Neural Circuits Research Articles

Biological computations: Limitations of attractor-based formalisms and the need for transients

Living systems, from single cells to higher vertebrates, receive a continuous stream of non-stationary inputs that they sense, for e.g. via cell surface receptors or sensory organs. By integrating these time-varying, multi-sensory, and often noisy information with memory using complex molecular or neuronal networks, they generate a variety of responses beyond simple stimulus-response association, including avoidance behavior, life-long-learning or social interactions. In a broad sense, these processes can be understood as a type of biological computation. Taking as a basis generic features of biological computations, such as real-time responsiveness or robustness and flexibility of the computation, we highlight the limitations of the current attractor-based framework for understanding computations in biological systems. We argue that frameworks based on transient dynamics away from attractors are better suited for the description of computations performed by neuronal and signaling networks. In particular, we discuss how quasi-stable transient dynamics from ghost states that emerge at criticality have a promising potential for developing an integrated framework of computations, that can help us understand how living system actively process information and learn from their continuously changing environment.

Ventral subiculum promotes wakefulness through several pathways in male mice.

The ventral subiculum (vSUB), the major output structure of the hippocampal formation, regulates motivation, stress integration, and anxiety-like behaviors that rely on heightened arousal. However, the roles and underlying neural circuits of the vSUB in wakefulness are poorly known. Using in vivo fiber photometry and multichannel electrophysiological recordings in mice, we found that the vSUB glutamatergicneurons exhibited highactivities during wakefulness. Moreover, activation of vSUB glutamatergic neurons caused an increase in wakefulness and anxiety-like behaviors and induced a rapid transition from sleep to wakefulness. In addition, optogenetic stimulation of vSUB glutamatergic terminals and retrograde-targeted chemogenetic activation of vSUB glutamatergic neurons revealed that vSUB promoted arousal by innervating the lateral hypothalamus (LH), nucleus accumbens (NAc) shell, and prefrontal cortex (PFC). Nevertheless, local microinjection of dopamine D1 or D2/D3 receptor antagonist blocked thewake-promoting effect induced by chemogenetic activation of vSUB pathways. Finally, chemogenetic inhibition of vSUB glutamatergic neurons decreased arousal. Altogether, our findings reveal a prominent contribution of vSUB glutamatergic neurons to the control of wakefulness through several pathways.

At the Basis of Brain Malformations: Brain Plasticity, Developmental Neurobiology, and Considerations for Rehabilitation

AbstractFrom early age in the human brain occurs plasticity process that influences its development. The functioning of the brain is governed by its neuronal connectivity and the synaptic dynamics of these connections. A neuron, over thousands of synapses, can receive a large number of inputs and produce different outputs leading to the consolidation and integration of memory. Synaptic plasticity is the set of experience-dependent changes in neuronal pathways that support acquired habits. It is the ability of the nervous system to reshape connectivity between neurons, changing the functional and structural organization of neuronal circuits that allows us to adapt to the multiple and continuous changes in the environment and leading to processes such as cognitive development and the ability to learn. Synaptic plasticity is mainly due to short- and long-term mechanisms. Short-term synaptic plasticity refers to changes in synaptic strength that occurs very quickly (from one-thousandth of a second to 5 minutes) and are temporary and decay over minutes (maximum 30 minutes). Long-term synaptic plasticity is defined by a long-lasting, activity-dependent change in synaptic efficacy, last from hours up to a lifetime (from 30 minutes to weeks, months, and years) and is thought to constitute the basis of learning and memory. A significant difference occurs in the nature of the change; short-term plasticity adds only a functional change, whereas long-term plasticity causes not only functional but also structural changes. Aside from genetic factors and metabolic processes, brain development is mediated also by environmental factors. Interaction with the environment plays a key role in the development and growth of neural networks and neuroplasticity. Environmental interactions that can modify and increase the development of neural networks and intelligence in children are several and are herein discussed.

Insight into the pathophysiological advances and molecular mechanisms underlying cerebral stroke: current status.

Molecular pathways involved in cerebral stroke are diverse. The major pathophysiological events that are observed in stroke comprises of excitotoxicity, oxidative stress, mitochondrial damage, endoplasmic reticulum stress, cellular acidosis, blood-brain barrier disruption, neuronal swelling and neuronal network mutilation. Various biomolecules are involved in these pathways and several major proteins are upregulated and/or suppressed following stroke. Different types of receptors, ion channels and transporters are activated. Fluctuations in levels of various ions and neurotransmitters have been observed. Cells involved in immune responses and various mediators involved in neuro-inflammation get upregulated progressing the pathogenesis of the disease. Despite of enormity of the problem, there is not a single therapy that can limit infarction and neurological disability due to stroke. This is because of poor understanding of the complex interplay between these pathophysiological processes. This review focuses upon the past to present research on pathophysiological events that are involved in stroke and various factors that are leading to neuronal death following cerebral stroke. This will pave a way to researchers for developing new potent therapeutics that can aid in the treatment of cerebral stroke.

