Neural Circuits Research Articles

Anti-artifacts techniques for neural recording front-ends in closed-loop brain-machine interface ICs.

In recent years, thanks to the development of integrated circuits, clinical medicine has witnessed significant advancements, enabling more efficient and intelligent treatment approaches. Particularly in the field of neuromedical, the utilization of brain-machine interfaces (BMI) has revolutionized the treatment of neurological diseases such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. The BMI acquires neural signals via recording circuits and analyze them to regulate neural stimulator circuits for effective neurological treatment. However, traditional BMI designs, which are often isolated, have given way to closed-loop brain-machine interfaces (CL-BMI) as a contemporary development trend. CL-BMI offers increased integration and accelerated response speed, marking a significant leap forward in neuromedicine. Nonetheless, this advancement comes with its challenges, notably the stimulation artifacts (SA) problem inherent to the structural characteristics of CL-BMI, which poses significant challenges on the neural recording front-ends (NRFE) site. This paper aims to provide a comprehensive overview of technologies addressing artifacts in the NRFE site within CL-BMI. Topics covered will include: (1) understanding and assessing artifacts; (2) exploring the impact of artifacts on traditional neural recording front-ends; (3) reviewing recent technological advancements aimed at addressing artifact-related issues; (4) summarizing and classifying the aforementioned technologies, along with an analysis of future trends.

Exercise Influences the Brain's Metabolic Response to Chronic Cocaine Exposure in Male Rats.

Cocaine use is associated with negative health outcomes: cocaine use disorders, speedballing, and overdose deaths. Currently, treatments for cocaine use disorders and overdose are non-existent when compared to opioid use disorders, and current standard cocaine use disorder treatments have high dropout and recidivism rates. Physical exercise has been shown to attenuate addiction behavior as well as modulate brain activity. This study examined the differential effects of chronic cocaine use between exercised and sedentary rats. The effects of exercise on brain glucose metabolism (BGluM) following chronic cocaine exposure were assessed using Positron Emission Tomography (PET) and [18F]-Fluorodeoxyglucose (FDG). Compared to sedentary animals, exercise decreased metabolism in the SIBF primary somatosensory cortex. Activation occurred in the amygdalopiriform and piriform cortex, trigeminothalamic tract, rhinal and perirhinal cortex, and visual cortex. BGluM changes may help ameliorate various aspects of cocaine abuse and reinstatement. Further investigation is needed into the underlying neuronal circuits involved in BGluM changes and their association with addiction behaviors.

Multiple scroll attractors and multistability in the collective dynamics of a four chain coupled hopfield inertial neuron network: analysis and circuit design investigations

This work deals with the chain bidirectional coupling of four inertial Hopfield neurons. Each of our cells taken alone is modeled by a second order differential equation having three resting points. Coupling these neurons helps increasing the number of fixed points that is related to the amount of memory assigned to the network. The system having a hyperbolic tangent as nonlinearity is investigated with the help of suitable nonlinear dynamical tools like bifurcation diagrams, Lyapunov exponent diagrams and phase portraits to resort the richness of the model. As the coupling adds the amount of equilibriums, it also helps in generating multiple scroll attractors. The system that was unable of oscillation presents firing patterns such as parallel branches, coexistence of up to sixteen attractors in the phase plane, extreme events and Hopf bifurcation to name a few. All these features are discovered when observing the coupling strengths, the dissipation coefficient and when programming initial states around sensitive equilibriums. The electronic version of the four-chain coupled inertial neurons system is provided and simulated on Pspice with the aim to confirm the results obtained in the numerical scheme.

Effects of spike-time-dependent plasticity on stochastic resonance in excitatory-inhibitory neuronal networks

Effects of spike-time-dependent plasticity on stochastic resonance in excitatory-inhibitory neuronal networks

Automated Crystallization Monitoring in Material Development using Computer Vision and Neuronal Networks

AbstractCrystallization is critical in producing various substances in the food and pharmaceutical industries. The process begins with nucleation from a supersaturated solution, followed by the directed movement of solute molecules toward the newly formed nuclei. This movement is influenced by different factors such as molecular attraction and thermodynamic properties, which influence the resulting crystal size and shape. This paper presents an automated monitoring approach for the growth process of crystallization from saturated solutions. To monitor such a process, a combination of artificial intelligence and computer vision is used. The approach was developed for application in material research to automate the processing of numerous samples, which would otherwise require laboratory personnel.

Hypergraph-Based Numerical Spiking Neural Membrane Systems with Novel Repartition Protocols.

