动态突触、神经耦合与时间延迟对神经元发放的影响

神经元膜电位的受激发放在神经系统的信息传递中起着重要作用.基于一个受动态突触刺激的突触后神经元发放模型,采用数值模拟和傅里叶变换分析的方法研究了动态突触、神经耦合与时间延迟对突触后神经元发放的影响.结果发现:突触前神经元发放频率与Hodgkin-Huxley神经元的固有频率发生共振决定了突触后神经元发放的难易,特定频率范围内的电流刺激有利于神经元激发,动态突触输出的随机突触电流中这些电流刺激所占的比率在很大程度上影响了突触后神经元的发放次数;将突触后神经元换成神经网络后,网络中神经元之间的耦合可以促进神经元的发放,耦合中的时间延迟可以增强这种促进作用,但是不会改变神经耦合对神经元发放的促进模式.     

Transient dynamics in a small ensemble of synaptically coupled Morris-Lecar neurons

We propose a new model of synaptic transmission on the basis of a FitzHugh–Nagumo system with nonlinear recovery. It is shown that the Morrice–Lecar neuron ensemble formed by such couplings shows various structurally stable regimes of transient dynamics in the form of sequential transitions between various metastable oscillatory states of the system.     

A model of a neuron with oscillatory activity below the excitation threshold

We propose a dynamic model of a neuron with spontaneous periodic oscillations below the excitation threshold. Such neurons, in particular, play an important role in the problem of coordination of motions by the brain specifying the universal rhythm of muscular contractions. The model is constructed on the basis of the known model dynamic systems and is described by a system of fourth-order differential equations. A good qualitative agreement between the model dynamics and experimental data for the actual neurons is obtained. This work was presented at the Summer Workshop “Dynamic Days” (Nizhny Novgorod, June 30–July 2, 1998). Lobachevsky State University of Nizhny Novgorod, Russia. Translated from Izvestiya Vysshikh Uchebnykh Zavedenii, Radiofizika, Vol. 41, No. 12, pp. 1623–1635, December, 1998.     

Stochastic resonance between limit cycles. Spring pendulum in a thermostat

The effect of white noise on phase synchronization is studied numerically for a classical model of a spring pendulum with a multiple ratio of the frequencies of small oscillations (Vitt-Gorelik model). It is shown that in the model investigated a Fermi resonance regime occurs for a system in a thermostat. A new type of nonlinear dynamics is found — stochastic resonance between limit cycles. Pis’ma Zh. éksp. Teor. Fiz. 69, No. 8, 585–589 (25 April 1999)     

Neural signal transduction aided by noise in multisynaptic excitatory and inhibitory pathways with saturation

We study the stochastic resonance phenomenon in saturating dynamical models of neural signal transduction, at the synaptic stage, wherein the noise in multipathways enhances the processing of neuronal information integrated by excitatory and inhibitory synaptic currents. For an excitatory synaptic pathway, the additive intervention of an inhibitory pathway reduces the stochastic resonance effect. However, as the number of synaptic pathways increases, the signal transduction is greatly improved for parallel multipathways that feature both excitation and inhibition. The obtained results lead us to the realization that the collective property of inhibitory synapses assists neural signal transmission, and a parallel array of neurons can enhance their responses to multiple synaptic currents by adjusting the contributions of excitatory and inhibitory currents.     

Noise induced intercellular propagation of calcium waves

In this paper, we investigate the spatiotemporal dynamics of a bidirectional coupled chain of cells, in which a cell is subjected to an external noise. Noisy oscillations of calcium (Ca²⁺), that is, a bursting-like phenomenon induced by noise with fluctuations in the baseline values of calcium, are induced in the first cell and propagated along the chain with noise suppression. This phenomenon of noise suppression is further investigated by computing the normalized fluctuation of pulse durations. It is therefore found that the noise induced coherence resonance phenomenon occurs at the cellular level. Coherence biresonance behaviour appears in the transmission of noise induced oscillations at appropriate noise intensity or noise coupling (for low noise intensity) and the information flow in each cell can be simultaneously optimized at the optimal value of noise or coupling.     

