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Female sexual receptivity is essential for reproduction of a species. Neuropeptides play the main role in regulating female receptivity. However, whether neuropeptides regulate female sexual receptivity during the neurodevelopment is unknown. Here, we found the peptide hormone prothoracicotropic hormone (PTTH), which belongs to the insect PG (prothoracic gland) axis, negatively regulated virgin female receptivity through ecdysone during neurodevelopment in Drosophila melanogaster . We identified PTTH neurons as doublesex-positive neurons, they regulated virgin female receptivity before the metamorphosis during the third-instar larval stage. PTTH deletion resulted in the increased EcR-A expression in the whole newly formed prepupae. Furthermore, the ecdysone receptor EcR-A in pC1 neurons positively regulated virgin female receptivity during metamorphosis. The decreased EcR-A in pC1 neurons induced abnormal morphological development of pC1 neurons without changing neural activity. Among all subtypes of pC1 neurons, the function of EcR-A in pC1b neurons was necessary for virgin female copulation rate. These suggested that the changes of synaptic connections between pC1b and other neurons decreased female copulation rate. Moreover, female receptivity significantly decreased when the expression of PTTH receptor Torso was reduced in pC1 neurons. This suggested that PTTH not only regulates female receptivity through ecdysone but also through affecting female receptivity associated neurons directly. The PG axis has similar functional strategy as the hypothalamic-pituitary-gonadal axis in mammals to trigger the juvenile-adult transition. Our work suggests a general mechanism underlying which the neurodevelopment during maturation regulates female sexual receptivity., Competing Interests: JL, CN, YL, BD, BW, KS, RW, RF, CZ No competing interests declared, (© 2023, Li, Ning, Liu et al.)

Spinal cord injury (SCI) usually induces profound microvascular dysfunction. It disrupts the integrity of the blood-spinal cord barrier (BSCB), which could trigger a cascade of secondary pathological events that manifest as neuronal apoptosis and axonal demyelination. These events can further lead to irreversible neurological impairments. Thus, reducing the permeability of the BSCB and maintaining its substructural integrity are essential to promote neuronal survival following SCI. Tetramethylpyrazine (TMP) has emerged as a potential protective agent for treating the BSCB after SCI. However, its therapeutic potential is hindered by challenges in the administration route and suboptimal bioavailability, leading to attenuated clinical outcomes. To address this challenge, traditional Chinese medicine, TMP, was used in this study to construct a drug-loaded electroconductive hydrogel for synergistic treatment of SCI. A conductive hydrogel combined with TMP demonstrates good electrical and mechanical properties as well as superior biocompatibility. Furthermore, it also facilitates sustained local release of TMP at the implantation site. Furthermore, the TMP-loaded electroconductive hydrogel could suppress oxidative stress responses, thereby diminishing endothelial cell apoptosis and the breakdown of tight junction proteins. This concerted action repairs BSCB integrity. Concurrently, myelin-associated axons and neurons are protected against death, which meaningfully restore neurological functions post spinal cord injury. Hence, these findings indicate that combining the electroconductive hydrogel with TMP presents a promising avenue for potentiating drug efficacy and synergistic repair following SCI.

Dissection of neural circuitry underlying behaviors is a central theme in neurobiology. We have previously proposed the concept of chemoconnectome (CCT) to cover the entire chemical transmission between neurons and target cells in an organism and created tools for studying it (CCTomics) by targeting all genes related to the CCT in Drosophila . Here we have created lines targeting the CCT in a conditional manner after modifying GFP RNA interference, Flp-out, and CRISPR/Cas9 technologies. All three strategies have been validated to be highly effective, with the best using chromatin-peptide fused Cas9 variants and scaffold optimized sgRNAs. As a proof of principle, we conducted a comprehensive intersection analysis of CCT genes expression profiles in the clock neurons, uncovering 43 CCT genes present in clock neurons. Specific elimination of each from clock neurons revealed that loss of the neuropeptide CNMa in two posterior dorsal clock neurons (DN1ps) or its receptor (CNMaR) caused advanced morning activity, indicating a suppressive role of CNMa-CNMaR on morning anticipation, opposite to the promoting role of PDF-PDFR on morning anticipation. These results demonstrate the effectiveness of conditional CCTomics and its tools created here and establish an antagonistic relationship between CNMa-CNMaR and PDF-PDFR signaling in regulating morning anticipation., Competing Interests: RM, JY, BD, XD, YD, SD, WZ, YR No competing interests declared, (© 2023, Mao et al.)