The classic spiking neural P (SN P) systems abstract the real biological neural network into a simple structure based on graphs, where neurons can only communicate on the plane. This study proposes the hypergraph-based numerical spiking neural membrane (HNSNM) systems with novel repartition protocols. Through the introduction of hypergraphs, the HNSNM systems can characterize the high-order relationships among neurons and extend the traditional neuron structure to high-dimensional nonlinear spaces. The HNSNM systems also abstract two biological mechanisms of synapse creation and pruning, and use plasticity rules with repartition protocols to achieve planar, hierarchical and spatial communications among neurons in hypergraph neuron structures. Through imitating register machines, the Turing universality of the HNSNM systems is proved by using them as number generating and accepting devices. A universal HNSNM system consisting of 41 neurons is constructed to compute arbitrary functions. By solving NP-complete problems using the subset sum problem as an example, the computational efficiency and effectiveness of HNSNM systems are verified.

Multifunctional Hierarchical Nanoplatform with Anisotropic Bimodal Mesopores for Effective Neural Circuit Reconstruction after Spinal Cord Injury.

A persistent inflammatory response, intrinsic limitations in axonal regenerative capacity, and widespread presence of extrinsic axonal inhibitors impede the restoration of motor function after a spinal cord injury (SCI). A versatile treatment platform is urgently needed to address diverse clinical manifestations of SCI. Herein, we present a multifunctional nanoplatform with anisotropic bimodal mesopores for effective neural circuit reconstruction after SCI. The hierarchical nanoplatform features of a Janus structure consist of dual compartments of hydrophilic mesoporous silica (mSiO2) and hydrophobic periodic mesoporous organosilica (PMO), each possessing distinct pore sizes of 12 and 3 nm, respectively. Unlike traditional hierarchical mesoporous nanomaterials with dual-mesopores interlaced with each other, the two sets of mesopores in this Janus nanoplatform are spatially independent and possess completely distinct chemical properties. The Janus mesopores facilitate controllable codelivery of dual drugs with distinct properties: the hydrophilic macromolecular enoxaparin (ENO) and the hydrophobic small molecular paclitaxel (PTX). Anchoring with CeO2, the resulting mSiO2&PMO-CeO2-PTX&ENO nanoformulation not only effectively alleviates ROS-induced neuronal apoptosis but also enhances microtubule stability to promote intrinsic axonal regeneration and facilitates axonal extension by diminishing the inhibitory effect of extracellular chondroitin sulfate proteoglycans. We believe that this functional dual-mesoporous nanoplatform holds significant potential for combination therapy in treating severe multifaceted diseases.

Distributed and dynamical communication: a mechanism for flexible cortico-cortical interactions and its functional roles in visual attention

Perceptual and cognitive processing relies on flexible communication among cortical areas; however, the underlying neural mechanism remains unclear. Here we report a mechanism based on the realistic spatiotemporal dynamics of propagating wave patterns in neural population activity. Using a biophysically plausible, multiarea spiking neural circuit model, we demonstrate that these wave patterns, characterized by their rich and complex dynamics, can account for a wide variety of empirically observed neural processes. The coordinated interactions of these wave patterns give rise to distributed and dynamic communication (DDC) that enables flexible and rapid routing of neural activity across cortical areas. We elucidate how DDC unifies the previously proposed oscillation synchronization-based and subspace-based views of interareal communication, offering experimentally testable predictions that we validate through the analysis of Allen Institute Neuropixels data. Furthermore, we demonstrate that DDC can be effectively modulated during attention tasks through the interplay of neuromodulators and cortical feedback loops. This modulation process explains many neural effects of attention, underscoring the fundamental functional role of DDC in cognition.

Neuronal Circuit Dysfunction in Amyotrophic Lateral Sclerosis.

The primary neural circuit affected in Amyotrophic Lateral Sclerosis (ALS) patients is the corticospinal motor circuit, originating in upper motor neurons (UMNs) in the cerebral motor cortex which descend to synapse with the lower motor neurons (LMNs) in the spinal cord to ultimately innervate the skeletal muscle. Perturbation of these neural circuits and consequent loss of both UMNs and LMNs, leading to muscle wastage and impaired movement, is the key pathophysiology observed. Despite decades of research, we are still lacking in ALS disease-modifying treatments. In this review, we document the current research from patient studies, rodent models, and human stem cell models in understanding the mechanisms of corticomotor circuit dysfunction and its implication in ALS. We summarize the current knowledge about cortical UMN dysfunction and degeneration, altered excitability in LMNs, neuromuscular junction degeneration, and the non-cell autonomous role of glial cells in motor circuit dysfunction in relation to ALS. We further highlight the advances in human stem cell technology to model the complex neural circuitry and how these can aid in future studies to better understand the mechanisms of neural circuit dysfunction underpinning ALS.

