A quantitative description of membrane current and its application to conduction and excitation in nerve. 1952

Conditional Generative Models for Simulation of EMG During Naturalistic Movements

Numerical models of electromyography (EMG) signals have provided a huge contribution to our fundamental understanding of human neurophysiology and remain a central pillar of motor neuroscience and the development of human-machine interfaces. However, while modern biophysical simulations based on finite element methods (FEMs) are highly accurate, they are extremely computationally expensive and thus are generally limited to modeling static systems such as isometrically contracting limbs. As a solution to this problem, we propose to use a conditional generative model to mimic the output of an advanced numerical model. To this end, we present BioMime, a conditional generative neural network trained adversarially to generate motor unit (MU) activation potential waveforms under a wide variety of volume conductor parameters. We demonstrate the ability of such a model to predictively interpolate between a much smaller number of numerical model's outputs with a high accuracy. Consequently, the computational load is dramatically reduced, which allows the rapid simulation of EMG signals during truly dynamic and naturalistic movements.

Bioinspired Nanofluidic Circuits with Integrating Excitatory and Inhibitory Synapses

Brain neural networks intricately integrate excitatory and inhibitory synaptic potentials to modulate the generation or suppression of action potentials, laying the foundation for neuronal computation. Although bioinspired nanofluidic systems have replicated some synaptic functions, complete integration of postsynaptic potentials remains unachieved. In this work, the developed ion concentration gradient nanofluidic memristor (ICGNM) modulates memristive effects through ion concentration gradient adjustments and exhibits synaptic plasticity phenomena, including paired-pulse facilitation, paired-pulse depression, and spike-rate-dependent plasticity. Furthermore, by incorporation of ICGNMs as the memristive elements into the classic Hodgkin-Huxley model, the action potential generation is replicated. In addition to simulating nanofluidic spiking, these ICGNMs are also employed in a bioinspired nanofluidic circuit to simulate the integration of excitatory and inhibitory synaptic signals, which is highly analogous to the signal integration in actual neural circuits. This work represents a new step toward ionic computing in solution with bioinspired nanofluidic circuits.

Collective dynamics of pulse-coupled swarmalators

Collective motion in nature, from the murmurations of starlings to firefly flashes, showcases a remarkable level of self-organization. Swarmalator models are an emerging paradigm for describing collective behavior of oscillators which sync and swarm. Traditionally, most studies have focused on the self-organizing dynamics of swarmalators with continuous coupling. However, a key aspect of many biological systems, pulsed interactions, remains unexplored within the framework of swarmalators. In this paper, we analyze the collective behavior of pulse-coupled swarmalators with different characteristics of pulse and phase response curves. We report several collective states facilitated by the pulsatile interactions including bump state, active bump state, partial synchronized state, radial wave state, and death state. The stationary nature of spatial and phase dynamics characterizes the death state. Further, we provide an analytical estimate for the threshold coupling strength for the death state's existence by analyzing the minimal model's dynamics with two coupled swarmalators.

Situation-Based Neuromorphic Memory in Spiking Neuron-Astrocyte Network

Mammalian brains operate in very special surroundings: to survive they have to react quickly and effectively to the pool of stimuli patterns previously recognized as danger. Many learning tasks often encountered by living organisms involve a specific set-up centered around a relatively small set of patterns presented in a particular environment. For example, at a party, people recognize friends immediately, without deep analysis, just by seeing a fragment of their clothes. This set-up with reduced "ontology" is referred to as a "situation." Situations are usually local in space and time. In this work, we propose that neuron-astrocyte networks provide a network topology that is effectively adapted to accommodate situation-based memory. In order to illustrate this, we numerically simulate and analyze a well-established model of a neuron-astrocyte network, which is subjected to stimuli conforming to the situation-driven environment. Three pools of stimuli patterns are considered: external patterns, patterns from the situation associative pool regularly presented to the network and learned by the network, and patterns already learned and remembered by astrocytes. Patterns from the external world are added to and removed from the associative pool. Then, we show that astrocytes are structurally necessary for an effective function in such a learning and testing set-up. To demonstrate this we present a novel neuromorphic computational model for short-term memory implemented by a two-net spiking neural-astrocytic network. Our results show that such a system tested on synthesized data with selective astrocyte-induced modulation of neuronal activity provides an enhancement of retrieval quality in comparison to standard spiking neural networks trained via Hebbian plasticity only. We argue that the proposed set-up may offer a new way to analyze, model, and understand neuromorphic artificial intelligence systems.

Alternating chimera states and synchronization in multilayer neuronal networks with ephaptic intralayer coupling

Over the past decade, most of researches on the communication between the neurons are based on synapses. However, the changes in action potentials in neurons may produce complex electromagnetic fields in the media, which may also have an impact on the electrical activity of neurons. To explore this factor, we construct a two-layer neuronal network composed of identical Hindmarsh-Rose neurons. Each neuron is connected with its neighbors in the layer via magnetic connections and a neuron in the corresponding position of the other layer via electrical synapse. By adjusting the electrical coupling strength and magnetic coupling strength, we find the appearance of alternating chimera states and transient chimera states whenever the intralayer coupling is nonlocal and local, respectively. According to our study, these phenomena have not been studied in multilayer networks of this structure. And it is found that the transient chimera states only could occur when the number of coupled neighbors is small. In addition, the states of two independent networks will affect the final states of networks applying the same sufficiently large interlayer coupling strength. Our study reveals a possible effect of electrical coupling and ephaptic coupling produced together on the dynamic behavior of the neuronal networks. Meanwhile, our results suggest that it makes sense to take electromagnetic induction into neuronal models.

Functionalized Nanodiamonds for Targeted Neuronal Electromagnetic Signal Detection

Intracellular sensing technologies necessitate a delicate balance of spatial resolution, sensitivity, biocompatibility, and stability. While existing methods partially fulfill these criteria, none offer a comprehensive solution. Nanodiamonds (NDs) harboring nitrogen-vacancy (NV) centers have emerged as promising candidates due to their sensing capabilities under biological conditions and their ability to meet all aforementioned requirements. This study focuses on expanding the application of NDs and NV center-based sensing to neuronal contexts by investigating their functionalization and subsequent effects on three distinct cell lines relevant to neurodegenerative disease research. Our study concentrates on positioning fluorescent NDs (FNDs) with NV center point defects onto neuronal cell surfaces. Achieving this through specific antibody attachment enhances the proximity of FND to neurites, facilitating the detection of local action potentials. Targeting voltage-dependent calcium channels (Cav2.2) with biotin-streptavidin-bound antibodies enables the precise positioning of FNDs. The functionalized FNDs (f-FNDs) show increased size and zeta potential, confirming the antibody presence without compromising cell viability. Two-color confocal imaging and co-localization algorithms are employed to further attest to the success of the functionalization. The f-FNDs are applied to cell cultures of three cell lines: SH-SY5Y, differentiated dopaminergic neurons, and hippocampal rat neurons; their biocompatibility and effects on synaptic activity are explored. Moreover, preliminary total internal reflection fluorescence - optically detected magnetic resonance (TIRF-ODMR) experiments across cellular sites demonstrate the magnetic field sensitivity of our sensor network. The successful establishment of this sensor network provides a platform for characterizing neuronal signaling in healthy models and conditions mimicking Parkinson's disease.

The effects of doxapram and its potential interactions with K2P channels in experimental model preparations

The channels commonly responsible for maintaining cell resting membrane potentials are referred to as K2P (two-P-domain K+ subunit) channels. These K+ ion channels generally remain open but can be modulated by their local environment. These channels are classified based on pharmacology, pH sensitivity, mechanical stretch, and ionic permeability. Little is known about the physiological nature of these K2P channels in invertebrates. Acidic conditions depolarize neurons and muscle fibers, which may be caused by K2P channels given that one subtype can be blocked by acidic conditions. Doxapram is used clinically as a respiratory aid known to block acid-sensitive K2P channels; thus, the effects of doxapram on the muscle fibers and synaptic transmission in larval Drosophila and crawfish were monitored. A dose-dependent response was observed via depolarization of the larval Drosophila muscle and an increase in evoked synaptic transmission, but doxapram blocked the production of action potentials in the crawfish motor neuron and had a minor effect on the resting membrane potential of the crawfish muscle. This indicates that the nerve and muscle tissues in larval Drosophila and crawfish likely express different K2P channel subtypes. Since these organisms serve as physiological models for neurobiology and physiology, it would be of interest to further investigate what types of K2P channel are expressed in these tissues. (212 words).