Development of synaptic connectivity onto interneurons in stratum radiatum in the CA1 region of the rat hippocampus

Background  

The impact of a given presynaptic neuron on the firing probability of the postsynaptic neuron critically depends on the number of functional release sites that connect the two neurons. One way of determining the average functional synaptic connectivity onto a postsynaptic neuron is to compare the amplitudes of action potential dependent spontaneous synaptic currents with the amplitude of the synaptic currents that are independent of action potentials ("minis"). With this method it has been found that average synaptic connectivity between glutamatergic CA3 and CA1 pyramidal cells increases from single connections in the neonatal rat, to multiple connections in the young adult rat. On the other hand, γ-aminobutyric acid (GABA)ergic interneurons form multiple connections onto CA1 pyramidal cells already in the neonatal rat, and the degree of multiple GABAergic connectivity is preserved into adulthood. In the present study, we have examined the development of glutamate and GABA connectivity onto GABAergic CA1 stratum radiatum interneurons in the hippocampal slice, and compared this to the connectivity onto CA1 pyramidal neurons.     

Effects of synaptic noise and filtering on the frequency response of spiking neurons

Noise can have a significant impact on the response dynamics of a nonlinear system. For neurons, the primary source of noise comes from background synaptic input activity. If this is approximated as white noise, the amplitude of the modulation of the firing rate in response to an input current oscillating at frequency omega decreases as 1/square root[omega] and lags the input by 45 degrees in phase. However, if filtering due to realistic synaptic dynamics is included, the firing rate is modulated by a finite amount even in the limit omega-->infinity and the phase lag is eliminated. Thus, through its effect on noise inputs, realistic synaptic dynamics can ensure unlagged neuronal responses to high-frequency inputs.     

Bifurcations of oscillations of the membrane potential and the use of mappings for modeling electrically coupled neurons

In this paper, we present the results of a qualitative analysis of a generalized system of three differential equations that represent the neuron model. The main nontrivial bifurcation sets leading to the appearance of complex motions, i.e., bursts, are given. A two-dimensional mapping that models the flows generated by this system, which is considered to be the simplest model of a neuron, is proposed. The chaotic dynamics of diffusely coupled neurons is studied using the coupled mappings. This work was presented at the Summer Workshop “Dynamic Days” (Nizhny Novgorod, June 30–July 2, 1998). Lobachevsky State Univesity of Nizhny Novgorod, Russia. Translated from Izvestiya Vysshikh Uchebnykh Zavedenii, Radiofizika, Vol. 41, No. 12, pp. 1572–1580, December, 1998.     

Role of synaptic filtering on the firing response of simple model neurons

During active states of the brain neurons process their afferent currents with an effective membrane time constant much shorter than its value at rest. This fact, together with the existence of several synaptic time scales, determines to which aspects of the input the neuron responds best. Here we present a solution to the response of a leaky integrate-and-fire neuron with synaptic filters when long synaptic times are present, and predict the firing rate for all values of the synaptic time constant. We also discuss under which conditions this neuron becomes a coincidence detector.     

Dynamics of a FitzHugh-Nagumo system subjected to autocorrelated noise

We analyze the dynamics of the FitzHugh-Nagumo (FHN) model in the presence of colored noise and a periodic signal. Two cases are considered: (i) the dynamics of the membrane potential is affected by the noise, (ii) the slow dynamics of the recovery variable is subject to noise. We investigate the role of the colored noise on the neuron dynamics by the mean response time (MRT) of the neuron. We find meaningful modifications of the resonant activation (RA) and noise enhanced stability (NES) phenomena due to the correlation time of the noise. For strongly correlated noise we observe suppression of NES effect and persistence of RA phenomenon, with an efficiency enhancement of the neuronal response. Finally we show that the self-correlation of the colored noise causes a reduction of the effective noise intensity, which appears as a rescaling of the fluctuations affecting the FHN system.     

The spike timing precision of FitzHugh--Nagumo neuron network coupled by gap junctions

It has been proved recently that the spike timing can play an important role in information transmission, so in this paper we develop a network with N-unit FitzHugh--Nagumo neurons coupled by gap junctions and discuss the dependence of the spike timing precision on synaptic coupling strength, the noise intensity and the size of the neuron ensemble. The calculated results show that the spike timing precision decreases as the noise intensity increases; and the ensemble spike timing precision increases with coupling strength increasing. The electric synapse coupling has a more important effect on the spike timing precision than the chemical synapse coupling.     