Spike-based perception brings up a new research idea in the field of neuromorphic engineering. A high-performance biologically inspired flexible spiking neural network (SNN) architecture provides a novel method for the exploration of perception mechanisms and the development of neuromorphic computing systems . In this article, we present a biological-inspired spike-based SNN perception digital system that can realize robust perception. The system employs a fully paralleled pipeline scheme to improve the performance and accelerate the processing of feature extraction. An auditory perception system prototype is realized on ten Intel Cyclone field-programmable gate arrays, which can reach the maximum frequency of 107.28 MHz and the maximum throughput of 5364 Mbps. Our design also achieves the power of 5. 148 W/system and energy efficiency of 845.85 μJ. Our auditory perception implementation is also proved to have superior robustness compared with other SNN systems. We use TIMIT digit speech in noise in accuracy testing. Result shows that it achieves up to 85.75% speech recognition accuracy under obvious noise conditions (signal-to-noise ratio of 20 dB) and maintain small accuracy attenuation with the decline of the signal-to-noise ratio. The overall performance of our proposed system outperforms the state-of-the-art perception system on SNN., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The propagation of slowly-varying firing rates has been proved significant for the development of the central nervous system. Recent reports have shown that the membrane passive properties of dendrites play a key role in the computation of the single neuron, which is of great importance to the function of neural networks. However, it is still unclear how dendritic passive properties affect the ability of cortical networks to propagate slowly-varying spiking activity. Here, we use two-compartment biophysical models to construct multilayered feedforward neural networks (FFNs) to investigate how dendritic passive properties affect the propagation of the slow-varying inputs. In the two-compartment biophysical models, one compartment represents apical dendrites, and the other compartment describes the soma plus the axon initial segment. Area proportion occupied by somatic compartment and coupling conductance between dendritic and somatic compartments are abstracted to capture the dendritic passive properties. A time-varying signal is injected into the first layer of the FFNs and the fidelity of the signal during propagation is used to qualify the ability of the FFN to transmit wave-like signals. Numerical results reveal an optimal value of coupling conductance between dendritic and somatic compartments to maximize the fidelity of the initial spiking activity. An increase of the dendritic area enhances the initial firing rate of neurons in the first layer by increasing the response of neurons to slow-varying wave-like input, resulting in a delay of attenuation of the firing rate, thus promoting the transmission of signals in FFN. Using a mean-field approach, we examine that changes in area proportion occupied by somatic compartment and coupling conductance between dendritic and somatic compartment affect the signal propagation ability of the FFN by adjusting the input-output transform of a single neuron. With the participation of external noise, a wide range of initial firing rates maintains a unique representation during propagation, which ensures the reliable transmission of slow-varying signals in FFNs. These findings are helpful to understand how passive properties of dendrites participate in the propagation of slowly varying signals in the cerebellum., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

This paper presents a field-programmable gate array (FPGA) implementation of an auditory system, which is biologically inspired and has the advantages of robustness and anti-noise ability. We propose an FPGA implementation of an eleven-channel hierarchical spiking neuron network (SNN) model, which has a sparsely connected architecture with low power consumption. According to the mechanism of the auditory pathway in human brain, spiking trains generated by the cochlea are analyzed in the hierarchical SNN, and the specific word can be identified by a Bayesian classifier. Modified leaky integrate-and-fire (LIF) model is used to realize the hierarchical SNN, which achieves both high efficiency and low hardware consumption. The hierarchical SNN implemented on FPGA enables the auditory system to be operated at high speed and can be interfaced and applied with external machines and sensors. A set of speech from different speakers mixed with noise are used as input to test the performance our system, and the experimental results show that the system can classify words in a biologically plausible way with the presence of noise. The method of our system is flexible and the system can be modified into desirable scale. These confirm that the proposed biologically plausible auditory system provides a better method for on-chip speech recognition. Compare to the state-of-the-art, our auditory system achieves a higher speed with a maximum frequency of 65.03 MHz and a lower energy consumption of 276.83 μJ for a single operation. It can be applied in the field of brain-computer interface and intelligent robots.

Tumour necrosis factor-α (TNF-α), a crucial cytokine, has various homeostatic and pathogenic bioactivities. The aim of this study was to assess the neuroprotective effect of ketamine against TNF-α-induced motor dysfunction and neuronal necroptosis in male C57BL/6J mice in vivo and HT-22 cell lines in vitro. The behavioural testing results of the present study indicate that ketamine ameliorated TNF-α-induced neurological dysfunction. Moreover, immunohistochemical staining results showed that TNF-α-induced brain dysfunction was caused by necroptosis and microglial activation, which could be attenuated by ketamine pre-treatment inhibiting reactive oxygen species production and mixed lineage kinase domain-like phosphorylation in hippocampal neurons. Therefore, we concluded that ketamine may have neuroprotective effects as a potent inhibitor of necroptosis, which provides a new theoretical and experimental basis for the application of ketamine in TNF-α-induced necroptosis-associated diseases., (© 2021 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.)

Electrical stimulation can shape oscillations in brain activity. However, the mechanism of how periodic electrical stimulation modulates brain oscillations by time-delayed neural networks is poorly understood at present. To address this question, we investigate the effects of periodic stimulations on the oscillations generated via a time-delayed neural network. We specifically study the effect of unipolar and asymmetric bidirectional pulse stimulations by altering amplitude and frequency in a systematic manner. Our findings suggest that electrical stimulations play a central role in altering oscillations in the time-delayed neural network and that these alterations are strongly dependent on the stimulus frequency. We observe that the time-delayed neural network responds differently as the stimulation frequency is altered, as manifested by changes in resonance, entrainment, non-linear oscillation, or oscillation suppression. The results also indicate that the network presents similar response activities with increasing stimulus frequency under different excitation-inhibition ratios. Collectively, our findings pave the way for exploring the potential mechanism underlying the frequency-dependent modulation of network activity via electrical stimulations and provide new insights into possible electrical stimulation therapies to the neurological and psychological disorders in clinical practice.