Spiral wave dynamics in a neuronal network model

Spiral waves are spatial-temporal patterns that can emerge in different systems as heart tissues, chemical oscillators, ecological networks and the brain. These waves have been identified in the neocortex of turtles, rats, and humans, particularly during sleep-like states. Although their functions in cognitive activities remain until now poorly understood, these patterns are related to cortical activity modulation and contribute to cortical processing. In this work, ,we construct a neuronal network layer based on the spatial distribution of pyramidal neurons. Our main goal is to investigate how local connectivity and coupling strength are associated with the emergence of spiral waves. Therefore, we propose a trustworthy method capable of detecting different wave patterns, based on local and global phase order parameters. As a result, we find that the range of connection radius (R) plays a crucial role in the appearance of spiral waves. For R < 20 µm, only asynchronous activity is observed due to small number of connections. The coupling strength ( gsyn ) greatly influences the pattern transitions for higher R, where spikes and bursts firing patterns can be observed in spiral and non-spiral waves. Finally, we show that for some values of R and gsyn bistable states of wave patterns are obtained.

Impaired GABAergic regulation and developmental immaturity in interneurons derived from the medial ganglionic eminence in the tuberous sclerosis complex

GABAergic interneurons play a critical role in maintaining neural circuit balance, excitation–inhibition regulation, and cognitive function modulation. In tuberous sclerosis complex (TSC), GABAergic neuron dysfunction contributes to disrupted network activity and associated neurological symptoms, assumingly in a cell type-specific manner. This GABAergic centric study focuses on identifying specific interneuron subpopulations within TSC, emphasizing the unique characteristics of medial ganglionic eminence (MGE)- and caudal ganglionic eminence (CGE)-derived interneurons. Using single-nuclei RNA sequencing in TSC patient material, we identify somatostatin-expressing (SST+) interneurons as a unique and immature subpopulation in TSC. The disrupted maturation of SST+ interneurons may undergo an incomplete switch from excitatory to inhibitory GABAergic signaling during development, resulting in reduced inhibitory properties. Notably, this study reveals markers of immaturity specifically in SST+ interneurons, including an abnormal NKCC1/KCC2 ratio, indicating an imbalance in chloride homeostasis crucial for the postsynaptic consequences of GABAergic signaling as well as the downregulation of GABAA receptor subunits, GABRA1, and upregulation of GABRA2. Further exploration of SST+ interneurons revealed altered localization patterns of SST+ interneurons in TSC brain tissue, concentrated in deeper cortical layers, possibly linked to cortical dyslamination. In the epilepsy context, our research underscores the diverse cell type-specific roles of GABAergic interneurons in shaping seizures, advocating for precise therapeutic considerations. Moreover, this study illuminates the potential contribution of SST+ interneurons to TSC pathophysiology, offering insights for targeted therapeutic interventions.

The actin-binding protein CAP1 represses MRTF-SRF-dependent gene expression in mouse cerebral cortex.

Serum response factor (SRF) is an essential transcription factor for brain development and function. Here, we explored how an SRF cofactor, the actin monomer-sensing myocardin-related transcription factor MRTF, is regulated in mouse cortical neurons. We found that MRTF-dependent SRF activity in vitro and in vivo was repressed by cyclase-associated protein CAP1. Inactivation of the actin-binding protein CAP1 reduced the amount of actin monomers in the cytoplasm, which promoted nuclear MRTF translocation and MRTF-SRF activation. This function was independent of cofilin1 and actin-depolymerizing factor, and CAP1 loss of function in cortical neurons was not compensated by endogenous CAP2. Transcriptomic and proteomic analyses of cerebral cortex lysates from wild-type and Cap1 knockout mice supported the role of CAP1 in repressing MRTF-SRF-dependent signaling in vivo. Bioinformatic analysis identified likely MRTF-SRF target genes, which aligned with the transcriptomic and proteomic results. Together with our previous studies that implicated CAP1 in axonal growth cone function as well as the morphology and plasticity of excitatory synapses, our findings establish CAP1 as a crucial actin regulator in the brain relevant for formation of neuronal networks.

Differential disruptions in population coding along the dorsal-ventral axis of CA1 in the APP/PS1 mouse model of Aβ pathology.

