fast learning capability and energy efficiency into neuromorphic hardware. But so far we have not been able to reproduce fast learning capabilities of the brain in spiking neural networks. Biological data suggest that a synergy of synaptic plasticity on a slow time scale with network dynamics on a faster time scale is responsible for fast learning capabilities of the brain. We show here that a suitable orchestration of this synergy between synaptic plasticity and network dynamics does in fact reproduce fast learning capabilities of generic recurrent networks of spiking neurons. This points to the important role of recurrent connections in spiking networks, since these are necessary for enabling salient network dynamics. We show more specifically that the proposed synergy enables synaptic weights to encode more general information such as priors and task structures, since moment-to-moment processing of new information can be delegated to the network dynamics.

Structure and function of GluN1-3A NMDA receptor excitatory glycine receptor channel.

N-methyl-d-aspartate receptors (NMDARs) and other ionotropic glutamate receptors (iGluRs) mediate most of the excitatory signaling in the mammalian brains in response to the neurotransmitter glutamate. Uniquely, NMDARs composed of GluN1 and GluN3 are activated exclusively by glycine, the neurotransmitter conventionally mediating inhibitory signaling when it binds to pentameric glycine receptors. The GluN1-3 NMDARs are vital for regulating neuronal excitability, circuit function, and specific behaviors, yet our understanding of their functional mechanism at the molecular level has remained limited. Here, we present cryo-electron microscopy structures of GluN1-3A NMDARs bound to an antagonist, CNQX, and an agonist, glycine. The structures show a 1-3-1-3 subunit heterotetrameric arrangement and an unprecedented pattern of GluN3A subunit orientation shift between the glycine-bound and CNQX-bound structures. Site-directed disruption of the unique subunit interface in the glycine-bound structure mitigated desensitization. Our study provides a foundation for understanding the distinct structural dynamics of GluN3 that are linked to the unique function of GluN1-3 NMDARs.

The homeostatic effects of the RE-1 silencing transcription factor on cortical networks are altered under ictogenic conditions in the mouse.

The Repressor Element-1 Silencing Transcription Factor (REST) is an epigenetic master regulator playing a crucial role in the nervous system. In early developmental stages, REST downregulation promotes neuronal differentiation and the acquisition of the neuronal phenotype. In addition, postnatal fluctuations in REST expression contribute to shaping neuronal networks and maintaining network homeostasis. Here we investigate the role of the early postnatal deletion of neuronal REST in the assembly and strength of excitatory and inhibitory synaptic connections. We investigated excitatory and inhibitory synaptic transmission by patch-clamp recordings in acute neocortical slices in a conditional knockout mouse model (RestGTi) in which Rest was deleted by delivering PHP.eB adeno-associated viruses encoding CRE recombinase under the control of the human synapsin I promoter in the lateral ventricles of P0-P1 pups. We show that, under physiological conditions, Rest deletion increased the intrinsic excitability of principal cortical neurons in the primary visual cortex and the density and strength of excitatory synaptic connections impinging on them, without affecting inhibitory transmission. Conversely, in the presence of a pathological excitation/inhibition imbalance induced by pentylenetetrazol, Rest deletion prevented the increase in synaptic excitation and decreased seizure severity. The data indicate that REST exerts distinct effects on the excitability of cortical circuits depending on whether it acts under physiological conditions or in the presence of pathologic network hyperexcitability. In the former case, REST preserves a correct excitatory/inhibitory balance in cortical circuits, while in the latter REST loses its homeostatic activity and may become pro-epileptogenic.