Efficient Spiking Neural Networks with Biologically Similar Lithium-Ion Memristor Neurons

Benefiting from the brain-inspired event-driven feature and asynchronous sparse coding approach, spiking neural networks (SNNs) are becoming a potentially energy-efficient replacement for conventional artificial neural networks. However, neuromorphic devices used to construct SNNs persistently result in considerable energy consumption owing to the absence of sufficient biological parallels. Drawing inspiration from the transport nature of Na+ and K+ in synapses, here, a Li-based memristor (LixAlOy) was proposed to emulate the biological synapse, leveraging the similarity of Li as a homologous main group element to Na and K. The Li-based memristor exhibits ∼8 ns ultrafast operating speed, 1.91 and 0.72 linearity conductance modulation, and reproducible switching behavior, enabled by lithium vacancies forming a conductive filament mechanism. Moreover, these memristors are capable of simulating fundamental behaviors of a biological synapse, including long-term potentiation and long-term depression behaviors. Most importantly, a threshold-tunable leaky integrate-and-fire (TT-LIF) neuron is built using LixAlOy memristors, successfully integrating synaptic signals from both temporal and spatial levels and achieving an optimal threshold of SNNs. A computationally efficient TT-LIF-based SNN algorithm is also implemented for image recognition schemes, featuring a high recognition rate of 90.1% and an ultralow firing rate of 0.335%, which is 4 times lower than those of other memristor-based SNNs. Our studies reveal the ion dynamics mechanism of the LixAlOy memristor and confirm its potential in rapid switching and the construction of SNNs.

A Boundary Element Method of Bidomain Modeling for Predicting Cellular Responses to Electromagnetic Fields

Objective

Commonly used cable equation-based approaches for determining the effects of electromagnetic fields on excitable cells make several simplifying assumptions that could limit their predictive power. Bidomain or "whole" finite element methods have been developed to fully couple cells and electric fields for more realistic neuron modeling. Here, we introduce a novel bidomain integral equation designed for determining the full electromagnetic coupling between stimulation devices and the intracellular, membrane, and extracellular regions of neurons.

Methods

Our proposed boundary element formulation offers a solution to an integral equation that connects the device, tissue inhomogeneity, and cell membrane-induced E-fields. We solve this integral equation using first-order nodal elements and an unconditionally stable Crank-Nicholson time-stepping scheme. To validate and demonstrate our approach, we simulated cylindrical Hodgkin-Huxley axons and spherical cells in multiple brain stimulation scenarios.

Main Results

Comparison studies show that a boundary element approach produces accurate results for both electric and magnetic stimulation. Unlike bidomain finite element methods, the bidomain boundary element method does not require volume meshes containing features at multiple scales. As a result, modeling cells, or tightly packed populations of cells, with microscale features embedded in a macroscale head model, is made computationally tractable, and the relative placement of devices and cells can be varied without the need to generate a new mesh.

Significance

Device-induced electromagnetic fields are commonly used to modulate brain activity for research and therapeutic applications. Bidomain solvers allow for the full incorporation of realistic cell geometries, device E-fields, and neuron populations. Thus, multi-cell studies of advanced neuronal mechanisms would greatly benefit from the development of fast-bidomain solvers to ensure scalability and the practical execution of neural network simulations with realistic neuron morphologies.

Central pattern generator network model for the alternating hind limb gait of rats based on the modified Van der Pol equation

Herein, we employed a central pattern generator (CPG), a spinal cord neural network that regulates lower-limb gait during intra-spinal micro-stimulation (ISMS). Particularly, ISMS was used to determine the spatial distribution pattern of CPG sites in the spinal cord and the signal regulation pattern that induced the CPG network to produce coordinated actions. Based on the oscillation phenomenon of the single CPG neurons of Van der Pol (VDP) oscillators, a double-cell CPG neural network model was constructed to realise double lower limbs, six-joint modelling, the simulation of 12 neural circuits, the CPG loci characterising stimuli-inducing alternating movements and changes in polarity stimulation signals in rat hindlimbs, and leg-state change movements. The feasibility and effectiveness of the CPG neural network were verified by recording the electromyographic burst-release mode of the flexor and extensor muscles of the knee joints during CPG electrical stimulation. The results revealed that the output pattern of the CPG presented stable rhythm and coordination characteristics. The 12-neuron CPG model based on the improved VDP equation realised single-point control while significantly reducing the number of stimulation electrodes in the gait training of spinal cord injury patients. We believe that this study advances motor function recovery in rehabilitation medicine.

Molecular mechanism of the spider toxin κ-LhTx-I acting on the bacterial voltage-gated sodium channel NaChBac

The bacterial sodium channel NaChBac is the prokaryotic prototype for the eukaryotic Na_V and Ca_V channels, which could be used as a relatively simple model to study their structure–function relationships. However, few modulators of NaChBac have been reported thus far, and the pharmacology of NaChBac remains to be investigated. In the present study, we show that the spider toxin κ-LhTx-1, an antagonist of the K_V4 family potassium channels, potently inhibits NaChBac with an IC₅₀ of 491.0 ± 61.7 nM. Kinetics analysis revealed that κ-LhTx-1 inhibits NaChBac by impeding the voltage-sensor activation. Site-directed mutagenesis confirmed that phenylalanine-103 (F103) in the S3–S4 extracellular loop of NaChBac was critical for interacting with κ-LhTx-1. Molecular docking predicts the binding interface between κ-LhTx-1 and NaChBac and highlights a dominant hydrophobic interaction between W27 in κ-LhTx-1 and F103 in NaChBac that stabilizes the interface. In contrast, κ-LhTx-1 showed weak activity on the mammalian Na_V channels, with 10 µM toxin slightly inhibiting the peak currents of Na_V1.2–1.9 subtypes. Taken together, our study shows that κ-LhTx-1 inhibits the bacterial sodium channel, NaChBac, using a voltage-sensor trapping mechanism similar to mammalian Na_V site 4 toxins. κ-LhTx-1 could be used as a ligand to study the toxin–channel interactions in the native membrane environments, given that the NaChBac structure was successfully resolved in a nanodisc.

Feature Activation through First Power Linear Unit with Sign

The activation function represents a crucial component in the design of a convolutional neural network (CNN). It enables the efficient extraction of multiple features from visual patterns, and introduces systemic non-linearity to data processing. This paper proposes a novel and insightful activation method termed FPLUS, which exploits mathematical power function with polar signs in form. It is enlightened by common inverse operations while endowed with an intuitive meaning of bionics. The formulation is derived theoretically under conditions of some prior knowledge and anticipative properties. Subsequently, its feasibility is verified through a series of experiments using typical benchmark datasets. The results indicate that our approach bears superior competitiveness among numerous activation functions, as well as compatible stability across many CNN architectures. Furthermore, we extend the function presented to a more generalized type called PFPLUS with two parameters that can be fixed or learnable, so as to augment its expressive capacity. The outcomes of identical tests serve to validate this improvement. Therefore, we believe the work in this paper holds a certain value in enriching the family of activation units.

Controlling synchronization of gamma oscillations by astrocytic modulation in a model hippocampal neural network

Recent in vitro and in vivo experiments demonstrate that astrocytes participate in the maintenance of cortical gamma oscillations and recognition memory. However, the mathematical understanding of the underlying dynamical mechanisms remains largely incomplete. Here we investigate how the interplay of slow modulatory astrocytic signaling with fast synaptic transmission controls coherent oscillations in the network of hippocampal interneurons that receive inputs from pyramidal cells. We show that the astrocytic regulation of signal transmission between neurons improves the firing synchrony and extends the region of coherent oscillations in the biologically relevant values of synaptic conductance. Astrocyte-mediated potentiation of inhibitory synaptic transmission markedly enhances the coherence of network oscillations over a broad range of model parameters. Astrocytic regulation of excitatory synaptic input improves the robustness of interneuron network gamma oscillations induced by physiologically relevant excitatory model drive. These findings suggest a mechanism, by which the astrocytes become involved in cognitive function and information processing through modulating fast neural network dynamics.