Aggressive behavior is regulated by various neuromodulators such as neuropeptides and biogenic amines. Here we found that the neuropeptide Drosulfakinin (Dsk ) modulates aggression in Drosophila melanogaster . Knock-out of Dsk or Dsk receptor CCKLR-17D1 reduced aggression. Activation and inactivation of Dsk-expressing neurons increased and decreased male aggressive behavior, respectively. Moreover, data from transsynaptic tracing, electrophysiology and behavioral epistasis reveal that Dsk-expressing neurons function downstream of a subset of P1 neurons ( P1 a -splitGAL4 ) to control fighting behavior. In addition, winners show increased calcium activity in Dsk-expressing neurons. Conditional overexpression of Dsk promotes social dominance, suggesting a positive correlation between Dsk signaling and winning effects. The mammalian ortholog CCK has been implicated in mammal aggression, thus our work suggests a conserved neuromodulatory system for the modulation of aggressive behavior., Competing Interests: FW, BD, NX, TW, YL, RW, KS, DL, YR, CZ No competing interests declared, (© 2020, Wu et al.)

Recent studies have demonstrated the effectiveness of supervised learning in spiking neural networks (SNNs). A trainable SNN provides a valuable tool not only for engineering applications but also for theoretical neuroscience studies. Here, we propose a modified SpikeProp learning algorithm, which ensures better learning stability for SNNs and provides more diverse network structures and coding schemes. Specifically, we designed a spike gradient threshold rule to solve the well-known gradient exploding problem in SNN training. In addition, regulation rules on firing rates and connection weights are proposed to control the network activity during training. Based on these rules, biologically realistic features such as lateral connections, complex synaptic dynamics, and sparse activities are included in the network to facilitate neural computation. We demonstrate the versatility of this framework by implementing three well-known temporal codes for different types of cognitive tasks, namely, handwritten digit recognition, spatial coordinate transformation, and motor sequence generation. Several important features observed in experimental studies, such as selective activity, excitatory-inhibitory balance, and weak pairwise correlation, emerged in the trained model. This agreement between experimental and computational results further confirmed the importance of these features in neural function. This work provides a new framework, in which various neural behaviors can be modeled and the underlying computational mechanisms can be studied.

Multicompartment emulation is an essential step to enhance the biological realism of neuromorphic systems and to further understand the computational power of neurons. In this paper, we present a hardware efficient, scalable, and real-time computing strategy for the implementation of large-scale biologically meaningful neural networks with one million multi-compartment neurons (CMNs). The hardware platform uses four Altera Stratix III field-programmable gate arrays, and both the cellular and the network levels are considered, which provides an efficient implementation of a large-scale spiking neural network with biophysically plausible dynamics. At the cellular level, a cost-efficient multi-CMN model is presented, which can reproduce the detailed neuronal dynamics with representative neuronal morphology. A set of efficient neuromorphic techniques for single-CMN implementation are presented with all the hardware cost of memory and multiplier resources removed and with hardware performance of computational speed enhanced by 56.59% in comparison with the classical digital implementation method. At the network level, a scalable network-on-chip (NoC) architecture is proposed with a novel routing algorithm to enhance the NoC performance including throughput and computational latency, leading to higher computational efficiency and capability in comparison with state-of-the-art projects. The experimental results demonstrate that the proposed work can provide an efficient model and architecture for large-scale biologically meaningful networks, while the hardware synthesis results demonstrate low area utilization and high computational speed that supports the scalability of the approach.

Our understanding of the molecular mechanisms underlying sleep homeostasis is limited. We have taken a systematic approach to study neural signaling by the transmitter 5-hydroxytryptamine (5-HT) in drosophila. We have generated knockout and knockin lines for Trh , the 5-HT synthesizing enzyme and all five 5-HT receptors, making it possible for us to determine their expression patterns and to investigate their functional roles. Loss of the Trh , 5HT1a or 5HT2b gene decreased sleep time whereas loss of the Trh or 5HT2b gene diminished sleep rebound after sleep deprivation. 5HT2b expression in a small subset of, probably a single pair of, neurons in the dorsal fan-shaped body (dFB) is functionally essential: elimination of the 5HT2b gene from these neurons led to loss of sleep homeostasis. Genetic ablation of 5HT2b neurons in the dFB decreased sleep and impaired sleep homeostasis. Our results have shown that serotonergic signaling in specific neurons is required for the regulation of sleep homeostasis.

Previous studies have proven that paired immunoglobulin-like receptor B (PirB) plays a crucial suppressant role in neurite outgrowth and neuronal plasticity after central nervous system injury. However, the role of PirB in neuronal survival after cerebral ischemic injury and its mechanisms remains unclear. In the present study, the role of PirB is investigated in the survival and apoptosis of cerebral cortical neurons in cultured primary after oxygen and glucose deprivation (OGD)-induced injury. The results have shown that rebarbative PirB exacerbates early neuron apoptosis and survival. PirB gene silencing remarkably decreases early apoptosis and promotes neuronal survival after OGD. The expression of bcl-2 markedly increased and the expression of bax significantly decreased in PirB RNAi-treated neurons, as compared with the control- and control RNAi-treated ones. Further, phosphorylated TrkB and mTOR levels are significantly downregulated in the damaged neurons. However, the PirB silencing markedly upregulates phosphorylated TrkB and mTOR levels in the neurons after the OGD. Taken together, the overexpression of PirB inhibits the neuronal survival through increased neuron apoptosis. Importantly, the inhibition of the phosphorylation of TrkB and mTOR may be one of its mechanisms.