Hox gene–specific cellular targeting using split intein Trojan exons

The Trojan exon method, which makes use of intronically inserted T2A-Gal4 cassettes, has been widely used in Drosophila to create thousands of gene-specific Gal4 driver lines. These dual-purpose lines provide genetic access to specific cell types based on their expression of a native gene while simultaneously mutating one allele of the gene to enable loss-of-function analysis in homozygous animals. While this dual use is often an advantage, the truncation mutations produced by Trojan exons are sometimes deleterious in heterozygotes, perhaps by creating translation products with dominant negative effects. Such mutagenic effects can cause developmental lethality as has been observed with genes encoding essential transcription factors. Given the importance of transcription factors in specifying cell type, alternative techniques for generating specific Gal4 lines that target them are required. Here, we introduce a modified Trojan exon method that retains the targeting fidelity and plug-and-play modularity of the original method but mitigates its mutagenic effects by exploiting the self-splicing capabilities of split inteins. "Split Intein Trojan exons" (siTrojans) ensure that the two truncation products generated from the interrupted allele of the native gene are trans-spliced to create a full-length native protein. We demonstrate the efficacy of siTrojans by generating a comprehensive toolkit of Gal4 and Split Gal4 lines for the segmentally expressed Hox transcription factors and illustrate their use in neural circuit mapping by targeting neurons according to their position along the anterior-posterior axis. Both the method and the Hox gene-specific toolkit introduced here should be broadly useful.

Neural signatures of indirect pathway activity during subthalamic stimulation in Parkinson’s disease

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) produces an electrophysiological signature called evoked resonant neural activity (ERNA); a high-frequency oscillation that has been linked to treatment efficacy. However, the single-neuron and synaptic bases of ERNA are unsubstantiated. This study proposes that ERNA is a subcortical neuronal circuit signature of DBS-mediated engagement of the basal ganglia indirect pathway network. In people with Parkinson’s disease, we: (i) showed that each peak of the ERNA waveform is associated with temporally-locked neuronal inhibition in the STN; (ii) characterized the temporal dynamics of ERNA; (iii) identified a putative mesocircuit architecture, embedded with empirically-derived synaptic dynamics, that is necessary for the emergence of ERNA in silico; (iv) localized ERNA to the dorsal STN in electrophysiological and normative anatomical space; (v) used patient-wise hotspot locations to assess spatial relevance of ERNA with respect to DBS outcome; and (vi) characterized the local fiber activation profile associated with the derived group-level ERNA hotspot.

Roger Sperry, the maverick brain scientist who was haunted by psyche

This paper describes the scientific figure of Roger Sperry as a maverick researcher, an original thinker who arrived at definitive notions about the working of the brain mostly by distancing himself from the prevalent views of his peers. After solving the riddle of the functions of the corpus callosum, he won a Nobel prize in physiology or medicine for identifying the different cognitive abilities of the disconnected right and left hemispheres of the human brain. He could have won another Nobel prize for his work on the prenatal formation of behavioral neuronal networks and their growth and development after birth. In the last part of his life, he fought a courageous but inconclusive battle for demonstrating that mental and spiritual factors can direct brain activity and behavior without violating the laws of orthodox neurophysiology. Some nodal points in his scientific career and some sources of inspirations for his thinking are identified and discussed within the historical background of the neurosciences of the twentieth century.

Stereo-EEG features of temporal and frontal lobe seizures with loss of consciousness.

The loss of consciousness (LOC) during seizures is one of the most striking features that significantly impact the quality of life, even though the neuronal network involved is not fully comprehended. We analyzed the intracerebral patterns in patients with focal drug-resistant epilepsy, both with and without LOC. We assessed the localization, lateralization, stereo electroencephalography (SEEG) patterns, seizure duration, and the quantification of contacts exhibiting electrical discharge. The degree of LOC was quantified using the Consciousness Seizure Scale. Thirteen patients (40 seizures) with focal drug-resistant epilepsy underwent SEEG. In cases of temporal lobe epilepsy (TLE, 6 patients and 15 seizures), LOC occurred more frequently in seizures with mesial rather than lateral temporal lobe onset. On the other hand, in cases of frontal lobe epilepsy (7 patients; 25 seizures), LOC was associated with pre-frontal onset, a higher number of contacts with epileptic discharge compared to the onset count and longer seizure durations. Our study revealed distinct characteristics during LOC depending on the epileptogenic zone. For temporal lobe seizures, LOC was associated with mesial seizure onset, whereas in frontal lobe epilepsy, seizure with LOC has a significant increase in contact showing epileptiform discharge and a pre-frontal onset. This phenomenon may be correlated with the broad neural network required to maintain consciousness, which can be affected in different ways, resulting in LOC.