Perspectives on Understanding Aberrant Brain Networks in Epilepsy

Epilepsy is a neurological disorder affecting approximately 70 million people worldwide. It is characterized by seizures that are complex aberrant dynamical events typically treated with drugs and surgery. Unfortunately, not all patients become seizure-free, and there is an opportunity for novel approaches to treat epilepsy using a network view of the brain. The traditional seizure focus theory presumed that seizures originated within a discrete cortical area with subsequent recruitment of adjacent cortices with seizure progression. However, a more recent view challenges this concept, suggesting that epilepsy is a network disease, and both focal and generalized seizures arise from aberrant activity in a distributed network. Changes in the anatomical configuration or widespread neural activities spanning lobes and hemispheres could make the brain more susceptible to seizures. In this perspective paper, we summarize the current state of knowledge, address several important challenges that could further improve our understanding of the human brain in epilepsy, and invite novel studies addressing these challenges.

BrainFreeze: Expanding the Capabilities of Neuromorphic Systems Using Mixed-Signal Superconducting Electronics

Superconducting electronics (SCE) is uniquely suited to implement neuromorphic systems. As a result, SCE has the potential to enable a new generation of neuromorphic architectures that can simultaneously provide scalability, programmability, biological fidelity, on-line learning support, efficiency and speed. Supporting all of these capabilities simultaneously has thus far proven to be difficult using existing semiconductor technologies. However, as the fields of computational neuroscience and artificial intelligence (AI) continue to advance, the need for architectures that can provide combinations of these capabilities will grow. In this paper, we will explain how superconducting electronics could be used to address this need by combining analog and digital SCE circuits to build large scale neuromorphic systems. In particular, we will show through detailed analysis that the available SCE technology is suitable for near term neuromorphic demonstrations. Furthermore, this analysis will establish that neuromorphic architectures built using SCE will have the potential to be significantly faster and more efficient than current approaches, all while supporting capabilities such as biologically suggestive neuron models and on-line learning. In the future, SCE-based neuromorphic systems could serve as experimental platforms supporting investigations that are not feasible with current approaches. Ultimately, these systems and the experiments that they support would enable the advancement of neuroscience and the development of more sophisticated AI.

Quantitative roles of ion channel dynamics on ventricular action potential

Mathematical models for the action potential (AP) generation of the electrically excitable cells including the heart are involved different mechanisms including the voltage-dependent currents with nonlinear time- and voltage-gating properties. From the shape of the AP waveforms to the duration of the refractory periods or heart rhythms are greatly affected by the functions describing the features or the quantities of these ion channels. In this work, a mathematical measure to analyze the regional contributions of voltage-gated channels is defined by dividing the AP into phases, epochs, and intervals of interest. The contribution of each time-dependent current for the newly defined cardiomyocyte model is successfully calculated and it is found that the contribution of dominant ion channels changes substantially not only for each phase but also for different regions of the cardiac AP. Besides, the defined method can also be applied in all Hodgkin-Huxley types of electrically excitable cell models to be able to understand the underlying dynamics better.

Memristive Hodgkin-Huxley Spiking Neuron Model for Reproducing Neuron Behaviors

The Hodgkin-Huxley (HH) spiking neuron model reproduces the dynamic characteristics of the neuron by mimicking the action potential, ionic channels, and spiking behaviors. The memristor is a nonlinear device with variable resistance. In this paper, the memristor is introduced to the HH spiking model, and the memristive Hodgkin-Huxley spiking neuron model (MHH) is presented. We experimentally compare the HH spiking model and the MHH spiking model by applying different stimuli. First, the individual current pulse is injected into the HH and MHH spiking models. The comparison between action potentials, current densities, and conductances is carried out. Second, the reverse single pulse stimulus and a series of pulse stimuli are applied to the two models. The effects of current density and action time on the production of the action potential are analyzed. Finally, the sinusoidal current stimulus acts on the two models. The various spiking behaviors are realized by adjusting the frequency of the sinusoidal stimulus. We experimentally demonstrate that the MHH spiking model generates more action potential than the HH spiking model and takes a short time to change the memductance. The reverse stimulus cannot activate the action potential in both models. The MHH spiking model performs smoother waveforms and a faster speed to return to the resting potential. The larger the external stimulus, the faster action potential generated, and the more noticeable change in conductances. Meanwhile, the MHH spiking model shows the various spiking patterns of neurons.

A Low-Power Spiking Neural Network Chip Based on a Compact LIF Neuron and Binary Exponential Charge Injector Synapse Circuits

To realize a large-scale Spiking Neural Network (SNN) on hardware for mobile applications, area and power optimized electronic circuit design is critical. In this work, an area and power optimized hardware implementation of a large-scale SNN for real time IoT applications is presented. The analog Complementary Metal Oxide Semiconductor (CMOS) implementation incorporates neuron and synaptic circuits optimized for area and power consumption. The asynchronous neuronal circuits implemented benefit from higher energy efficiency and higher sensitivity. The proposed synapse circuit based on Binary Exponential Charge Injector (BECI) saves area and power consumption, and provides design scalability for higher resolutions. The SNN model implemented is optimized for 9 × 9 pixel input image and minimum bit-width weights that can satisfy target accuracy, occupies less area and power consumption. Moreover, the spiking neural network is replicated in full digital implementation for area and power comparisons. The SNN chip integrated from neuron and synapse circuits is capable of pattern recognition. The proposed SNN chip is fabricated using 180 nm CMOS process, which occupies a 3.6 mm² chip core area, and achieves a classification accuracy of 94.66% for the MNIST dataset. The proposed SNN chip consumes an average power of 1.06 mW—20 times lower than the digital implementation.

Temporal coherency of mechanical stimuli modulates tactile form perception

The human hand can detect both form and texture information of a contact surface. The detection of skin displacement (sustained stimulus) and changes in skin displacement (transient stimulus) are thought to be mediated in different tactile channels; however, tactile form perception may use both types of information. Here, we studied whether both the temporal frequency and the temporal coherency information of tactile stimuli encoded in sensory neurons could be used to recognize the form of contact surfaces. We used the fishbone tactile illusion (FTI), a known tactile phenomenon, as a probe for tactile form perception in humans. This illusion typically occurs with a surface geometry that has a smooth bar and coarse textures in its adjacent areas. When stroking the central bar back and forth with a fingertip, a human observer perceives a hollow surface geometry even though the bar is physically flat. We used a passive high-density pin matrix to extract only the vertical information of the contact surface, suppressing tangential displacement from surface rubbing. Participants in the psychological experiment reported indented surface geometry by tracing over the FTI textures with pin matrices of the different spatial densities (1.0 and 2.0 mm pin intervals). Human participants reported that the relative magnitude of perceived surface indentation steeply decreased when pins in the adjacent areas vibrated in synchrony. To address possible mechanisms for tactile form perception in the FTI, we developed a computational model of sensory neurons to estimate temporal patterns of action potentials from tactile receptive fields. Our computational data suggest that (1) the temporal asynchrony of sensory neuron responses is correlated with the relative magnitude of perceived surface indentation and (2) the spatiotemporal change of displacements in tactile stimuli are correlated with the asynchrony of simulated sensory neuron responses for the fishbone surface patterns. Based on these results, we propose that both the frequency and the asynchrony of temporal activity in sensory neurons could produce tactile form perception.

The Study for Synchronization between Two Coupled FitzHugh-Nagumo Neurons Based on the Laplace Transform and the Adomian Decomposition Method

The synchronization between two coupled FitzHugh-Nagumo (FHN) neurons with or without external current is studied by using the Laplace transform and the Adomian decomposition method. Different from other researches, the synchronization error system is expressed as sets of Volterra integral equations based on the convolution theorem in the Laplace transform. Then, it is easy to analytically obtain the conditions that synchronization errors disappear based on the successive approximation method in integral equation theorem, the correctness of which is verified by numerical simulations. Furthermore, the synchronous dynamics of the two coupled FHN neurons also can be written in the form of Volterra integral equations, which is more convenient to analytically solve by using the Adomian decomposition method. It is found that the occurrence of synchronization between the two FHN neurons only depends on the coupling strength and is irrelevant to the external current. Only synchronous rest state in the two FHN neurons without external current can be achieved, while synchronous spikes appear if the external current is not zero.