Real-time estimation of dynamical characteristics of thalamocortical cells, such as dynamics of ion channels and membrane potentials, is useful and essential in the study of the thalamus in Parkinsonian state. However, measuring the dynamical properties of ion channels is extremely challenging experimentally and even impossible in clinical applications. This paper presents and evaluates a real-time estimation system for thalamocortical hidden properties. For the sake of efficiency, we use a field programmable gate array for strictly hardware-based computation and algorithm optimization. In the proposed system, the FPGA-based unscented Kalman filter is implemented into a conductance-based TC neuron model. Since the complexity of TC neuron model restrains its hardware implementation in parallel structure, a cost efficient model is proposed to reduce the resource cost while retaining the relevant ionic dynamics. Experimental results demonstrate the real-time capability to estimate thalamocortical hidden properties with high precision under both normal and Parkinsonian states. While it is applied to estimate the hidden properties of the thalamus and explore the mechanism of the Parkinsonian state, the proposed method can be useful in the dynamic clamp technique of the electrophysiological experiments, the neural control engineering and brain-machine interface studies.

A significant feature of Parkinson's disease (PD) is the inability of the thalamus to respond faithfully to sensorimotor information from the cerebral cortex. This may be the result of abnormal oscillations in the basal ganglia (BG). Deep brain stimulation (DBS) is regarded as an effective method to modulate these pathological brain rhythmic activities. However, the selection of DBS parameters is challenging because the mechanism is not well understood. This work proposes the design of a closed-loop control strategy to automatically adjust the parameters of a DBS waveform based on a computational model. By estimating the synaptic input from BG to the thalamic neuron model as feedback variable, we designed and compared various control algorithms to counteract the effects of pathological oscillatory inputs. We then obtained optimal DBS parameters to modulate the tremor-predominant Parkinsonian state. We showed that even a simple proportional controller provides higher fidelity of thalamic relay of sensorimotor information and lower energy expenditure, as compared with classical open-loop DBS. Integral action further enhances DBS performance. Additionally, a positive bias voltage further improves the relay ability of the thalamus with decreased stimulation energy expenditure. These findings were conducive to the development of a more effective DBS to further improve the treatment of the PD.

Mathematical models provide a mathematical description of neuron activity, which can better understand and quantify neural computations and corresponding biophysical mechanisms evoked by stimulus. In this paper, based on the output spike train evoked by the acupuncture mechanical stimulus, we present two different levels of models to describe the input-output system to achieve the reconstruction of neuronal input. The reconstruction process is divided into two steps: First, considering the neuronal spiking event as a Gamma stochastic process. The scale parameter and the shape parameter of Gamma process are, respectively, defined as two spiking characteristics, which are estimated by a state-space method. Then, leaky integrate-and-fire (LIF) model is used to mimic the response system and the estimated spiking characteristics are transformed into two temporal input parameters of LIF model, through two conversion formulas. We test this reconstruction method by three different groups of simulation data. All three groups of estimates reconstruct input parameters with fairly high accuracy. We then use this reconstruction method to estimate the non-measurable acupuncture input parameters. Results show that under three different frequencies of acupuncture stimulus conditions, estimated input parameters have an obvious difference. The higher the frequency of the acupuncture stimulus is, the higher the accuracy of reconstruction is.

Dynamic spike threshold plays a critical role in neuronal input-output relations. In many neurons, the threshold potential depends on the rate of membrane potential depolarization (dV/dt) preceding a spike. There are two basic classes of neural excitability, i.e., Type I and Type II, according to input-output properties. Although the dynamical and biophysical basis of their spike initiation has been established, the spike threshold dynamic for each cell type has not been well described. Here, we use a biophysical model to investigate how spike threshold depends on dV/dt in two types of neuron. It is observed that Type II spike threshold is more depolarized and more sensitive to dV/dt than Type I. With phase plane analysis, we show that each threshold dynamic arises from the different separatrix and K+ current kinetics. By analyzing subthreshold properties of membrane currents, we find the activation of hyperpolarizing current prior to spike initiation is a major factor that regulates the threshold dynamics. The outward K+ current in Type I neuron does not activate at the perithresholds, which makes its spike threshold insensitive to dV/dt. The Type II K+ current activates prior to spike initiation and there is a large net hyperpolarizing current at the perithresholds, which results in a depolarized threshold as well as a pronounced threshold dynamic. These predictions are further attested in several other functionally equivalent cases of neural excitability. Our study provides a fundamental description about how intrinsic biophysical properties contribute to the threshold dynamics in Type I and Type II neurons, which could decipher their significant functions in neural coding.

Spike-frequency adaptation has been shown to play an important role in neural coding. Based on a reduced two-compartment model, here we investigate how two common adaptation currents, i.e., voltage-sensitive potassium current (I(M)) and calcium-sensitive potassium current (I(AHP)), modulate neuronal responses to extracellular electric fields. It is shown that two adaptation mechanisms lead to distinct effects on the dynamical behavior of the neuron to electric fields. These effects depend on a neuronal morphological parameter that characterizes the ratio of soma area to total membrane area and internal coupling conductance. In the case of I(AHP) current, changing the morphological parameter switches spike initiation dynamics between saddle-node on invariant cycle bifurcation and supercritical Hopf bifurcation, whereas it only switches between subcritical and supercritical Hopf bifurcations for I(M) current. Unlike the morphological parameter, internal coupling conductance is unable to alter the bifurcation scenario for both adaptation currents. We also find that the electric field threshold for triggering neuronal steady-state firing is determined by two parameters, especially by the morphological parameter. Furthermore, the neuron with I(AHP) current generates mixed-mode oscillations through the canard phenomenon for some small values of the morphological parameter. All these results suggest that morphological properties play a critical role in field-induced effects on neuronal dynamics, which could qualitatively alter the outcome of adaptation by modulating internal current between soma and dendrite. The findings are readily testable in experiments, which could help to reveal the mechanisms underlying how the neuron responds to electric field stimulus.