Complex activity and short-term plasticity of human cerebral organoids reciprocally connected with axons

An inter-regional cortical tract is one of the most fundamental architectural motifs that integrates neural circuits to orchestrate and generate complex functions of the human brain. To understand the mechanistic significance of inter-regional projections on development of neural circuits, we investigated an in vitro neural tissue model for inter-regional connections, in which two cerebral organoids are connected with a bundle of reciprocally extended axons. The connected organoids produced more complex and intense oscillatory activity than conventional or directly fused cerebral organoids, suggesting the inter-organoid axonal connections enhance and support the complex network activity. In addition, optogenetic stimulation of the inter-organoid axon bundles could entrain the activity of the organoids and induce robust short-term plasticity of the macroscopic circuit. These results demonstrated that the projection axons could serve as a structural hub that boosts functionality of the organoid-circuits. This model could contribute to further investigation on development and functions of macroscopic neuronal circuits in vitro.

Spontaneous intersibling polymorphism in the development of dopaminergic neuroendocrine cells in sea urchin larvae: impacts on the expansion of marine benthic species

IntroductionThe plasticity of the nervous system plays a crucial role in shaping adaptive neural circuits and corresponding animal behaviors. Understanding the mechanisms underlying neural plasticity during development and its implications for animal adaptation constitutes an intriguing area of research. Sea urchin larvae offer a fascinating subject for investigation due to their remarkable evolutionary and ecological diversity, as well as their diverse developmental forms and behavioral patterns.Materials and methodsWe conducted immunochemical and histochemical analyses of serotonin-containing (5-HT-neurons) and dopamine-containing (DA-positive) neurons to study their developmental dynamics in two sea urchin species: Mesocentrotus nudus and Paracentrotus lividus. Our approach involved detailed visualization of 5-HT- and DA-positive neurons at gastrula-pluteus stages, coupled with behavioral assays to assess larval upward and downward swimming in the water column, with a focus on correlating cell numbers with larval swimming ability.ResultsThe study reveals a heterochronic polymorphism in the appearance of post-oral DA-positive neuroendocrine cells and confirms the stable differentiation pattern of apical 5-HT neurons in larvae of both species. Notably, larvae of the same age exhibit a two- to four-fold difference in DA neurons. An increased number of DA neurons and application of dopamine positively correlate with larval downward swimming, whereas 5-HT-neurons and serotonin application induce upward swimming. The ratio of 5-HT/DA neurons determines the stage-dependent vertical distribution of larvae within the water column. Consequently, larvae from the same generation with a higher number of DA-positive neurons tend to remain at the bottom compared to those with fewer DA-positive neurons.DiscussionThe proportion of 5-HT and DA neurons within larvae of the same age underlies the different potentials of individuals for upward and downward swimming. A proposed model illustrates how coordination in humoral regulation, based on heterochrony in DA-positive neuroendocrine cell differentiation, influences larval behavior, mitigates competition between siblings, and ensures optimal population expansion. The study explores the evolutionary and ecological implications of these neuroendocrine adaptations in marine species.

Highly biomimetic spiking neuron using SiGe heterojunction bipolar transistors for energy-efficient neuromorphic systems

We demonstrate a highly biomimetic spiking neuron capable of fast and energy-efficient neuronal oscillation dynamics. Our simple neuron circuit is constructed using silicon–germanium heterojunction based bipolar transistors (HBTs) with nanowire structure. The HBT has a hysteresis window with steep switching characteristics and high current margin in the low voltage range, which enables a high spiking frequency (~ 245 kHz) with low energy consumption (≤ 1.37 pJ/spike). Also, gated structure achieves a stable balance in the activity of the neural system by incorporating both excitatory and inhibitory signal. Furthermore, inhibition of multiple strengths can be realized by adjusting the integration time according to the amplitude of the inhibitory signal. In addition, the spiking frequency can be tuned by mutually controlling the hysteresis window in the HBTs. These results ensure the sparse activity and homeostasis of neural networks.