Modeling Working Memory in a Spiking Neuron Network Accompanied by Astrocytes

We propose a novel biologically plausible computational model of working memory (WM) implemented by a spiking neuron network (SNN) interacting with a network of astrocytes. The SNN is modeled by synaptically coupled Izhikevich neurons with a non-specific architecture connection topology. Astrocytes generating calcium signals are connected by local gap junction diffusive couplings and interact with neurons via chemicals diffused in the extracellular space. Calcium elevations occur in response to the increased concentration of the neurotransmitter released by spiking neurons when a group of them fire coherently. In turn, gliotransmitters are released by activated astrocytes modulating the strength of the synaptic connections in the corresponding neuronal group. Input information is encoded as two-dimensional patterns of short applied current pulses stimulating neurons. The output is taken from frequencies of transient discharges of corresponding neurons. We show how a set of information patterns with quite significant overlapping areas can be uploaded into the neuron-astrocyte network and stored for several seconds. Information retrieval is organized by the application of a cue pattern representing one from the memory set distorted by noise. We found that successful retrieval with the level of the correlation between the recalled pattern and ideal pattern exceeding 90% is possible for the multi-item WM task. Having analyzed the dynamical mechanism of WM formation, we discovered that astrocytes operating at a time scale of a dozen of seconds can successfully store traces of neuronal activations corresponding to information patterns. In the retrieval stage, the astrocytic network selectively modulates synaptic connections in the SNN leading to successful recall. Information and dynamical characteristics of the proposed WM model agrees with classical concepts and other WM models.

Granular layEr Simulator: Design and Multi-GPU Simulation of the Cerebellar Granular Layer

In modern computational modeling, neuroscientists need to reproduce long-lasting activity of large-scale networks, where neurons are described by highly complex mathematical models. These aspects strongly increase the computational load of the simulations, which can be efficiently performed by exploiting parallel systems to reduce the processing times. Graphics Processing Unit (GPU) devices meet this need providing on desktop High Performance Computing. In this work, authors describe a novel Granular layEr Simulator development implemented on a multi-GPU system capable of reconstructing the cerebellar granular layer in a 3D space and reproducing its neuronal activity. The reconstruction is characterized by a high level of novelty and realism considering axonal/dendritic field geometries, oriented in the 3D space, and following convergence/divergence rates provided in literature. Neurons are modeled using Hodgkin and Huxley representations. The network is validated by reproducing typical behaviors which are well-documented in the literature, such as the center-surround organization. The reconstruction of a network, whose volume is 600 × 150 × 1,200 μm³ with 432,000 granules, 972 Golgi cells, 32,399 glomeruli, and 4,051 mossy fibers, takes 235 s on an Intel i9 processor. The 10 s activity reproduction takes only 4.34 and 3.37 h exploiting a single and multi-GPU desktop system (with one or two NVIDIA RTX 2080 GPU, respectively). Moreover, the code takes only 3.52 and 2.44 h if run on one or two NVIDIA V100 GPU, respectively. The relevant speedups reached (up to ~38× in the single-GPU version, and ~55× in the multi-GPU) clearly demonstrate that the GPU technology is highly suitable for realistic large network simulations.

Brain Inspired Sequences Production by Spiking Neural Networks With Reward-Modulated STDP

Understanding and producing embedded sequences according to supra-regular grammars in language has always been considered a high-level cognitive function of human beings, named "syntax barrier" between humans and animals. However, some neurologists recently showed that macaques could be trained to produce embedded sequences involving supra-regular grammars through a well-designed experiment paradigm. Via comparing macaques and preschool children's experimental results, they claimed that human uniqueness might only lie in the speed and learning strategy resulting from the chunking mechanism. Inspired by their research, we proposed a Brain-inspired Sequence Production Spiking Neural Network (SP-SNN) to model the same production process, followed by memory and learning mechanisms of the multi-brain region cooperation. After experimental verification, we demonstrated that SP-SNN could also handle embedded sequence production tasks, striding over the "syntax barrier." SP-SNN used Population-Coding and STDP mechanism to realize working memory, Reward-Modulated STDP mechanism for acquiring supra-regular grammars. Therefore, SP-SNN needs to simultaneously coordinate short-term plasticity (STP) and long-term plasticity (LTP) mechanisms. Besides, we found that the chunking mechanism indeed makes a difference in improving our model's robustness. As far as we know, our work is the first one toward the "syntax barrier" in the SNN field, providing the computational foundation for further study of related underlying animals' neural mechanisms in the future.

A Synchronization Criterion for Two Hindmarsh-Rose Neurons with Linear and Nonlinear Coupling Functions Based on the Laplace Transform Method

In this paper, an analytical criterion is proposed to investigate the synchronization between two Hindmarsh-Rose neurons with linear and nonlinear coupling functions based on the Laplace transform method. Different from previous works, the synchronization error system is expressed in its integral form, which is more convenient to analyze. The synchronization problem of two HR coupled neurons is ultimately converted into the stability problem of roots to a nonlinear algebraic equation. Then, an analytical criterion for synchronization between the two HR neurons can be given by using the Routh-Hurwitz criterion. Numerical simulations show that the synchronization criterion derived in this paper is valid, regardless of the periodic spikes or burst-spike chaotic behavior of the two HR neurons. Furthermore, the analytical results have almost the same accuracy as the conditional Lyapunov method. In addition, the calculation quantities always are small no matter the linear and nonlinear coupling functions, which show that the approach presented in this paper is easy to be developed to study synchronization between a large number of HR neurons.

Platoon control design for unmanned surface vehicles subject to input delay

Vessel train formation as a new trend has been raised in cooperative control for multiple vessels. This paper addresses formation control design for a group of unmanned surface vehicles platoon considering input delay. To account for connectivity-preserving and collision-avoiding, Barrier Lyapunov function is incorporated into the constraints design of line-of-sight range and bearing. To alleviate the computational burden, neural dynamic model is employed to simplify the control design and smooth the input signals. Besides, input control arising from time delay due to mechanisms and communication is considered in the marine vessels. Within the framework of the backstepping technique, distributed coordination is accomplished in finite time and the uniformly ultimately boundness of overall system is guaranteed via rigorous stability analysis. Finally, the simulation is performed to verify the effectiveness of the proposed control method.

Local glutamate-mediated dendritic plateau potentials change the state of the cortical pyramidal neuron

Dendritic spikes in thin dendritic branches (basal and oblique dendrites) are traditionally inferred from spikelets measured in the cell body. Here, we used laser-spot voltage-sensitive dye imaging in cortical pyramidal neurons (rat brain slices) to investigate the voltage waveforms of dendritic potentials occurring in response to spatially restricted glutamatergic inputs. Local dendritic potentials lasted 200-500 ms and propagated to the cell body, where they caused sustained 10- to 20-mV depolarizations. Plateau potentials propagating from dendrite to soma and action potentials propagating from soma to dendrite created complex voltage waveforms in the middle of the thin basal dendrite, comprised of local sodium spikelets, local plateau potentials, and backpropagating action potentials, superimposed on each other. Our model replicated these voltage waveforms across a gradient of glutamatergic stimulation intensities. The model then predicted that somatic input resistance (Rin) and membrane time constant (tau) may be reduced during dendritic plateau potential. We then tested these model predictions in real neurons and found that the model correctly predicted the direction of Rin and tau change but not the magnitude. In summary, dendritic plateau potentials occurring in basal and oblique branches put pyramidal neurons into an activated neuronal state ("prepared state"), characterized by depolarized membrane potential and smaller but faster membrane responses. The prepared state provides a time window of 200-500 ms, during which cortical neurons are particularly excitable and capable of following afferent inputs. At the network level, this predicts that sets of cells with simultaneous plateaus would provide cellular substrate for the formation of functional neuronal ensembles.NEW & NOTEWORTHY In cortical pyramidal neurons, we recorded glutamate-mediated dendritic plateau potentials with voltage imaging and created a computer model that recreated experimental measures from dendrite and cell body. Our model made new predictions, which were then tested in experiments. Plateau potentials profoundly change neuronal state: a plateau potential triggered in one basal dendrite depolarizes the soma and shortens membrane time constant, making the cell more susceptible to firing triggered by other afferent inputs.