Reliable signal propagation across distributed brain areas is an essential requirement for cognitive function, and it has been investigated extensively in computational studies where feed-forward network (FFN) is taken as a generic model. But it is still unclear how distinct local network states, which are intrinsically generated by synaptic interactions within each layer, would affect the ability of FFN to transmit information. Here we investigate the impact of such network states on propagating transient synchrony (synfire) and firing rate by a combination of numerical simulations and analytical approach. Specifically, local network dynamics is attributed to the competition between excitatory and inhibitory neurons within each layer. Our results show that concomitant with different local network states, the performance of signal propagation differs dramatically. For both synfire propagation and firing rate propagation, there exists an optimal local excitability state, respectively, that optimizes the performance of signal propagation. Furthermore, we find that long-range connections strongly change the dependence of spiking activity propagation on local network state and propose that these two factors work jointly to determine information transmission across distributed networks. Finally, a simple mean field approach that bridges response properties of long-range connectivity and local subnetworks is utilized to reveal the underlying mechanism.

To investigate how extracellular electric field modulates neuron activity, a reduced two-compartment neuron model in the presence of electric field is introduced in this study. Depending on neuronal geometric and internal coupling parameters, the behaviors of the model have been studied extensively. The neuron model can exist in quiescent state or repetitive spiking state in response to electric field stimulus. Negative electric field mainly acts as inhibitory stimulus to the neuron, positive weak electric field could modulate spiking frequency and spike timing when the neuron is already active, and positive electric fields with sufficient intensity could directly trigger neuronal spiking in the absence of other stimulations. By bifurcation analysis, it is observed that there is saddle-node on invariant circle bifurcation, supercritical Hopf bifurcation and subcritical Hopf bifurcation appearing in the obtained two parameter bifurcation diagrams. The bifurcation structures and electric field thresholds for triggering neuron firing are determined by neuronal geometric and coupling parameters. The model predicts that the neurons with a nonsymmetric morphology between soma and dendrite, are more sensitive to electric field stimulus than those with the spherical structure. These findings suggest that neuronal geometric features play a crucial role in electric field effects on the polarization of neuronal compartments. Moreover, by determining the electric field threshold of our biophysical model, we could accurately distinguish between suprathreshold and subthreshold electric fields. Our study highlights the effects of extracellular electric field on neuronal activity from the biophysical modeling point of view. These insights into the dynamical mechanism of electric field may contribute to the investigation and development of electromagnetic therapies, and the model in our study could be further extended to a neuronal network in which the effects of electric fields on network activity may be investigated.

The FitzHugh-Nagumo neuron model subject to a biharmonical external force with two different frequencies is used to investigate the underlying mechanism of vibrational resonance in an excitable system in which the time scales between the fast and slow variables are separated clearly. The theoretical analysis is given based on the approximation approach and the concept of the phase-locking ratio instead of the amplification ratio widely used in the investigation of vibrational resonance in bistable oscillators. The result shows that the high-frequency subthreshold force with the frequency close to the natural frequency of the neuron model in the resting state can induce the change of potential shape of the model near the bifurcation point. This gives rise to the different phase-locking modes of the neuron responses to the same low-frequency subthreshold input. It is also shown that besides the parameters of the high-frequency force such as amplitude and frequency, the bifurcation parameter of the model can affect the vibrational resonance notably. Finally, the numerical results have verified the theoretical analysis.

Based on a reduced two-compartment model, the dynamical and biophysical mechanism underlying the spike initiation of the neuron to extracellular electric fields is investigated in this paper. With stability and phase plane analysis, we first investigate in detail the dynamical properties of neuronal spike initiation induced by geometric parameter and internal coupling conductance. The geometric parameter is the ratio between soma area and total membrane area, which describes the proportion of area occupied by somatic chamber. It is found that varying it could qualitatively alter the bifurcation structures of equilibrium as well as neuronal phase portraits, which remain unchanged when varying internal coupling conductance. By analyzing the activating properties of somatic membrane currents at subthreshold potentials, we explore the relevant biophysical basis of spike initiation dynamics induced by these two parameters. It is observed that increasing geometric parameter could greatly decrease the intensity of the internal current flowing from soma to dendrite, which switches spike initiation dynamics from Hopf bifurcation to SNIC bifurcation; increasing internal coupling conductance could lead to the increase of this outward internal current, whereas the increasing range is so small that it could not qualitatively alter the spike initiation dynamics. These results highlight that neuronal geometric parameter is a crucial factor in determining the spike initiation dynamics to electric fields. The finding is useful to interpret the functional significance of neuronal biophysical properties in their encoding dynamics, which could contribute to uncovering how neuron encodes electric field signals.