The Determining Role of Covariances in Large Networks of Stochastic Neurons.

Biological neural networks are notoriously hard to model due to their stochastic behavior and high dimensionality. We tackle this problem by constructing a dynamical model of both the expectations and covariances of the fractions of active and refractory neurons in the network's populations. We do so by describing the evolution of the states of individual neurons with a continuous-time Markov chain, from which we formally derive a low-dimensional dynamical system. This is done by solving a moment closure problem in a way that is compatible with the nonlinearity and boundedness of the activation function. Our dynamical system captures the behavior of the high-dimensional stochastic model even in cases where the mean-field approximation fails to do so. Taking into account the second-order moments modifies the solutions that would be obtained with the mean-field approximation and can lead to the appearance or disappearance of fixed points and limit cycles. We moreover perform numerical experiments where the mean-field approximation leads to periodically oscillating solutions, while the solutions of the second-order model can be interpreted as an average taken over many realizations of the stochastic model. Altogether, our results highlight the importance of including higher moments when studying stochastic networks and deepen our understanding of correlated neuronal activity.

Distinct roles of excitatory and inhibitory neurons in the medial prefrontal cortex in the expression and reconsolidation of methamphetamine-associated memory in male mice.

Methamphetamine, a commonly abused drug, is known for its high relapse rate. The persistence of addictive memories associated with methamphetamine poses a significant challenge in preventing relapse. Memory retrieval and subsequent reconsolidation provide an opportunity to disrupt addictive memories. However, the key node in the brain network involved in methamphetamine-associated memory retrieval has not been clearly defined. In this study, using the conditioned place preference in male mice, whole brain c-FOS mapping and functional connectivity analysis, together with chemogenetic manipulations of neural circuits, we identified the medial prefrontal cortex (mPFC) as a critical hub that integrates inputs from the retrosplenial cortex and the ventral tegmental area to support both the expression and reconsolidation of methamphetamine-associated memory during its retrieval. Surprisingly, with further cell-type specific analysis and manipulation, we also observed that methamphetamine-associated memory retrieval activated inhibitory neurons in the mPFC to facilitate memory reconsolidation, while suppressing excitatory neurons to aid memory expression. These findings provide novel insights into the neural circuits and cellular mechanisms involved in the retrieval process of addictive memories. They suggest that targeting the balance between excitation and inhibition in the mPFC during memory retrieval could be a promising treatment strategy to prevent relapse in methamphetamine addiction.

The role of glial and neuronal Eph/ephrin signaling in Drosophila mushroom body development and sleep and circadian behavior

The Eph receptor, a prototypically large receptor protein tyrosine kinase, interacts with ephrin ligands, forming a bidirectional signaling system that impacts diverse brain functions. Eph receptors and ephrins mediate forward and reverse signaling, affecting neurogenesis, axon guidance, and synaptic signaling. While mammalian studies have emphasized their roles in neurogenesis and synaptic plasticity, the Drosophila counterparts are less studied, especially in glial cells, despite structural similarities. Using RNAi to modulate Eph/ephrin expression in Drosophila neurons and glia, we studied their roles in brain development and sleep and circadian behavior. Knockdown of neuronal ephrin disrupted mushroom body development, while glial knockdown had minimal impact. Surprisingly, disrupting ephrin in neurons or glial cells altered sleep and circadian rhythms, indicating a direct involvement in these behaviors independent from developmental effects. Further analysis revealed distinct sleep phenotypes between neuronal and glial knockdowns, underscoring the intricate interplay within the neural circuits that govern behavior. Glia-specific knockdowns showed altered sleep patterns and reduced circadian rhythmicity, suggesting an intricate role of glia in sleep regulation. Our findings challenge simplistic models of Eph/ephrin signaling limited to neuron-glia communication and emphasize the complexity of the regulatory networks modulating behavior. Future investigations targeting specific glial subtypes will enhance our understanding of Eph/ephrin signaling's role in sleep regulation across species.

Inhibitory Subpopulations in preBötzinger Complex Play Distinct Roles in Modulating Inspiratory Rhythm and Pattern.