A New Spiking Convolutional Recurrent Neural Network (SCRNN) With Applications to Event-Based Hand Gesture Recognition

The combination of neuromorphic visual sensors and spiking neural network offers a high efficient bio-inspired solution to real-world applications. However, processing event- based sequences remains challenging because of the nature of their asynchronism and sparsity behavior. In this paper, a novel spiking convolutional recurrent neural network (SCRNN) architecture that takes advantage of both convolution operation and recurrent connectivity to maintain the spatial and temporal relations from event-based sequence data are presented. The use of recurrent architecture enables the network to have a sampling window with an arbitrary length, allowing the network to exploit temporal correlations between event collections. Rather than standard ANN to SNN conversion techniques, the network utilizes a supervised Spike Layer Error Reassignment (SLAYER) training mechanism that allows the network to adapt to neuromorphic (event-based) data directly. The network structure is validated on the DVS gesture dataset and achieves a 10 class gesture recognition accuracy of 96.59% and an 11 class gesture recognition accuracy of 90.28%.

Neural Tissue Degeneration in Rosenthal's Canal and Its Impact on Electrical Stimulation of the Auditory Nerve by Cochlear Implants: An Image-Based Modeling Study

Sensorineural deafness is caused by the loss of peripheral neural input to the auditory nerve, which may result from peripheral neural degeneration and/or a loss of inner hair cells. Provided spiral ganglion cells and their central processes are patent, cochlear implants can be used to electrically stimulate the auditory nerve to facilitate hearing in the deaf or severely hard-of-hearing. Neural degeneration is a crucial impediment to the functional success of a cochlear implant. The present, first-of-its-kind two-dimensional finite-element model investigates how the depletion of neural tissues might alter the electrically induced transmembrane potential of spiral ganglion neurons. The study suggests that even as little as 10% of neural tissue degeneration could lead to a disproportionate change in the stimulation profile of the auditory nerve. This result implies that apart from encapsulation layer formation around the cochlear implant electrode, tissue degeneration could also be an essential reason for the apparent inconsistencies in the functionality of cochlear implants.

Electrical cell-to-cell communication using aggregates of model cells

Cell-to-cell communication via a local current caused by ion transport is elucidated using a model-cell system. To imitate tissues such as smooth muscles and cardiac muscles, liquid-membrane cells mimicking the function of K+ and Na+ channels were made. Connecting these channel-mimicking cells (K+ channel and voltage-gated Na+ channel) in parallel, model cells imitating living cell functions were constructed. Action-potential propagation within the cell aggregate model constructed by multiple model cells was investigated. When an action potential was generated at one cell, the cell behaved as an electric power source. Since a circulating current flowed around the cell, it flowed through neighboring model cells. Influx and efflux currents caused negative and positive shifts of the membrane potential, respectively, on the surface of neighboring model cells. The action potential was generated at the depolarized domain when the membrane potential exceeded the threshold of the voltage-gated Na+ channels. Thus, the action potential spread all over the cell system. When an external electric stimulus was applied to the layered cell-aggregate model system, propagation of the action potential was facilitated as if they were synchronized.

A modeling study of spinal motoneuron recruitment regulated by ionic channels during fictive locomotion

During fictive locomotion cat lumbar motoneurons exhibit changes in membrane proprieties including a decrease in voltage threshold (V_th), afterhyperpolarization (AHP) and input resistance (R_in) and an increase in non-linear membrane property. The impact of these changes on the motoneuron recruitment remains unknown. Using modeling approach we investigated the channel mechanism regulating the motoneuron recruitment. Three types of motoneuron pools including slow (S), fatigue-resistant (FR) and fast-fatigable (FF) motoneurons were constructed based on the membrane proprieties of cat lumbar motoneurons. The transient sodium (NaT), persistent sodium (NaP), delayed-rectifier potassium [K(DR)], Ca²⁺-dependent K⁺ [K(AHP)] and L-type calcium (Ca_L) channels were included in the models. Simulation results showed that (1) Strengthening synaptic inputs increased the number of recruitments in all three types of motoneurons following the size principle. (2) Increasing NaT or NaP or decreasing K(DR) or K(AHP) lowered rheobase of spike generation thus increased recruitment of motoneuron pools. (3) Decreasing R_in reduced recruitment in all three types of motoneurons. (4) The FF-type motoneuron pool, followed by FR- and S-type, were the most sensitive to increase of synaptic inputs, reduction of R_in, upregulation of NaT and NaP, and downregulation of K(DR) and K(AHP). (5) Increasing Ca_L enhanced overall discharge rate of motoneuron pools with little effect on the recruitment. Simulation results suggested that modulation of ionic channels altered the output of motoneuron pools with either modulating the number of recruited motoneurons or regulating the overall discharge rate of motoneuron pools. Multiple channels contributed to the recruitment of motoneurons with interaction of excitatory and inhibitory synaptic inputs during walking.

Double MgO-Based Perpendicular Magnetic Tunnel Junction for Artificial Neuron

A perpendicular spin transfer torque (p-STT)-based neuron was developed for a spiking neural network (SNN). It demonstrated the integration behavior of a typical neuron in an SNN; in particular, the integration behavior corresponding to magnetic resistance change gradually increased with the input spike number. This behavior occurred when the spin electron directions between double Co₂Fe₆B₂ free and pinned layers in the p-STT-based neuron were switched from parallel to antiparallel states. In addition, a neuron circuit for integrate-and-fire operation was proposed. Finally, pattern-recognition simulation was performed for a single-layer SNN.

The Appearance Order of Varying Intervals Introduces Extra Modulation Effects on Neuronal Firing Through Non-linear Dynamics of Sodium Channels During High-Frequency Stimulations

Electrical pulse stimulation in the brain has shown success in treating several brain disorders with constant pulse frequency or constant inter-pulse interval (IPI). Varying IPI may offer a variety of novel stimulation paradigms and may extend the clinical applications. However, a lack of understanding of neuronal responses to varying IPI limits its informed applications. In this study, to investigate the effects of varying IPI, we performed both rat experiments and computational modeling by applying high-frequency stimulation (HFS) to efferent axon fibers of hippocampal pyramidal cells. Antidromically evoked population spikes (PSs) were used to evaluate the neuronal responses to pulse stimulations with different IPI patterns including constant IPI, gradually varying IPI, and randomly varying IPI. All the varying IPI sequences were uniformly distributed in the same interval range of 10 to 5 ms (i.e., 100 to 200 Hz). The experimental results showed that the mean correlation coefficient of PS amplitudes to the lengths of preceding IPI during HFS with random IPI (0.72 ± 0.04, n = 7 rats) was significantly smaller than the corresponding correlation coefficient during HFS with gradual IPI (0.92 ± 0.03, n = 7 rats, P < 0.001, t-test). The PS amplitudes induced by the random IPI covered a wider range, over twice as much as that induced by the gradual IPI, indicating additional effects induced by merely changing the appearance order of IPI. The computational modeling reproduced these experimental results and provided insights into these modulatory effects through the mechanism of non-linear dynamics of sodium channels and potassium accumulation in the narrow peri-axonal space. The simulation results showed that the HFS-induced increase of extracellular potassium ([K⁺] _o ) elevated the membrane potential of axons, delayed the recovery course of sodium channels that were repeatedly activated and inactivated during HFS, and resulted in intermittent neuronal firing. Because of non-linear membrane dynamics, random IPI recruited more neurons to fire together following specific sub-sequences of pulses than gradual IPI, thereby widening the range of PS amplitudes. In conclusion, the study demonstrated novel HFS effects of neuronal modulation induced by merely changing the appearance order of the same group of IPI of pulses, which may inform the development of new stimulation patterns to meet different demands for treating various brain diseases.