Electromagnetic pulse (EMP) causes central nervous system damage and neurobehavioral disorders, and sevoflurane protects the brain from ischemic injury. We investigated the effects of sevoflurane on EMP-induced brain injury. Rats were exposed to EMP and immediately treated with sevoflurane. The protective effects of sevoflurane were assessed by Nissl staining, Fluoro-Jade C staining and electron microscopy. The neurobehavioral effects were assessed using the open-field test and the Morris water maze. Finally, primary cerebral cortical neurons were exposed to EMP and incubated with different concentration of sevoflurane. The cellular viability, lactate dehydrogenase (LDH) release, superoxide dismutase (SOD) activity and malondialdehyde (MDA) level were assayed. TUNEL staining was performed, and the expression of apoptotic markers was determined. The cerebral cortexes of EMP-exposed rats presented neuronal abnormalities. Sevoflurane alleviated these effects, as well as the learning and memory deficits caused by EMP exposure. In vitro, cell viability was reduced and LDH release was increased after EMP exposure; treatment with sevoflurane ameliorated these effects. Additionally, sevoflurane increased SOD activity, decreased MDA levels and alleviated neuronal apoptosis by regulating the expression of cleaved caspase-3, Bax and Bcl-2. These findings demonstrate that Sevoflurane conferred neuroprotective effects against EMP radiation-induced brain damage by inhibiting neuronal oxidative stress and apoptosis.

A combined method composing of the unscented Kalman filter (UKF) and the synchronization-based method is proposed for estimating electrophysiological variables and parameters of a thalamocortical (TC) neuron model, which is commonly used for studying Parkinson's disease for its relay role of connecting the basal ganglia and the cortex. In this work, we take into account the condition when only the time series of action potential with heavy noise are available. Numerical results demonstrate that not only this method can estimate model parameters from the extracted time series of action potential successfully but also the effect of its estimation is much better than the only use of the UKF or synchronization-based method, with a higher accuracy and a better robustness against noise, especially under the severe noise conditions. Considering the rather important role of TC neuron in the normal and pathological brain functions, the exploration of the method to estimate the critical parameters could have important implications for the study of its nonlinear dynamics and further treatment of Parkinson's disease.

In a previous study, we generated two monoclonal antibodies (mAbs) in mice, aNogoA-N and aNogo-66 mAb, which were raised against recombinant N-terminal fragments of rat NogoA and Nogo-66, respectively. When compared with the commercial rabbit anti-rat NogoA polyclonal antibody (pAb), which can specifically recognise NogoA, the two mAbs were also specific for the NogoA antigen in immunofluorescence histochemical (IHC) staining and Western blot (WB) analysis. Serial truncations of NogoA covering the N-terminal region of NogoA (aa 570-691) and Nogo-66 (aa 1026-1091) were expressed in E. coli. The epitopes recognised by aNogoA-N and aNogo-66 are located in the aa 634-668 and aa 1026-1055 regions of NogoA, respectively. Both mAbs remarkably enhanced the axon growth and branching of cultured hippocampal neurons in vitro. These results suggest that the antibodies that bind to aa 634-668 and aa 1026-1055 of NogoA may have stimulatory effects on axon growth and branching. Additionally, the two mAbs that we generated are specific for NogoA and significantly block NogoA function. In conclusion, two sites in NogoA located within aa 634-668 and aa 1026-1055 are recognised by our two antibodies and are novel and potentially promising targets for repair after central nervous system (CNS) injury.

To provide insights into the modulation of neuronal activity by extremely low-frequency (ELF) magnetic field (MF), we present a conductance-based neuron model and introduce ELF sinusoidal MF as an additive voltage input. By analyzing spike times and spiking frequency, it is observed that neuron with distinct spiking patterns exhibits different response properties in the presence of MF exposure. For tonic spiking neuron, the perturbations of MF exposure on spike times is maximized at the harmonics of neuronal intrinsic spiking frequency, while it is maximized at the harmonics of bursting frequency for burst spiking neuron. As MF intensity increases, the perturbations also increase. Compared with tonic spiking, bursting dynamics are less sensitive to the perturbations of ELF MF exposure. Further, ELF MF exposure is more prone to perturb neuronal spike times relative to spiking frequency. Our finding suggests that the resonance may be one of the neural mechanisms underlying the modulatory effects of the low-intensity ELF MFs on neuronal activities. The results highlight the impacts of ELF MFs exposure on neuronal activity from the single cell level, and demonstrate various factors including ELF MF properties and neuronal spiking characteristics could determine the outcome of exposure. These insights into the mechanism of MF exposure may be relevant for the design of multi-intensity magnetic stimulus protocols, and may even contribute to the interpretation of MF effects on the central nervous systems.

The combined effects of the information transmission delay and the ratio of the electrical and chemical synapses on the synchronization transitions in the hybrid modular neuronal network are investigated in this paper. Numerical results show that the synchronization of neuron activities can be either promoted or destroyed as the information transmission delay increases, irrespective of the probability of electrical synapses in the hybrid-synaptic network. Interestingly, when the number of the electrical synapses exceeds a certain level, further increasing its proportion can obviously enhance the spatiotemporal synchronization transitions. Moreover, the coupling strength has a significant effect on the synchronization transition. The dominated type of the synapse always has a more profound effect on the emergency of the synchronous behaviors. Furthermore, the results of the modular neuronal network structures demonstrate that excessive partitioning of the modular network may result in the dramatic detriment of neuronal synchronization. Considering that information transmission delays are inevitable in intra- and inter-neuronal networks communication, the obtained results may have important implications for the exploration of the synchronization mechanism underlying several neural system diseases such as Parkinson's Disease.

