Equalization of synaptic efficacy by synchronous neural activity

It is commonly believed that spike timings of a postsynaptic neuron tend to follow those of the presynaptic neuron. Such orthodromic firing may, however, cause a conflict with the functional integrity of complex neuronal networks due to asymmetric temporal Hebbian plasticity. We argue that reversed spike timing in a synapse is a typical phenomenon in the cortex, which has a stabilizing effect on the neuronal network structure. We further demonstrate how the firing causality in a synapse is perturbed by synchronous neural activity and how the equilibrium property of spike-timing dependent plasticity is determined principally by the degree of synchronization. Remarkably, even noise-induced activity and synchrony of neurons can result in equalization of synaptic efficacy.     

Filtered interspike interval encoding by class II neurons

Dynamics of class II neurons, firing frequencies of which are strongly regulated by the inherent neuronal property, have been extensively studied since the formulation of the Hodgkin–Huxley model in 1952. However, how class II neurons process stimulus information and what kind of external information and internal structure firing patterns of neurons represent are vaguely understood in contrast to firing rate coding by class I neurons. Here we show that the FitzHugh–Nagumo class II neuron simultaneously filters inputs based on the input frequency and represent the signal strength by interspike intervals. In this sense, the class II neuron works as an AM processor that passes the information on the carrier and on the temporal waveform of signals.     

Adaptive Regulation for Noise-Aided Weak Signal Detection in Spiking Neurons

Spiking neurons usually change their membrane properties, especially ion channel activity, during adaptation or synaptic modification to improve information processing and transmission. Using simple and biophysically realistic models, our analyses reveal that activity-dependent regulation of membrane properties contributes to sensitivity adaptation that improves the neuron ability of detecting sub-threshold signals in the presence of background noises. The improvement is achieved by regulating the conductance of ion channels on the membrane, dependent on the neuron firing activity.     

Tomographic-probability description of solitons in Bose-Einstein condensates

The influence of a weight-dependent spike-timing dependent plasticity (STDP) rule on the temporal evolution and equilibrium state of a certain synapse is investigated. We show that under certain conditions, a spike-induced rate-learning scheme could be achieved. Through studying the situation when a single Hodgkin-Huxley neuron is driven by a large ensemble of input neurons, we find that synchronized firing of a sub population of input neurons may be important to information processing in the nervous system. Using simulations, we show that the temporal structure of the spike trains of these synchronized input neurons can be transmitted reliably; further, synapses from these neurons will increase stably due to the STDP rule and this may provide a mechanism for learning and information storage in biologically plausible network models. Received 12 September 2002 / Received in final form 12 December 2002 Published online 14 February 2003 RID="a" ID="a"e-mail: huang_yue@netease.com     

Rhythm Synchronization of Coupled Neurons with Temporal Coding Scheme

Encoding information by firing patterns is one of the basic neural functions, and synchronization is important collective behaviour of a group of coupled neurons. Taking account of two schemes for encoding information （that is, rate coding and temporal coding）, rhythm synchronization of coupled neurons is studied. There are two types of rhythm synchronization of neurons： spike and burst synchronizations. Firstly, it is shown that the spike synchronization is equivalent to the phase synchronization for coupled neurons. Secondly, the similarity function of the slow variables of neurons, which have relevant to the bursting process, is proposed to judge the burst synchronization. It is also found that the burst synchronization can be achieved more easily than the spike synchronization, whatever the firing patterns of the neurons are. Hence the temporal encoding scheme, which is closely related to both the spike and burst synchronizations, is more comprehensive than the rate coding scheme in essence.     

非线性物理破解神经编码机制

信息在神经系统中以脉冲点序列的形式传输和处理，神经系统如何通过脉冲点序列对所表达的信息进行编码，一直是一个谜．人们曾普遍认为，神经可能通过发放率（单位时间发放脉冲出现的次数）对信息编码，也有人猜测，神经可通过脉冲点序列的时序编码．由于神经发放的随机性，这就使得任何编码机制都面临着被表达信息的确定性与表达该信息的信号的随机性的矛盾．通过神经非线性随机动力学模型，文章作者发现，神经点序列的发放率对点序列的时序信息传输的影响，揭示了神经点序列时序信息在神经非线性传输中的随机共振特征．由此预期，并进一步通过听觉心理物理实验观察到，在一定条件下噪声对听觉的增强作用．从而通过非线性物理揭示了听觉时序编码机制的存在以及在时序编码中随机性噪声的积极作用．     