Effects of synaptic integration on the dynamics and computational performance of spiking neural network

Neurons in the brain receive thousands of synaptic inputs from other neurons. This afferent information is processed by neurons through synaptic integration, which is an important information processing mechanism in biological neural networks. Synaptic currents integrated from spiking trains of presynaptic neurons have complex nonlinear dynamics which endow neurons with significant computational abilities. However, in many computational studies of neural networks, external input currents are often simply taken as a direct current that is static. In this paper, the influences of synaptic and noise external currents on the dynamics of spiking neural network and its computational capability have been investigated in detail. Our results show that due to the nonlinear synaptic integration, both of fast and slow excitatory synaptic currents have much more complex and oscillatory fluctuations than the noise current with the same average intensity. Thus network driven by synaptic external current exhibits remarkably more complex dynamics than that driven by noise external current. Interestingly, the enhancement of network activity is beneficial for information transmission, which is further supported by two computational tasks conducted on the liquid state machine (LSM) network. LSM with synaptic external current displays considerably better performance in both nonlinear fitting and pattern classification than that with noise external current. Synaptic integration can significantly enhance the entropy of activity patterns and computational performance of LSM. Our results demonstrate that the complex dynamics of nonlinear synaptic integration play a critical role in the computational abilities of neural networks and should be more broadly considered in the modelling studies of spiking neural networks.

Intracellular Chloride Channels: Novel Biomarkers in Diseases

Ion channels are integral membrane proteins present on the plasma membrane as well as intracellular membranes. In the human genome, there are more than 400 known genes encoding ion channel proteins. Ion channels are known to regulate several cellular, organellar, and physiological processes. Any mutation or disruption in their function can result in pathological disorders, both common or rare. Ion channels present on the plasma membrane are widely acknowledged for their role in various biological processes, but in recent years, several studies have pointed out the importance of ion channels located in intracellular organelles. However, ion channels located in intracellular organelles are not well-understood in the context of physiological conditions, such as the generation of cellular excitability and ionic homeostasis. Due to the lack of information regarding their molecular identity and technical limitations of studying them, intracellular organelle ion channels have thus far been overlooked as potential therapeutic targets. In this review, we focus on a novel class of intracellular organelle ion channels, Chloride Intracellular Ion Channels (CLICs), mainly documented for their role in cardiovascular, neurophysiology, and tumor biology. CLICs have a single transmembrane domain, and in cells, they exist in cytosolic as well as membranous forms. They are predominantly present in intracellular organelles and have recently been shown to be localized to cardiomyocyte mitochondria as well as exosomes. In fact, a member of this family, CLIC5, is the first mitochondrial chloride channel to be identified on the molecular level in the inner mitochondrial membrane, while another member, CLIC4, is located predominantly in the outer mitochondrial membrane. In this review, we discuss this unique class of intracellular chloride channels, their role in pathologies, such as cardiovascular, cancer, and neurodegenerative diseases, and the recent developments concerning their usage as theraputic targets.

The General Anesthetic Isoflurane Bilaterally Modulates Neuronal Excitability

Volatile anesthetics induce hyperactivity during induction while producing anesthesia at higher concentrations. They also bidirectionally modulate many neuronal functions. However, the neuronal mechanism is unclear. The effects of isoflurane on sodium channel currents were analyzed in acute mouse brain slices, including sodium leak (NALCN) currents and voltage-gated sodium channels (Nav) currents. Isoflurane at sub-anesthetic concentrations increased the spontaneous firing rate of CA3 pyramidal neurons, whereas anesthetic concentrations of isoflurane decreased the firing rate. Isoflurane at sub-anesthetic concentrations enhanced NALCN conductance but minimally inhibited Nav currents. Isoflurane at anesthetic concentrations depressed Nav currents and action potential amplitudes. Isoflurane at sub-anesthetic concentrations depolarized resting membrane potential (RMP) of neurons, whereas hyperpolarized the RMP at anesthetic concentrations. Isoflurane at low concentrations induced hyperactivity in vivo, which was diminished in NALCN knockdown mice. In conclusion, enhancement of NALCN by isoflurane contributes to its bidirectional modulation of neuronal excitability and the hyperactivity during induction.

Network Models Predict That Pyramidal Neuron Hyperexcitability and Synapse Loss in the dlPFC Lead to Age-Related Spatial Working Memory Impairment in Rhesus Monkeys

Behavioral studies have shown spatial working memory impairment with aging in several animal species, including humans. Persistent activity of layer 3 pyramidal dorsolateral prefrontal cortex (dlPFC) neurons during delay periods of working memory tasks is important for encoding memory of the stimulus. In vitro studies have shown that these neurons undergo significant age-related structural and functional changes, but the extent to which these changes affect neural mechanisms underlying spatial working memory is not understood fully. Here, we confirm previous studies showing impairment on the Delayed Recognition Span Task in the spatial condition (DRSTsp), and increased in vitro action potential firing rates (hyperexcitability), across the adult life span of the rhesus monkey. We use a bump attractor model to predict how empirically observed changes in the aging dlPFC affect performance on the Delayed Response Task (DRT), and introduce a model of memory retention in the DRSTsp. Persistent activity-and, in turn, cognitive performance-in both models was affected much more by hyperexcitability of pyramidal neurons than by a loss of synapses. Our DRT simulations predict that additional changes to the network, such as increased firing of inhibitory interneurons, are needed to account for lower firing rates during the DRT with aging reported in vivo. Synaptic facilitation was an essential feature of the DRSTsp model, but it did not compensate fully for the effects of the other age-related changes on DRT performance. Modeling pyramidal neuron hyperexcitability and synapse loss simultaneously led to a partial recovery of function in both tasks, with the simulated level of DRSTsp impairment similar to that observed in aging monkeys. This modeling work integrates empirical data across multiple scales, from synapse counts to cognitive testing, to further our understanding of aging in non-human primates.

A cortical model with multi-layers to study visual attentional modulation of neurons at the synaptic level

Visual attention is a selective process of visual information and improves perceptual performance by modulating activities of neurons in the visual system. It has been reported that attention increased firing rates of neurons, reduced their response variability and improved reliability of coding relevant stimuli. Recent neurophysiological studies demonstrated that attention also enhanced the synaptic efficacy between neurons mediated through NMDA and AMPA receptors. Majority of computational models of attention usually are based on firing rates, which cannot explain attentional modulations observed at the synaptic level. To understand mechanisms of attentional modulations at the synaptic level, we proposed a neural network consisting of three layers, corresponding to three different brain regions. Each layer has excitatory and inhibitory neurons. Each neuron was modeled by the Hodgkin-Huxley model. The connections between neurons were through excitatory AMPA and NMDA receptors, as well as inhibitory GABAA receptors. Since the binding process of neurotransmitters with receptors is stochastic in the synapse, it is hypothesized that attention could reduce the variation of the stochastic binding process and increase the fraction of bound receptors in the model. We investigated how attention modulated neurons' responses at the synaptic level on the basis of this hypothesis. Simulated results demonstrated that attention increased firing rates of neurons and reduced their response variability. The attention-induced effects were stronger in higher regions compared to those in lower regions, and stronger for inhibitory neurons than for excitatory neurons. In addition, AMPA receptor antagonist (CNQX) impaired attention-induced modulations on neurons' responses, while NMDA receptor antagonist (APV) did not. These results suggest that attention may modulate neuronal activity at the synaptic level.

Frequency cluster formation and slow oscillations in neural populations with plasticity

We report the phenomenon of frequency clustering in a network of Hodgkin-Huxley neurons with spike timing-dependent plasticity. The clustering leads to a splitting of a neural population into a few groups synchronized at different frequencies. In this regime, the amplitude of the mean field undergoes low-frequency modulations, which may contribute to the mechanism of the emergence of slow oscillations of neural activity observed in spectral power of local field potentials or electroencephalographic signals at high frequencies. In addition to numerical simulations of such multi-clusters, we investigate the mechanisms of the observed phenomena using the simplest case of two clusters. In particular, we propose a phenomenological model which describes the dynamics of two clusters taking into account the adaptation of coupling weights. We also determine the set of plasticity functions (update rules), which lead to multi-clustering.