A novel closed-loop control strategy is proposed to control Parkinsonian state based on a computational model. By modeling thalamocortical relay neurons under external electric field, a slow variable feedback control is applied to restore its relay functionality. Qualitative and quantitative analysis demonstrates the performance of feedback controller based on slow variable is more efficient compared with traditional feedback control based on fast variable. These findings point to the potential value of model-based design of feedback controllers for Parkinson's disease.

The effects of time delay and rewiring probability on stochastic resonance and spatiotemporal order in small-world neuronal networks are studied in this paper. Numerical results show that, irrespective of the pacemaker introduced to one single neuron or all neurons of the network, the phenomenon of stochastic resonance occurs. The time delay in the coupling process can either enhance or destroy stochastic resonance on small-world neuronal networks. In particular, appropriately tuned delays can induce multiple stochastic resonances, which appear intermittently at integer multiples of the oscillation period of the pacemaker. More importantly, it is found that the small-world topology can significantly affect the stochastic resonance on excitable neuronal networks. For small time delays, increasing the rewiring probability can largely enhance the efficiency of pacemaker-driven stochastic resonance. We argue that the time delay and the rewiring probability both play a key role in determining the ability of the small-world neuronal network to improve the noise-induced outreach of the localized subthreshold pacemaker.

In this paper, we proposed a new approach to estimate unknown parameters and topology of a neuronal network based on the adaptive synchronization control scheme. A virtual neuronal network is constructed as an observer to track the membrane potential of the corresponding neurons in the original network. When they achieve synchronization, the unknown parameters and topology of the original network are obtained. The method is applied to estimate the real-time status of the connection in the feedforward network and the neurotransmitter release probability of unreliable synapses is obtained by statistic computation. Numerical simulations are also performed to demonstrate the effectiveness of the proposed adaptive controller. The obtained results may have important implications in system identification in neural science.

We investigate the onset of chaotic phase synchronization of bursting oscillators in a modular neuronal network of small-world subnetworks. A transition to mutual phase synchronization takes place on the bursting time scale of coupled oscillators, while on the spiking time scale, they behave asynchronously. It is shown that this bursting synchronization transition can be induced not only by the variations of inter- and intra-coupling strengths but also by changing the probability of random links between different subnetworks. We also analyze the effect of external chaotic phase synchronization of bursting behavior in this clustered network by an external time-periodic signal applied to a single neuron. Simulation results demonstrate a frequency locking tongue in the driving parameter plane, where bursting synchronization is maintained, even with the external driving. The width of this synchronization region increases with the signal amplitude and the number of driven neurons but decreases rapidly with the network size. Considering that the synchronization of bursting neurons is thought to play a key role in some pathological conditions, the presented results could have important implications for the role of externally applied driving signal in controlling bursting activity in neuronal ensembles.

In this paper, we investigate the effect of a high-frequency driving on the dynamical response of excitable neuronal systems to a subthreshold low-frequency signal by numerical simulation. We demonstrate the occurrence of vibrational resonance in spatially extended neuronal networks. Different network topologies from single small-world networks to modular networks of small-world subnetworks are considered. It is shown that an optimal amplitude of high-frequency driving enhances the response of neuron populations to a low-frequency signal. This effect of vibrational resonance of neuronal systems depends extensively on the network structure and parameters, such as the coupling strength between neurons, network size, and rewiring probability of single small-world networks, as well as the number of links between different subnetworks and the number of subnetworks in the modular networks. All these parameters play a key role in determining the ability of the network to enhance the outreach of the localized subthreshold low-frequency signal. Considering that two-frequency signals are ubiquity in brain dynamics, we expect the presented results could have important implications for the weak signal detection and information propagation across neuronal systems.

We study the phenomenon of stochastic resonance on a modular neuronal network consisting of several small-world subnetworks with a subthreshold periodic pacemaker. Numerical results show that the correlation between the pacemaker frequency and the dynamical response of the network is resonantly dependent on the intensity of additive spatiotemporal noise. This effect of pacemaker-driven stochastic resonance of the system depends extensively on the local and the global network structure, such as the intra- and inter-coupling strengths, rewiring probability of individual small-world subnetwork, the number of links between different subnetworks, and the number of subnetworks. All these parameters play a key role in determining the ability of the network to enhance the noise-induced outreach of the localized subthreshold pacemaker, and only they bounded to a rather sharp interval of values warrant the emergence of the pronounced stochastic resonance phenomenon. Considering the rather important role of pacemakers in real-life, the presented results could have important implications for many biological processes that rely on an effective pacemaker for their proper functioning.

We investigate the chaotic phase synchronization in a system of coupled bursting neurons in small-world networks. A transition to mutual phase synchronization takes place on the bursting time scale of coupled oscillators, while on the spiking time scale, they behave asynchronously. It is shown that phase synchronization is largely facilitated by a large fraction of shortcuts, but saturates when it exceeds a critical value. We also study the external chaotic phase synchronization of bursting oscillators in the small-world network by a periodic driving signal applied to a single neuron. It is demonstrated that there exists an optimal small-world topology, resulting in the largest peak value of frequency locking interval in the parameter plane, where bursting synchronization is maintained, even with the external driving. The width of this interval increases with the driving amplitude, but decrease rapidly with the network size. We infer that the externally applied driving parameters outside the frequency locking region can effectively suppress pathologically synchronized rhythms of bursting neurons in the brain.