Time-varying irregularities in multiple trial spike data

The measurement of neuronal firing rates has been a standard methodology for characterizing properties of neurons. The peri-stimulus time histogram (PSTH) is primarily used for visualizing changes of firing rates in relation to an external stimulus or an event. On the other hand, modulation of other statistics such as distribution and patterns of interspike intervals can be an important index for analysis of neuronal response and may provide insights into the neuronal codes. In particular, it is desirable to visualize the temporal modulation not only of the firing rates but also of the other statistics. In this study, we propose an analysis method for measuring irregularities in multiple trial spike data. The method calculates a local measure by extracting a short segment of data within a predefined time bin and connecting them each other. We compare the different data extraction methods in Poisson and gamma processes and show that our proposed method is effective for estimating the statistics of the irregular spike data.     

Bumps, breathers, and waves in a neural network with spike frequency adaptation

We introduce a continuum model of neural tissue that includes the effects of spike frequency adaptation (SFA). The basic model is an integral equation for synaptic activity that depends upon nonlocal network connectivity, synaptic response, and the firing rate of a single neuron. We consider a phenomenological model of SFA via a simple state-dependent threshold firing rate function. As without SFA, Mexican-hat connectivity allows for the existence of spatially localized states (bumps). Importantly recent Evans function techniques are used to show that bumps may destabilize leading to the emergence of breathers and traveling waves. Moreover, a similar analysis for traveling pulses leads to the conditions necessary to observe a stable traveling breather. Simulations confirm our theoretical predictions and illustrate the rich behavior of this model.     

Firing activities in a fractional-order Hindmarsh-Rose neuron with multistable memristor as autapse

Considering the fact that memristors have the characteristics similar to biological synapses, a fractional-order multistable memristor is proposed in this paper. It is verified that the fractional-order memristor has multiple local active regions and multiple stable hysteresis loops, and the influence of fractional-order on its nonvolatility is also revealed. Then by considering the fractional-order memristor as an autapse of Hindmarsh-Rose (HR) neuron model, a fractional-order memristive neuron model is developed. The effects of the initial value, external excitation current, coupling strength and fractional-order on the firing behavior are discussed by time series, phase diagram, Lyapunov exponent and inter spike interval (ISI) bifurcation diagram. Three coexisting firing patterns, including irregular asymptotically periodic (A-periodic) bursting, A-periodic bursting and chaotic bursting, dependent on the memristor initial values, are observed. It is also revealed that the fractional-order can not only induce the transition of firing patterns, but also change the firing frequency of the neuron. Finally, a neuron circuit with variable fractional-order is designed to verify the numerical simulations.     

Dynamical response of a neuron–astrocyte coupling system under electromagnetic induction and external stimulation

Previous studies have observed that electromagnetic induction can seriously affect the electrophysiological activity of the nervous system. Considering the role of astrocytes in regulating neural firing, we studied a simple neuron–astrocyte coupled system under electromagnetic induction in response to different types of external stimulation. Both the duration and intensity of the external stimulus can induce different modes of electrical activity in this system, and thus the neuronal firing patterns can be subtly controlled. When the external stimulation ceases, the neuron will continue to fire for a long time and then reset to its resting state. In this study, "delay" is defined as the delayed time from the firing state to the resting state, and it is highly sensitive to changes in the duration or intensity of the external stimulus. Meanwhile, the self-similarity embodied in the aforementioned sensitivity can be quantified by fractal dimension. Moreover, a hysteresis loop of calcium activity in the astrocyte is observed in the specific interval of the external stimulus when the stimulus duration is extended to infinity, since astrocytic calcium or neuron electrical activity in the resting state or during periodic oscillation depends on the initial state. Finally, the regulating effect of electromagnetic induction in this system is considered. It is clarified that the occurrence of "delay" depends purely on the existence of electromagnetic induction. This model can reveal the dynamic characteristics of the neuron–astrocyte coupling system with magnetic induction under external stimulation. These results can provide some insights into the effects of electromagnetic induction and stimulation on neuronal activity.