Inhibitory neurons embedded within mammalian neural circuits shape breathing, walking, and other rhythmic motor behaviors. At the core of the neural circuit controlling breathing is the preBötzinger Complex (preBötC), where GABAergic (GAD1/2+) and glycinergic (GlyT2+) neurons are functionally and anatomically intercalated among glutamatergic Dbx1-derived (Dbx1+) neurons that generate rhythmic inspiratory drive. The roles of these preBötC inhibitory neurons in breathing remain unclear. We first characterized the spatial distribution of molecularly defined preBötC inhibitory subpopulations in male and female neonatal double reporter mice expressing either tdTomato or EGFP in GlyT2+, GAD1+, or GAD2+ neurons. We found that the majority of preBötC inhibitory neurons expressed both GlyT2 and GAD2 while a much smaller subpopulation also expressed GAD1. To determine the functional role of these subpopulations, we used holographic photostimulation, a patterned illumination technique, in rhythmically active medullary slices from neonatal Dbx1tdTomato;GlyT2EGFP and Dbx1tdTomato;GAD1EGFP double reporter mice of either sex. Stimulation of 4 or 8 preBötC GlyT2+ neurons during endogenous rhythm prolonged the interburst interval in a phase-dependent manner and increased the latency to burst initiation when bursts were evoked by stimulation of Dbx1+ neurons. In contrast, stimulation of 4 or 8 preBötC GAD1+ neurons did not affect interburst interval or latency to burst initiation. Instead, photoactivation of GAD1+ neurons during the inspiratory burst prolonged endogenous and evoked burst duration and decreased evoked burst amplitude. We conclude that GlyT2+/GAD2+ neurons modulate breathing rhythm by delaying burst initiation while a smaller GAD1+ subpopulation shapes inspiratory patterning by altering burst duration and amplitude.

Experimental Demonstration of Reservoir Computing with Self-Assembled Percolating Networks of Nanoparticles.

The complex self-assembled network of neurons and synapses that comprises the biological brain enables natural information processing with remarkable efficiency. Percolating networks of nanoparticles (PNNs) are complex self-assembled nanoscale systems that have been shown to possess many promising brain-like attributes and which are therefore appealing systems for neuromorphic computation. Here experiments are performed that show that PNNs can be utilized as physical reservoirs within a nanoelectronic reservoir computing framework and demonstrate successful computation for several benchmark tasks (chaotic time series prediction, nonlinear transformation, and memory capacity). For each task, relevant literature results are compiled and it is shown that the performance of the PNNs compares favorably to that previously reported from nanoelectronic reservoirs. It is then demonstrated experimentally that PNNs can be used for spoken digit recognition with state-of-the-art accuracy. Finally, a parallel reservoir architecture is emulated, which increases the dimensionality and richness of the reservoir outputs and results in further improvements in performance across all tasks.

Critical appraisal of how COVID-19 infection and imposed lockdowns have impacted gastroesophageal reflux: A review.

Previous literature has demonstrated that COronaVIrus Disease of 2019 (COVID-19) impacts an individual gastrointestinal tract (GIT), causing symptoms like nausea, diarrhea, and loss of appetite. Severe acute respiratory syndrome coronavirus RNA has been discovered in the stool of infected individuals in earlier research. It was discovered that severe acute respiratory syndrome coronavirus was significantly expressed in the GIT, indicating that the virus can also infect the digestive system. Angiotensin-converting enzyme 2 functions as the viral receptor. The chronic illness known as gastroesophageal reflux disease (GERD) is typified by frequent reflux of stomach acid into the esophagus. By triggering the sensitized esophageal-bronchial neuronal circuit or aspirating into the airways (microaspiration), GER exacerbates respiratory diseases. Aspiration is a well-known risk to be considered when treating patients in intensive care units. Strong genetic correlations have been identified between COVID-19 infection and GERD susceptibility, suggesting a shared genetic basis for both conditions. Nonetheless, even though GERD, extraesophageal reflex, and COVID-19 have a number of significant risk factors and exhibit similar symptoms, the relationship between these illnesses has not yet been examined in depth. This review is the first of its kind to critically examine the association between the COVID-19 epidemic and GER and its associated diseases. The key objective of this work is to promote the creation of prevention plans, treatment plans, and guidelines while also enhancing and optimizing our understanding of the relationship between COVID-19 and GERs.

Coupling Relationships between the Brain and the Central Pattern Generator Based on a Fractional-Order Extended Hindmarsh-Rose Model.