Shaw and Shal voltage-gated potassium channels mediate circadian changes in Drosophila clock neuron excitability

As in mammals, Drosophila circadian clock neurons display rhythms of activity with higher action potential firing rates and more positive resting membrane potentials during the day. This rhythmic excitability has been widely observed but, critically, its regulation remains unresolved. We have characterized and modelled the changes underlying these electrical activity rhythms in the lateral ventral clock neurons (LNvs). We show that currents mediated by the voltage-gated potassium channels Shaw (Kv3) and Shal (Kv4) oscillate in a circadian manner. Disruption of these channels, by expression of dominant negative (DN) subunits, leads to changes in circadian locomotor activity and shortens lifespan. LNv whole-cell recordings then show that changes in Shaw and Shal currents drive changes in action potential firing rate and that these rhythms are abolished when the circadian molecular clock is stopped. A whole-cell biophysical model using Hodgkin-Huxley equations can recapitulate these changes in electrical activity. Based on this model and by using dynamic clamp to manipulate clock neurons directly, we can rescue the pharmacological block of Shaw and Shal, restore the firing rhythm, and thus demonstrate the critical importance of Shaw and Shal. Together, these findings point to a key role for Shaw and Shal in controlling circadian firing of clock neurons and show that changes in clock neuron currents can account for this. Moreover, with dynamic clamp we can switch the LNvs between morning-like and evening-like states of electrical activity. We conclude that changes in Shaw and Shal underlie the daily oscillation in LNv firing rate.

Multiscale Modeling for Application-Oriented Optimization of Resistive Random-Access Memory

Memristor-based neuromorphic systems have been proposed as a promising alternative to von Neumann computing architectures, which are currently challenged by the ever-increasing computational power required by modern artificial intelligence (AI) algorithms. The design and optimization of memristive devices for specific AI applications is thus of paramount importance, but still extremely complex, as many different physical mechanisms and their interactions have to be accounted for, which are, in many cases, not fully understood. The high complexity of the physical mechanisms involved and their partial comprehension are currently hampering the development of memristive devices and preventing their optimization. In this work, we tackle the application-oriented optimization of Resistive Random-Access Memory (RRAM) devices using a multiscale modeling platform. The considered platform includes all the involved physical mechanisms (i.e., charge transport and trapping, and ion generation, diffusion, and recombination) and accounts for the 3D electric and temperature field in the device. Thanks to its multiscale nature, the modeling platform allows RRAM devices to be simulated and the microscopic physical mechanisms involved to be investigated, the device performance to be connected to the material’s microscopic properties and geometries, the device electrical characteristics to be predicted, the effect of the forming conditions (i.e., temperature, compliance current, and voltage stress) on the device’s performance and variability to be evaluated, the analog resistance switching to be optimized, and the device’s reliability and failure causes to be investigated. The discussion of the presented simulation results provides useful insights for supporting the application-oriented optimization of RRAM technology according to specific AI applications, for the implementation of either non-volatile memories, deep neural networks, or spiking neural networks.

Unidirectional Synchronization of Hodgkin-Huxley Neurons With Prescribed Performance Under Transcranial Magneto-Acoustical Simulation

This paper exploits the unidirectional synchronization dynamics of two Hodgkin-Huxley (HH) neurons under transcranial magneto-acoustical stimulation (TMAS). The major purpose is to explore a control scheme to make the spiking modes of the neural potentials stimulated by TMAS achieve synchronization states under the feedback input. For this purpose, an adaptive neural controller, which makes the neurons satisfy the prescribed master-slaver synchronization performance, is designed by introducing a tracking error into Lyapunov analysis. Under the proposed control scheme, the slaver neuron can not only overcome the model uncertainties and the difficulties brought by prescribed performance, but also track the spiking patterns of the master neuron. Finally, the simulations are implemented to demonstrate the effectiveness of the proposed controller, that is, the TMAS induced synchronization states of the HH neuron system can achieve the prescribed performance under the proposed controller.

Bibliometric analysis of recent sodium channel research

Although sodium channels have been a hot multidisciplinary focus for decades and most of nerve system drugs worked on alerting sodium channel function, the trends and future directions of sodium channel studies have not been comprehensive analyzed bibliometrically. Herein, we collected the scientific publications of sodium channels research and constructed a model to evaluate the current trend systematically.

Publications were selected from the Web of Science Core Collection (WoSCC) database from 2013 to 2017. Microsoft Excel 2016, Prism 6, and CiteSpace V software were used to analyze publication outputs, journal sources, countries, territories, institutions, authors, and research areas. A total of 4,275 publications on sodium channel research were identified. PLoS ONE ranked top for publishing 170 papers. The United States of America had the largest number of publications (1,595), citation frequency (19,490), and H-index (53). S. G. Waxman (62 publications) and W. A. Catterall (585 citations) were the most productive authors and had the greatest co-citation counts. This is the first report that shows the trends and future development in sodium channel publications, and our study provides a clear profile for the contribution to this field by countries, authors, keywords, and institutions.

A novel IRBF-RVM model for diagnosis of atrial fibrillation

BACKGROUND AND OBJECTIVE

Atrial fibrillation (AF) is one of the common cardiovascular diseases, and electrocardiography (ECG) is a key indicator for the detection and diagnosis of AF and other heart diseases. In this study, an improved machine learning method is proposed for rapid modeling and accurate diagnosis of AF.

METHODS

This paper presents a novel IRBF-RVM model that combines the integrated radial basis function (IRBF) and relevance vector machine (RVM), which is utilized for the diagnosis of AF. The synchronous 12-lead ECG signals are collected from the human body surface so as to fully reflect the electrical activity of the whole heart. RR intervals of the QRS-waves in ECG signals are obtained by means of the classical Pan-Tompkins algorithm. The RR-features extracted from RR intervals are adopted as the diagnostic features for AF patients. In addition, the conventional RBF-RVM model, support vector machine (SVM) and other machine learning methods are also investigated for the diagnosis of AF so as to reflect the advantage of the proposed IRBF-RVM model. The open MIT-BIH arrhythmia database (MITDB) is also used to evaluate the predictive performance of these state-of-the-art methods.

RESULTS

Altogether 1056 AF patients and 904 healthy people are participated in this study and validate the effectiveness of each channel of the 12-lead ECG signals. Experimental results show that the classification rate of IRBF-RVM can reach up to 98.16% by recurring to Channel II of the 12-lead ECG signals.

CONCLUSIONS

IRBF-RVM absorbs the advantages of IRBF, which makes the kernel parameter of IRBF-RVM have a much larger selectable region than RBF-RVM. In addition, RVM has faster modeling and recognition speed in comparison with SVM. This work lays the foundation for the application of RVM to accurate diagnosis of AF.

Electric activities of time-delay memristive neuron disturbed by Gaussian white noise

To study the effect of electromagnetic induction on the electric activities of neuron, memristive neuron models have been proposed by coupling membrane potential with magnetic flux. In this paper, on the basis of memristive Hindmarsh-Rose neuron model, time-delay memristive Hindmarsh-Rose neuron model is described and the responses of neuron in electrical activities are detected. The effect of time-delay on the dynamical behaviors of the neuron is discussed and the transition of electrical activities of the neuron is investigated with the change of noise intensity. It is found that, both the time-delay and the noise have effect on the electrical activities of the neuron. Especially, by selecting appropriate parameters, the noise not only can excite neuron from quiescent state to bursting state, but also can suppress the electrical activities in neuron during certain discharge period. Results mean that multiple modes and coherence resonance can be observed by changing the size of time-delay or the noise intensity, which could be associated with memory effect and self-adaption in neurons.

A Spatio-Temporal Autowave Model of Shanghai Territory Development

A spatio-temporal model of megacity development that treats the megacity as an active medium is presented. From our point of view, it is advisable to consider the process of urban ecosystem development from the standpoint of the theory of autowave self-organization in active media. According to this concept, the urban ecosystem is considered as interacting with each other’s natural and anthropogenic subsystems with significant heterogeneity of areas affected by human intervention and urban geobiocoenoses. The model is based on the general principles of active medium dynamics; therefore, it is universal for any object to be considered an active medium. The only difference when using the model to predict the development of urban ecosystems in countries with different socio-economic and political prerequisites is the variety of parameters included in the model, i.e., the activation parameter, the autowave process inhibitors, and the characteristic scales of the activator and inhibitor. The model was tested on the example of Moscow expansion in the period of 1952–1968 and showed good agreement with the map data. By means of the model, a prediction of Shanghai and surrounding territory development until 2030 was made.