Alzheimer's Disease (AD) is characterized by a range of behavioral alterations, including memory loss and psychiatric symptoms. While there is evidence that molecular pathologies, such as amyloid beta (Aβ), contribute to AD, it remains unclear how this histopathology gives rise to such disparate behavioral deficits. One hypothesis is that Aβ exerts differential effects on neuronal circuits across brain regions, depending on the neurophysiology and connectivity of different areas. To test this, we recorded from large neuronal populations in dorsal CA1 (dCA1) and ventral CA1 (vCA1), two hippocampal areas known to be structurally and functionally diverse, in the APP/PS1 mouse model of amyloidosis. Despite similar levels of Aβ pathology, dCA1 and vCA1 showed distinct disruptions in neuronal population activity as animals navigated a virtual reality environment. In dCA1, pairwise correlations and entropy, a measure of the diversity of activity patterns, were decreased in APP/PS1 mice relative to age-matched C57BL/6 controls. However, in vCA1, APP/PS1 mice had increased pair-wise correlations and entropy as compared to age matched controls. Finally, using maximum entropy models, we connected the microscopic features of population activity (correlations) to the macroscopic features of the population code (entropy). We found that the models' performance increased in predicting dCA1 activity, but decreased in predicting vCA1 activity, in APP/PS1 mice relative to the controls. Taken together, we found that Aβ exerts distinct effects across different hippocampal regions, suggesting that the various behavioral deficits of AD may reflect underlying heterogeneities in neuronal circuits and the different disruptions that Aβ pathology causes in those circuits.

A general neural membrane computing model

Neural P systems are a class of distributed parallel computing models inspired by the way neurons cooperate in biological neural networks. Artificial neural networks integrate biological neural networks with mathematical models, which have powerful information processing capabilities. This work adopts the structure and data processing method of neural networks and integrates them with membrane computing to improve the neural P systems in data processing. A general neural membrane computing (GNMC) model is developed to realize the flow and manipulation of data in the form of objects and rules in the neuron. Glial cells are introduced into the GNMC model at the cellular level to modulate the states of the connected neurons. The trigger conditions, the activation mechanisms and the dissolve-reconnect rules make the GNMC model more efficient in handling objects. The multiple objects and multiple topological structures enable the GNMC model to handle different practical problems. The Turing universality of the GNMC model is proved as number generating and accepting devices. Furthermore, a small universal GNMC model with 71 neurons, fewer than that of the existing state-of-the-art neural P systems, is constructed for computing functions.

Polysaccharide extracted from Sarcandra glabra residue attenuate cognitive impairment by regulating gut microbiota in diabetic mice

Diabetic encephalopathy (DE), characterized by cognitive impairment, currently lacks targeted treatment. Previous studies have shown that Sarcandra glabra extracted residue polysaccharide (SERP) exhibited hypoglycemic effects either in vitro or in streptozotocin-induced diabetes mice. However, the therapeutic effect of SERP on DE was not elucidated. This study investigated the therapeutic effect of SERP on DE and its underlying mechanism. Our results revealed that SERP regulates glucose and lipid metabolism, improves cognitive function, and exhibits diminished activity post-antibiotic intervention. Importantly, we discovered a novel mechanism by which SERP modulates the gut microbiota, specifically enriching Bacteroidales S24-7, resulting in elevated levels of butyric acid in the intestine. This regulation modulates the intestinal endocrine cell lipid metabolism level, restores damaged intestinal barriers and neural epithelial circuits, thus exhibiting cure effects. Our findings suggest that SERP could become a candidate for treating DE, potentially involving the regulation mechanism of the “microbiota-gut-brain axis”. This study underscores the unique therapeutic efficacy of SERP in managing DE, offering fresh drug candidates and innovative treatment strategies for this challenging condition.

Contrasting neurofunctional correlates of face- and visuospatial-processing in children and adolescents with Williams syndrome: convergent results from four fMRI paradigms

Understanding neurogenetic mechanisms underlying neuropsychiatric disorders such as schizophrenia and autism is complicated by their inherent clinical and genetic heterogeneity. Williams syndrome (WS), a rare neurodevelopmental condition in which both the genetic alteration (hemideletion of ~ twenty-six 7q11.23 genes) and the cognitive/behavioral profile are well-defined, offers an invaluable opportunity to delineate gene-brain-behavior relationships. People with WS are characterized by increased social drive, including particular interest in faces, together with hallmark difficulty in visuospatial processing. Prior work, primarily in adults with WS, has searched for neural correlates of these characteristics, with reports of altered fusiform gyrus function while viewing socioemotional stimuli such as faces, along with hypoactivation of the intraparietal sulcus during visuospatial processing. Here, we investigated neural function in children and adolescents with WS by using four separate fMRI paradigms, two that probe each of these two cognitive/behavioral domains. During the two visuospatial tasks, but not during the two face processing tasks, we found bilateral intraparietal sulcus hypoactivation in WS. In contrast, during both face processing tasks, but not during the visuospatial tasks, we found fusiform hyperactivation. These data not only demonstrate that previous findings in adults with WS are also present in childhood and adolescence, but also provide a clear example that genetic mechanisms can bias neural circuit function, thereby affecting behavioral traits.

