Guang-Jun Zhang

Presynaptic NR2B-Containing NMDA Autoreceptors Mediate Glutamatergic Synaptic Transmission in the Rat Visual Cortex

N-methyl-D-aspartate (NMDA) receptors (NMDA-Rs) have different modulatory effects on excitatory synaptic transmission depending on the receptor subtypes involved. The present study investigated the subunit composition of the presynaptic NMDA-Rs in layer II/III pyramidal neurons of the rat visual cortex. We recorded evoked a-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor-mediated excitatory postsynaptic currents (eEPSCs) using whole-cell voltage clamp with the open-channel NMDA receptor (NMDA-R) blocker, (+)-5-Methyl-10,11-dihydro-5H-dibenzo(a,d)cyclohepten-5,10-imine hydrogen maleate (MK-801), in the recording pipette. We found that the paired-pulse ratio (PPR) by two successive stimuli with inter-pulse intervals of 50 ms was significantly increased by D-APV, a selective NMDA-R antagonist. Using a specific antagonist for NR2B-NMDA-Rs, (alphaR,betaS)-alpha-(4-hydroxyphenyl)-beta-methyl-4-(phenylmethyl)-1-piperidinepropanol hydrochloride (Ro 25-6981), instead of d-2-amino-5-phosphonovalerate (D-APV), we found that the PPR of eEPSCs was also significantly increased. Moreover, Zn(2+), an NR2A-NMDA-R antagonist, did not influence on the PPR. These results suggest that presynaptic NR2B-containing NMDA-Rs are located in layer II/III pyramidal neurons of the rat visual cortex, and that presynaptic NR2B-containing NMDA autoreceptors but not NR2A-containing NMDA autoreceptors mediate glutamate release in the rat visual cortex. Moreover, these findings may be clinically relevant to schizophrenia, where enhancing NMDA-R function is considered to be a promising strategy for treatment of the disease.

Involvement of synaptic NR2B-containing NMDA receptors in long-term depression induction in the young rat visual cortex in vitro.

Activation of N-methyl-D-aspartate receptors (NMDARs) has been implicated in various forms of synaptic plasticity depending on the receptor subtypes involved. However, the contribution of NR2A and NR2B subunits in the induction of long-term depression (LTD) of excitatory postsynaptic currents (EPSCs) in layer II/III pyramidal neurons of the young rat visual cortex remains unclear. The present study used whole-cell patch-clamp recordings in vitro to investigate the role of NR2A- and NR2B-containing NMDARs in the induction of LTD in visual cortical slices from 12- to 15-day old rats. We found that LTD was readily induced in layer II/III pyramidal neurons of the rat visual cortex with 10-min 1-Hz stimulation paired with postsynaptic depolarization. D-APV, a selective NMDAR antagonist, blocked the induction of LTD. Moreover, the selective NR2B-containing NMDAR antagonists (Ro 25-6981 and ifenprodil) also prevented the induction of LTD. However, Zn2+, a voltage-independent NR2A-containing NMDAR antagonist, displayed no influence on the induction of LTD. These results suggest that the induction of LTD in layer II/III pyramidal neurons of the young rat visual cortex is NMDAR-dependent and requires NR2B-containing NMDARs, not NR2A-containing NMDARs.

Generalized Hybrid Dislocated Function Projective Synchronization of Chaotic Systems with Time Delay

In order to improve the security of secure communication, a novel generalized hybrid dislocated function projective synchronization (GHDFPS) was proposed and GHDFPS of time delay chaotic systems with uncertain parameters were researched in this paper. Due to time delay, the chaotic system can produce multiple positive Lyapunov exponential; this characteristic can enhance security in secure communications noticeably. Based on Lyapunove stability theory and modified hybrid feedback control method, the modified hybrid feedback controller and the parameter updating laws were designed for the GHDFPS between the two time delay chaotic systems with uncertain parameters. The feedback gain can be adjusted automatically according to the synchronization error values. Under the controller, generalized hybrid dislocated function projective synchronization of the two chaotic systems is achieved, and the uncertain parameters of response systems are identified. The chaotic item is added in the function scale factor. The chaotic item in the function scaling factor makes function scaling factor more complex and unpredictable. So this can enhance the features of indeterminism in secure communication. The time delay feedback Lorenz system as an example; by numerical simulations the effectiveness of the proposed method is demonstrated.

Globus pallidus neuron spike time series prediction based on local-region multi-step forecasting model

Add-weighted one-rank local-region multi-steps forecasting model (AOLMM) is adopted to predict the neuron spikes of MPTP monkey model of Parkinson¿s disease (PD).The AOLMM based on Takens embedding theory has been proved as effectively predict many chaotic systems and overcome some shortcomings like Large computational quantity and cumulative error of other chaotic prediction methods. Many previous studies have demonstrated the existence of certain neurons in the thalamus of PD patients especially in the Globus Pallidus(GP) is closely related with the pathogenesis of tremor. We observed that with appropriate embedding dimension and the proper maximum forecasting step, the AOLMM can well foretell the dynamical trend of the GP neuron spikes of the MPTP induced monkey model of PD. It indicates that AOLMM is powerful to help us understand the pathological mechanism of PD better and clear.