In this paper different topologies of populations of FitzHugh-Nagumo neurons have been introduce to investigate the effect of high-frequency driving on the response of neuron populations to a subthreshold low-frequency signal. We show that optimal amplitude of high-frequency driving enhances the response of neuron populations to a subthreshold low-frequency input and the optimal amplitude dependences on the connection among the neurons. By analyzing several kinds of topology (i.e., random and small world) different behaviors have been observed. Several topologies behave in an optimal way with respect to the range of low-frequency amplitude leading to an improvement in the stimulus response coherence, while others with respect to the maximum values of the performance index. However, the best results in terms of both the suitable amplitude of high-frequency driving and high stimulus response coherence have been obtained when the neurons have been connected in a small-world topology.

Electronic circuit analogues of biological membranes are presented, by which the passive channels are modeled as parallel and serial combinations of resistors (Ohmic conductors) and diffusors (negative non-Ohmic conductors), and the active channels of the one-way ion pumps are modeled as one-way inductors. We show that our simple analog models can reproduce known phenomena of membrane excitability such as the generation of action potentials and spike bursts.

Duchenne muscular dystrophy (DMD) results from null mutation of dystrophin, a membrane-associated structural protein that is expressed in skeletal muscle. Dystrophin deficiency causes muscle membrane lesions, muscle degeneration and eventually death in afflicted individuals. However, dystrophin deficiency also causes cognitive defects that are difficult to relate to the loss of dystrophin. We assayed neurogenesis in the dentate gyrus (DG) in the mdx mouse model of DMD, using bromodeoxyuridine incorporation as a marker of proliferation and NeuN expression as a marker of differentiation. Our findings show that dystrophin mutation disrupts adult neurogenesis by promoting cell proliferation in the DG and suppressing neuronal differentiation. Because loss of dystrophin from muscle results in the secondary loss of neuronal nitric oxide synthase (nNOS), and NO is able to modulate neurogenesis, we assayed whether the genetic restoration of nNOS to mdx muscles corrected defects in adult, hippocampal neurogenesis. Assays of NO in the sera of active mice showed significant reductions in NO caused by the dystrophin mutation. However, over-expression of nNOS in the muscles of mdx mice increased serum NO and normalized cell proliferation and neuronal differentiation in the DG. These findings indicate that muscle-derived NO regulates adult neurogenesis in the brain and loss of muscle nNOS may underlie defects in the central nervous system in DMD.

A method that combines the means of unscented Kalman filter (UKF) with the technique of synchronization-based parameter estimation is introduced for estimating unknown parameters of neuron when only a heavily noise-corrupted time series of active potential is given. Compared with other synchronization-based methods, this approach uses the state variables estimated by UKF instead of the measured data to drive the auxiliary system. The synchronization-based approach supplies a systematic and analytical procedure for estimating parameters from time series; however, it is only robust against weak noise of measurement, so the UKF is employed to estimate state variables which are used by the synchronization-based method to estimate all unknown parameters of neuron model. It is found out that the estimation accuracy of this combined method is much higher than only using UKF or synchronization-based method when the data of measurement were heavily noise corrupted.

The response of three coupled FitzHugh-Nagumo neurons, under high-frequency driving, to a subthreshold low-frequency signal is investigated. We show that an optimal amplitude of the high-frequency driving enhances the response of coupled excited neurons to a subthreshold low-frequency input, and the chemical synaptic coupling is more efficient than the well-known electrical coupling (gap junction), especially when the coupled neurons are near the canard regime, for local signal input, i.e., only one of the three neurons is subject to a low-frequency signal. The influence of additive noise and the interplay between vibrational and stochastic resonance are also analyzed.

As an important neuron model, the Morris-Lecar (ML) equations can exhibit classes I and II excitabilities with appropriate system parameters. In this paper, the effects of external DC electric field on the neuro-computational properties of ML model are investigated using bifurcation analysis. We obtain the bifurcation diagram in two dimensional parameter space of externally applied DC current and trans-membrane potential induced by external DC electric field. The bifurcation sets partition the two dimensional parameter space about the qualitatively different behaviors of the ML model. Thus the neuron's information encodes the stimulus information, and vice versa, which is significant in neural control. Furthermore, we identify the electric field as a key parameter to control the transitions among four different excitability and spiking properties.

An adaptive internal model control strategy is introduced into the robust complete synchronization of two gap-junction coupled FizHugh-Nagumo (FHN) neurons under uncertain heterogeneous disturbances which satisfies some general immersion condition. The synchronization problem can be converted into a robust stabilization problem of an augmented system consisting of the original given plants and an internal model. An adaptive law is employed against uncertain disturbances to make the estimate of internal model to converge to the ideal one. Following a proper state-feedback stabilizer is designed to guarantee the asymptotic stability of the resulting closed-loop system achieved for some appointed initial condition in the state space and for all possible values of the uncertain parameter vector. Finally, the simulation results demonstrate the validity of the proposed method.

In this paper, we got the implementation of the electronic neuron based on the Morris-Lecar model, and use the circuit to analysis the bifurcation of the Morris-Lecor model. Based on the results of the bifurcation analysis, we get bursting neurons by introducing a slow driving current. Furthermore, based on the implementation of a single electronic neuron, the electronic neurons are coupled through inhibiting chemical synapses which is one of the most important connections. Finally, we analyze behavior of the network of the electronic neurons coupled through the inhibiting chemical synapses.