The states of the central nervous system (CNS) can be classified into subcritical, critical, and supercritical states that endow the system with information capacity, transmission capabilities, and dynamic range. A further investigation of the relationship between the CNS and the central pattern generators (CPG) is warranted to provide insight into the mechanisms that govern the locomotion system. In this study, we established a fractional-order CPG model based on an extended Hindmarsh-Rose model with time delay. A CNS model was further established using a recurrent excitation-inhibition neuronal network. Coupling between these CNS and CPG models was then explored, demonstrating a potential means by which oscillations generated by a neural network respond to periodic stimuli. These simulations yielded two key sets of findings. First, frequency sliding was observed when the CPG was sent to the CNS in the subcritical, critical, and supercritical states with different external stimulus and fractional-order index values, indicating that frequency sliding regulates brain function on multiple spatiotemporal scales when the CPG and CNS are coupled together. The main frequency range for these simulations was observed in the gamma band. Second, with increasing external inputs the coherence index for the CNS decreases, demonstrating that strong external inputs introduce neuronal stochasticity. Neural network synchronization is then reduced, triggering irregular neuronal firing. Together these results provide novel insight into the potential mechanisms that may underlie the locomotion system.

The role and mechanism of 5-HTDRN-BNST neural circuit in anxiety and fear lesions.

Central 5-hydroxytryptaminergic dorsal raphe nucleus (5-HTDRN)-bed nucleus of stria terminalis (BNST) neural circuit dysfunction is one of the important neurobiological basis of anxiety and fear disorders. Under stress, 5-hydroxytryptamine (5-HT) neurons act on BNST receptors to attenuate anxiety and fear responses or enhance anxiety and fear. In BNST, corticotropin releasing factor neurons play a role in regulating emotions by reversely regulating excitatory or inhibitory 5-HT neurons. The composition of 5-HTDRN-BNST neural circuit, the pathological changes of 5-HTDRN-BNST neural circuit function damage under stress, and the effects of 5-HTDRN-BNST neural circuit on anxiety disorder, panic disorder and post-traumatic stress disorder were analyzed and are summarized in this paper. The characteristics of functional changes of the neural circuit and its effects on brain functional activities provide a basis and ideas for the treatment of anxiety and fear disorders through the regulation of 5-HTDRN-BNST neural circuit, and they also provide a new perspective for understanding the pathological mechanism of such diseases.

Low power nanoscale S-FED based single ended sense amplifier applied in integrate and fire neuron circuit

Current advancements in neuromorphic computing systems are focused on decreasing power consumption and enriching computational functions. Correspondingly, state-of-the-art system-on-chip developers are encouraged to design nanoscale devices with minimum power dissipation and high-speed operation. This paper deals with designing a sense amplifier based on side-contacted field-effect diodes to reduce the power-delay product (PDP) and the noise susceptibility, as critical factors in neuron circuits. Our findings reveal that both static and dynamic power consumption of the S-FED-based sense amplifier, equal to 1.86 μW and 1.92 fW/GHz, are × 243.03 and × 332.83 lower than those of the conventional CMOS counterpart, respectively. While the sense-amplifier circuit based on CMOS technology undergoes an output voltage deviation of 170.97 mV, the proposed S-FED-based one enjoys a minor output deviation of 27.31 mV. Meanwhile, the superior HIGH-level and LOW-level noise margins of the S-FED-based sense amplifier to the CMOS counterparts (∆NMH = 70 mV and ∆NML = 120 mV), respectively, can ensure the system-level operation stability of the former one. Subsequent to the attainment of an area-efficient, low-power, and high-speed S-FED-based sense amplifier (PDP = 187.75 × 10–18 W s) as a fundamental building block, devising an innovative integrate-and-fire neuron circuit based on S-FED paves the way to realize a new generation of neuromorphic architectures. To shed light on this context, an S-FED-based integrate-and-fire neuron circuit is designed and analyzed utilizing a sense amplifier and feedback loop to enhance spiking voltage and subsequent noise immunity in addition to an about fourfold increase in firing frequency compared to CMOS-based ones.

Comparative study between two artificial intelligence techniques (NN and SVM) applied in fault diagnosis of Wind Turbine

This paper addresses the application of artificial intelligence (AI) techniques for the classification of faults in wind turbine. Wind Energy Conversion Systems have become a focal point in the research of renewable energy sources. Reliability of wind turbine is critical to extract this maximum amount of energy from the wind. Many condition monitoring techniques that are based on steady-state analysis are being applied to wind generators. Bearing faults in this generator causes mechanical vibrations and the variations in the air gap density. The air gap flux density is modulated and currents are generated at different frequencies related to the mechanical vibrations. Characteristic bearing frequencies are associated with the physical construction of bearing and its failure mode. Classical spectral analysis using the Fourier transform was used to detect different bearing failure modes. The magnitudes of the frequencies components formed by the bearing defect are small compared to the rest of the current spectrum. This large difference in magnitude makes difficult the detection and classification of bearing faults. In this paper, we show that the utilization of different approaches of artificial intelligence such as neuronal network (NN) and support vector machines (SVM) can be discriminate different failure modes and gives a good basis for an automatic and non-invasive condition monitoring for wind turbine.

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.