The Bifurcation Phenomenon of Fractional-Order Hindmarsh–Rose Neuronal Model with the Decrease of the Order

The bifurcation behavior of fractional-order Hindmarsh–Rose neuronal model (HR) is investigated with the gradual decrease of the order in this paper. The results show that the change of order can vary qualitatively bifurcation behavior of this system with I bifurcation parameter, and that the firing modes of the fractional-order model are more complex. When the order is less than the critical value, a novel periodic window will appear in the chaotic region and the periodic window display regularity of continuous and gradual enlargement with the decrease of order in a special range. Different obviously from the integer-order HR neuronal model, which with the increase of I the response of system will lead to chaos by the routine of period add bifurcation from period-4 to period-5 and period doubling bifurcation, this fractional-order model when the order is q = 0.7 will lead to chaos by the routine of two period add bifurcations from period-3 to period-4, period-4 to period-5 and period doubling bifurcation with the increase of I.

The chaotic characteristic and control of chaotic synchronization of nonlinear dynamic model of fractional-order

In this paper, for a three dimensions autonomous continuous nonlinear dynamical system, the chaotic characteristic and control of chaotic synchronization of model of fractional-order are researched by the theory of fractional-order calculus. The results show that the lowest order of which chaos can occur is 0.855 in this nonlinear dynamic model of fractional-order. Based on the theory of stability of the fractional-order dynamical system, the controller is designed, the control of chaotic synchronization of nonlinear dynamic model of fractional-order is implement and the chaotic synchronization is proved analytically. Finally, the validity of nonlinear controller designed in this paper is verified by numerical simulation using Adams-Bashforth-Moulton arithmetic.

Function projective synchronization and parameters identification of different hyperchaotic systems based on adaptive control

In this paper a novel three dimensions chaotic system with uncertain parameters and lorenz hyperchaotic system are as examples, the function projective synchronization and parameters identification of different hyperchaotic systems are researched. First, based on the Lyapunov theory of stability and adaptive control method, the adaptive nonlinear controller and adaptive identifying rule to uncertain parameter are designed logically. And by the controller and identifying rule, the function projective synchronization of different systems between three dimensions response system with uncertain parameter and drive system is realized. Second, the feasibility of the controller and identifying rule to uncertain parameter designed in this paper is analyzed theoretically, and the function projective synchronization and parameters identification are proved strictly theoretically. Finally, the theoretical results are verified by numerical simulation.

Coherence Resonance of Excitable Chaotic Spiking Hindmarsh-Rose Neuron Model

In contrast to the previous researches on coherence resonance (CR), which have dealt with CR of periodic spiking neuron model, in this paper the characteristic of CR of chaotic neuron model, is researched. Whether CR of chaotic excitable neuron model can occur typically is of particular importance in neurophysiology, where CR is positive effect on transmission of neural information because noise can make the unordered chaotic spiking series of neurons more ordered. Here we show, based on a physical analysis and numerical evidence, that CR in the chaotic region of system bifurcation parameter can occur in the Hind marsh-Rose (HR) neuron model. Different from the previous results about CR that the occurrence and characterisitc of CR are dependent on the distance between the bifurcation point and bifurcation parameter, the occurrence and characterisitc of CR in this case are independent on the value of bifurcation parameter. CR in this case, which is relevant to an experimental phenomenon observed in neural electro physiology experiment, can be expected to occur commonly in other chaotic spiking neuron model.

The dynamical mechanism of a kind of noise-induced complete chaotic synchronization of two non-coupled periodically driven FitzHugh-Nagumo neuron models and strange non-chaotic attractor

The dynamical mechanism of noise-induced complete chaotic synchronization of two uncoupled periodically driven FitzHugh-Nagumo (FHN) neuron models is initially researched in this paper. Here we show: based on a nonlinear dynamical analysis and numerical evidence, that under the perturbation of weak noise strange non-chaotic attractor (SNA) can be induced to appear in FHN neuron model, which is formed through transitions among chaotic attractor, periodic attractor and chaotic saddle in two sides of boundary crisis point of system respectively. When SNA appears the maximum Lyapunov exponent of the attractor is non-positive and there is thus no sensitive dependence on initial conditions. The two FHN neuron models are identical but there is slight difference between their initial states. After the attractors of the two FHN neuron models become strange non-chaotic attractors from strange chaotic attractors under the appropriate noise, the responses of the two systems, which are no sensitive dependence on initial conditions, are complete synchronous. The dynamical mechanism of noise-induced complete chaotic synchronization of two non-coupled FHN neuron models is related to the strange non-chaotic attractor induced to appear by noise.

Strange Non-chaotic Attractors in Noisy FitzHugh-Nagumo Neuron Model

Strange Non-chaotic attractors in noisy FHN neuron model periodically driven are researched in this paper. Here we show, based on a nonlinear dynamical analysis and numerical evidence, that under the perturbation of weak noise strange non-chaotic attractor can be induced in FHN neuron model. And the mechanism of strange non-chaotic attractor is related to transitions among chaotic attractor, periodic-3 attractor and chaotic saddle in two sides of crisis point of system respectively.