Mechanism of the differentiation of neural responses to excitatory input signals

A dynamical mechanism of the generation of qualitatively different neural responses to typical excitatory stimuli such as an applied current or AMPA and NMDA synaptic currents has been presented. The mechanism is based on a nonlinearity simulating the calcium-dependent potassium current. It has been shown with the FitzHugh-Nagumo equation that, in the presence of such a nonlinearity, only the NMDA synaptic current can strongly increase the frequency of self-sustained oscillations, whereas other stimuli suppress neural activity.     

Multimode fabry-perot laser: Number of relaxation frequencies

We study analytically the Tang, Statz, and deMars equations describing a solid-state Fabry-Perot laser to determine how many relaxation oscillations it displays. When the modes have equal gains, the number of relaxation oscillations varies between zero and the mode number, depending on the laser parameters. In particular, a large number of modes or a relatively large pumping rate leads to the elimination of all relaxation oscillations except one, thereby simplifying the noise spectrum. These results are generalized to include unequal modal gains such as might result from the Lorentzian gain profile. Institute of Applied Physics, Russian Academy of Sciences, Nizhny Novgorod, Russia; Universite Libre de Bruxelles, Bruxelles, Belgium. Published from Izvestiya Vysshikh Uchebnykh Zavedenii, Radiofizika, Vol. 40, Nos. 1–2, pp. 161–175, January–February, 1997.     

Josephson dynamics for coupled polariton modes under the atom–field interaction in the cavity

We consider a new approach to the problem of Bose–Einstein condensation (BEC) of polaritons for atom–field interaction under the strong coupling regime in the cavity. We investigate the dynamics of two macroscopically populated polariton modes corresponding to the upper and lower branch energy states coupled via Kerr-like nonlinearity of atomic medium. We found out the dispersion relations for new type of collective excitations in the system under consideration. Various temporal regimes like linear (nonlinear) Josephson transition and/or Rabi oscillations, macroscopic quantum self-trapping (MQST) dynamics for population imbalance of polariton modes are predicted. We also examine the switching properties for time-averaged population imbalance depending on initial conditions, effective nonlinear parameter of atomic medium and kinetic energy of low-branch polaritons. PACS 03.75.Lm; 71.36.+c; 42.50.Fx     

Noise-induced hypersensitivity to weak alternating signals

An example of the helpful role of noise in information transmission processes is the well-known phenomenon of stochastic resonance. This letter examines another such example — parametric-noise-induced giant amplification of ultraweak signals in a system with on-off intermittency. Pis’ma Zh. éksp. Teor. Fiz. 67, No. 8, 592–596 (25 April 1998)     

Resonant propagation of spike trains in delay-coupled neural subthreshold oscillators

We study the propagation of spike trains through one-dimensional chains of coupled neurons exhibiting subthreshold oscillations. We consider the existence of a synaptic delay that provides a time scale in addition to the ones given by the periods of the input train and of the subthreshold oscillations. These three time scales affect the evolution of the phase of the neural oscillators, preparing the state of the postsynaptic neuron for the presynaptic input, which can trigger a suprathreshold response according to that phase. In the case of pulsed chemical coupling, results from two coupled neurons help infer the success of the propagation through a larger chain. This situation exhibits a resonant behavior with respect to the period of the input spike train, by which successful propagation arises for certain values of the input period, irrespective of the delay. In the presence of additional electrical coupling via gap junctions, the synaptic delay starts to play a relevant role, and a second resonance appears with respect to that time scale.     

Inverse stochastic resonance induced by synaptic background activity with unreliable synapses

Inverse stochastic resonance (ISR) is a recently pronounced phenomenon that is the minimum occurrence in mean firing rate of a rhythmically firing neuron as noise level varies. Here, by using a realistic modeling approach for the noise, we investigate the ISR with concrete biophysical mechanisms. It is shown that mean firing rate of a single neuron subjected to synaptic bombardment exhibits a minimum as the spike transmission probability varies. We also demonstrate that the occurrence of ISR strongly depends on the synaptic input regime, where it is most prominent in the balanced state of excitatory and inhibitory inputs.