Isoflurane Modulates Hippocampal Cornu Ammonis Pyramidal Neuron Excitability by Inhibition of Both Transient and Persistent Sodium Currents in Mice

BACKGROUND

Volatile anesthetics inhibit presynaptic voltage-gated sodium channels to reduce neurotransmitter release, but their effects on excitatory neuron excitability by sodium current inhibition are unclear. The authors hypothesized that inhibition of transient and persistent neuronal sodium currents by the volatile anesthetic isoflurane contributes to reduced hippocampal pyramidal neuron excitability.

METHODS

Whole-cell patch-clamp recordings of sodium currents of hippocampal cornu ammonis pyramidal neurons were performed in acute mouse brain slices. The actions of isoflurane on both transient and persistent sodium currents were analyzed at clinically relevant concentrations of isoflurane.

RESULTS

The median inhibitory concentration of isoflurane for inhibition of transient sodium currents was 1.0 ± 0.3 mM (~3.7 minimum alveolar concentration [MAC]) from a physiologic holding potential of -70 mV. Currents from a hyperpolarized holding potential of -120 mV were minimally inhibited (median inhibitory concentration = 3.6 ± 0.7 mM, ~13.3 MAC). Isoflurane (0.55 mM; ~2 MAC) shifted the voltage-dependence of steady-state inactivation by -6.5 ± 1.0 mV (n = 11, P < 0.0001), but did not affect the voltage-dependence of activation. Isoflurane increased the time constant for sodium channel recovery from 7.5 ± 0.6 to 12.7 ± 1.3 ms (n = 13, P < 0.001). Isoflurane also reduced persistent sodium current density (median inhibitory concentration = 0.4 ± 0.1 mM, ~1.5 MAC) and resurgent currents. Isoflurane (0.55 mM; ~2 MAC) reduced action potential amplitude, and hyperpolarized resting membrane potential from -54.6 ± 2.3 to -58.7 ± 2.1 mV (n = 16, P = 0.001).

CONCLUSIONS

Isoflurane at clinically relevant concentrations inhibits both transient and persistent sodium currents in hippocampal cornu ammonis pyramidal neurons. These mechanisms may contribute to reductions in both hippocampal neuron excitability and synaptic neurotransmission.

Coding Capacity of Purkinje Cells With Different Schemes of Morphological Reduction

The brain as a neuronal system has very complex structures with a large diversity of neuronal types. The most basic complexity is seen from the structure of neuronal morphology, which usually has a complex tree-like structure with dendritic spines distributed in branches. To simulate a large-scale network with spiking neurons, the simple point neuron, such as the integrate-and-fire neuron, is often used. However, recent experimental evidence suggests that the computational ability of a single neuron is largely enhanced by its morphological structure, in particular, by various types of dendritic dynamics. As the morphology reduction of detailed biophysical models is a classic question in systems neuroscience, much effort has been taken to simulate a neuron with a few compartments to include the interaction between the soma and dendritic spines. Yet, novel reduction methods are still needed to deal with the complex dendritic tree. Here, using 10 individual Purkinje cells of the cerebellum from three species of guinea-pig, mouse and rat, we consider four types of reduction methods and study their effects on the coding capacity of Purkinje cells in terms of firing rate, timing coding, spiking pattern, and modulated firing under different stimulation protocols. We found that there is a variation of reduction performance depending on individual cells and species, however, all reduction methods can preserve, to some degree, firing activity of the full model of Purkinje cell. Therefore, when stimulating large-scale network of neurons, one has to choose a proper type of reduced neuronal model depending on the questions addressed. Among these reduction schemes, Branch method, that preserves the geometrical volume of neurons, can achieve the best balance among different performance measures of accuracy, simplification, and computational efficiency, and reproduce various phenomena shown in the full morphology model of Purkinje cells. Altogether, these results suggest that the Branch reduction scheme seems to provide a general guideline for reducing complex morphology into a few compartments without the loss of basic characteristics of the firing properties of neurons.

In vivo electrophysiological recordings of the effects of antidepressant drugs

Antidepressant drugs are a standard biological treatment for various neuropsychiatric disorders, yet relatively little is known about their electrophysiologic and synaptic effects on mood systems that set moment-to-moment emotional tone. In vivo electrical recording of local field potentials (LFPs) and single neuron spiking has been crucial for elucidating important details of neural processing and control in many other systems, and yet electrical approaches have not been broadly applied to the actions of antidepressants on mood-related circuits. Here we review the literature encompassing electrophysiologic effects of antidepressants in animals, including studies that examine older drugs, and extending to more recently synthesized novel compounds, as well as rapidly acting antidepressants. The existing studies on neuromodulator-based drugs have focused on recording in the brainstem nuclei, with much less known about their effects on prefrontal or sensory cortex. Studies on neuromodulatory drugs have moreover focused on single unit firing patterns with less emphasis on LFPs, whereas the rapidly acting antidepressant literature shows the opposite trend. In a synthesis of this information, we hypothesize that all classes of antidepressants could have common final effects on limbic circuitry. Whereas NMDA receptor blockade may induce a high powered gamma oscillatory state via direct and fast alteration of glutamatergic systems in mood-related circuits, neuromodulatory antidepressants may induce similar effects over slower timescales, corresponding with the timecourse of response in patients, while resetting synaptic excitatory versus inhibitory signaling to a normal level. Thus, gamma signaling may provide a biomarker (or “neural readout”) of the therapeutic effects of all classes of antidepressants.

Optically Stimulated Artificial Synapse Based on Layered Black Phosphorus

The translation of biological synapses onto a hardware platform is an important step toward the realization of brain-inspired electronics. However, to mimic biological synapses, devices till-date continue to rely on the need for simultaneously altering the polarity of an applied electric field or the output of these devices is photonic instead of an electrical synapse. As the next big step toward practical realization of optogenetics inspired circuits that exhibit fidelity and flexibility of biological synapses, optically-stimulated synaptic devices without a need to apply polarity-altering electric field are needed. Utilizing a unique photoresponse in black phosphorus (BP), here reported is an all-optical pathway to emulate excitatory and inhibitory action potentials by exploiting oxidation-related defects. These optical synapses are capable of imitating key neural functions such as psychological learning and forgetting, spatiotemporally correlated dynamic logic and Hebbian spike-time dependent plasticity. These functionalities are also demonstrated on a flexible platform suitable for wearable electronics. Such low-power consuming devices are highly attractive for deployment in neuromorphic architectures. The manifestation of cognition and spatiotemporal processing solely through optical stimuli provides an incredibly simple and powerful platform to emulate sophisticated neural functionalities such as associative sensory data processing and decision making.

Excitable dynamics of Ras triggers spontaneous symmetry breaking of PIP3 signaling in motile cells

Spontaneous cell movement is underpinned by an asymmetric distribution of signaling molecules including small G proteins and phosphoinositides on the cell membrane. However, the molecular network necessary for spontaneous symmetry breaking has not been fully elucidated. Here, we report that, in Dictyostelium discoideum, the spatiotemporal dynamics of GTP bound Ras (Ras-GTP) breaks the symmetry due its intrinsic excitability even in the absence of extracellular spatial cues and downstream signaling activities. A stochastic excitation of local and transient Ras activation induced phosphatidylinositol (3,4,5)-trisphosphate (PIP3) accumulation via direct interaction with Phosphoinositide 3-kinase (PI3K), causing tightly coupled traveling waves that propagated along the membrane. Comprehensive phase analysis of the waves of Ras-GTP and PIP3 metabolism-related molecules revealed the network structure of the excitable system including positive-feedback regulation of Ras-GTP by the downstream PIP3. A mathematical model reconstituted a series of the observed symmetry-breaking phenomena, illustrating the essential involvement of Ras excitability in the cellular decision-making process.