A Loss Landscape Perspective and Simulations for Imaging Inverse Problems Based on AI and Neuron Network Training Method

A Loss Landscape Perspective and Simulations for Imaging Inverse Problems Based on AI and Neuron Network Training Method

Classification of 2-node excitatory–inhibitory networks

We classify connected 2-node excitatory–inhibitory networks under various conditions. We assume that, as well as for connections, there are two distinct node-types, excitatory and inhibitory. In our classification we consider four different types of excitatory–inhibitory networks: restricted, partially restricted, unrestricted and completely unrestricted. For each type we give two different classifications. Using results on ODE-equivalence and minimality, we classify the ODE-classes and present a minimal representative for each ODE-class. We also classify all the networks with valence ≤2. These classifications are up to renumbering of nodes and the interchange of ‘excitatory’ and ‘inhibitory’ on nodes and arrows. These classifications constitute a first step towards analysing dynamics and bifurcations of excitatory–inhibitory networks. The results have potential applications to biological network models, especially neuronal networks, gene regulatory networks, and synthetic gene networks.

Neurophotonics: a comprehensive review, current challenges and future trends.

The human brain, with its vast network of billions of neurons and trillions of synapses (connections) between diverse cell types, remains one of the greatest mysteries in science and medicine. Despite extensive research, an understanding of the underlying mechanisms that drive normal behaviors and response to disease states is still limited. Advancement in the Neuroscience field and development of therapeutics for related pathologies requires innovative technologies that can provide a dynamic and systematic understanding of the interactions between neurons and neural circuits. In this work, we provide an up-to-date overview of the evolution of neurophotonic approaches in the last 10 years through a multi-source, literature analysis. From an initial corpus of 243 papers retrieved from Scopus, PubMed and WoS databases, we have followed the PRISMA approach to select 56 papers in the area. Following a full-text evaluation of these 56 scientific articles, six main areas of applied research were identified and discussed: (1) Advanced optogenetics, (2) Multimodal neural interfaces, (3) Innovative therapeutics, (4) Imaging devices and probes, (5) Remote operations, and (6) Microfluidic platforms. For each area, the main technologies selected are discussed according to the photonic principles applied, the neuroscience application evaluated and the more indicative results of efficiency and scientific potential. This detailed analysis is followed by an outlook of the main challenges tackled over the last 10 years in the Neurophotonics field, as well as the main technological advances regarding specificity, light delivery, multimodality, imaging, materials and system designs. We conclude with a discussion of considerable challenges for future innovation and translation in Neurophotonics, from light delivery within the brain to physical constraints and data management strategies.

Muscarinic Modulation of Synaptic Transmission and Short-Term Plasticity in the Dorsal and Ventral Hippocampus

Muscarinic neurotransmission is fundamentally involved in supporting several brain functions by modulating flow of information in brain neural circuits including the hippocampus which displays a remarkable functional segregation along its longitudinal axis. However, how muscarinic neuromodulation contributes to the functional segregation along the hippocampus remains unclear. In this study we show that the nonselective muscarinic receptor agonist carbachol similarly suppresses basal synaptic transmission in the dorsal and ventral CA1 hippocampal field, in a concentration-depended manner. Furthermore, using a ten-pulse stimulation train of varying frequency we found that carbachol changes the frequency filtering properties more in ventral than dorsal hippocampus by facilitating synaptic inputs at a wide range of input frequencies in the ventral compared with dorsal hippocampus. Using the M2 receptor antagonist gallamine and the M4 receptor antagonist tropicamide, we found that M2 receptors are involved in controlling basal synaptic transmission and short-term synaptic plasticity (STSP) in the ventral but not the dorsal hippocampus, while M4 receptors participate in modulating basal synaptic transmission and STSP in both segments of the hippocampus. These results were corroborated by the higher protein expression levels of M2 receptors in the ventral compared with dorsal hippocampus. We conclude that muscarinic transmission modulates excitatory synaptic transmission and short-term synaptic plasticity along the entire rat hippocampus by acting through M4 receptors and recruiting M2 receptors only in the ventral hippocampus. Furthermore, M4 receptors appear to exert a permissive role on the actions of M2 receptors on STSP in the ventral hippocampus. This dorsoventral differentiation of muscarinic modulation is expected to have important implications in information processing along the endogenous hippocampal circuitry.