感觉神经放电中的随机共振现象和噪声的作用

随机共振现象是非线性系统中普遍存在的自然现象 ,其中 ,噪声可以帮助检测弱信号而不是淹没弱信号。本文介绍了感觉神经放电活动中的随机共振现象和产生的机制 ,揭示了神经系统利用噪声检测弱信号的机制 ,并提出了随机共振在神经系统信息处理中的可能作用。     

线性整合放电模型的相干共振

研究了神经递质以随机点序列释放和电压门控离子通道噪声共同作用下,线性整合放电模型的相干共振现象。基于分形布朗运动和改进的欧拉方法,得到了神经元膜电压分布和神经元放电峰峰间隔的信噪比。结果表明,神经元放电的峰峰间隔是神经递质的达到强度、离子通道噪声强度的非单调函数。适当的神经递质到达强度和离子通道噪声强度使峰峰间隔的信噪比出现最大值,即出现相干共振现象。     

噪声对细胞体系检测弱激素信号影响的化学动力学模拟

该文通过构建细胞体系的介观随机模型,采用计算机模拟的方法,探讨了共存的内、外噪声对细胞体系检测弱激素信号的影响。结果表明,在外噪声存在时,内噪声强度的增加对弱激素信号的检测起消极作用;在内噪声存在时,有一个最佳的外噪声强度,使得体系对弱激素信号的检测能力最佳;在大多数高等动植物组织的细胞体积范围内,始终有内信号随机共振现象发生,且存在一个最佳的外噪声强度(2.0~3.0),使得细胞体系检测弱激素信号的能力最强。生物体系很可能已经学会自我调节到一个最佳的细胞体积,从而较好地利用外噪声的积极作用。该研究结果很可能对生命体系检测弱信号的机理研究具有一定的参考意义。     

持续与间断噪声前掩蔽条件下小鼠下丘神经元的不同反应模式

有关听中枢神经元纯音前掩蔽效应的神经表征已进行了大量研究,但是,噪声前掩蔽尤其是间断噪声前掩蔽效应的神经表征却鲜有报道。本研究观察了自由声场条件下,昆明小鼠下丘神经元在持续与间断噪声前掩蔽条件下对纯音探测声的反应。共记录到96个下丘神经元,测量了其中51个神经元在不同声刺激条件下的强度一放电率函数。结果显示,掩蔽声强度分布较广（探测声阈下21dB至阈上19dB之间）。在将近一半的神经元中,间断噪声的前掩蔽效应比持续噪声强（Ⅰ型,45．10％,P〈0．001）,但也有少数神经元其间断噪声的掩蔽效应较持续噪声的弱（Ⅲ型,17．65％,P〈0．001）,部分神经元无显著性差异（Ⅱ型,37．25％,P〉0．05）。无论Ⅰ型还是Ⅲ型神经元,持续噪声和间断噪声均在探测声强度较低时产生较强的抑制效应,随着探测声强度的升高,抑制效应逐渐降低（P〈0．001）;同时,持续噪声和间断噪声之间前掩蔽效应差异亦不复存在（P〉0．05）。此外,当掩蔽声由持续噪声换为间断噪声后,部分Ⅰ型神经元掩蔽时相的类型发生转变,其中最主要的转变为由前期抑制转变为均衡抑制（53．85％,7／13）。对下丘神经元声反应的时间域以及强度域,持续与间断噪声具有分化性前掩蔽效应,提示噪声前掩蔽并非简单的神经元发放压抑源,某些主动性神经调制机制可能参与了噪声条件下时相声信息的编码过程。     

生物钟体系中色噪音诱导的日夜节律振荡和内信号随机共振

利用脉孢菌生物钟体系,研究了色噪音对其进行诱导所产生的日夜节律振荡信号及其内信号随机共振的行为．结果表明,色噪音的相关时间对该体系内信号随机共振的强弱起较大的影响作用．当无外信号存在时,色噪音的相关时间对体系内信号随机共振强度起抑制的作用,且随相关时间的增大,抑制作用增强．当外信号加到体系中时,由于相关时间和外信号的协同作用,相关时间不仅对其内信号随机共振强度起抑制的作用,而且还影响内信号随机共振峰的数目,即随相关时间的增大,可使单峰随机共振变为随机双共振．存在最佳的外信号频率使体系的内信号随机共振强度得到最大的增强,而其他频率的外信号却起抑制作用．色内噪音和色外噪音相比,前者对该体系进行诱导所得的内信号随机共振强度比后者的更强,而且体系对前者更敏感．另外,存在极限的噪音强度使白噪音和色噪音对该体系内信号随机共振的影响差异得以消失．所得结果可为治疗生物钟紊乱综合症提供理论依据,同时可更好地理解其他节奏机理,如心脏搏动节奏、呼吸节奏以及荷尔蒙水平的波动节奏等．     

弱噪声对小鼠下丘神经元频率调谐的影响

为探讨弱噪声对小鼠 (MusmusculusKm)中脑下丘 (inferiorcolliculus ,IC)神经元声信号提取的影响 ,采用单位胞外记录方法 ,研究了加入弱白噪声 (强度相当于纯音阈强度下 5dB)前后神经元频率调谐曲线的变化。实验共记录到 10 4个下丘神经元 ,测量了 32个神经元的频率调谐曲线。结果显示 :①弱噪声条件下神经元的频率调谐曲线表现出 3种类型 ,即锐化 (34 4 % ,11/ 32 )、拓宽 (18 8% ,6 / 32 )和不受影响 (4 6 9% ,15 / 32 ) ,其中锐化呈现有意义的变化 ;②频率调谐受弱噪声锐化的神经元 ,其Q10 、Q3 0 平均分别增大 (34 4 2±17 0 4 ) % (P =0 0 2 6 ,n =11)和 (4 6 34± 2 2 88) % (P =0 0 0 9,n =7) ,且Q3 0 变化率大于Q10 ;③弱噪声对调谐曲线的高、低频边锐化度不一 ,神经元低频边的反转斜率基本不变 [由 0 16± 0 0 8变为 0 16± 0 0 7kHz/dB (P =0 94 7,n =7) ],而高频边明显下降 [由 0 5 2± 0 2 5下降为 0 2 6± 0 13kHz/dB ,平均减小 (4 3 81±2 4 0 6 ) % ,(P =0 0 4 6 ,n =7) ]。上述结果表明 ,弱噪声可锐化小鼠IC神经元频率调谐 ,并强化神经元的声信号高频分析能力     

弱噪声对下丘神经元声强敏感性的动态调制

为探讨复杂听环境下行为相关声信号提取的可能机制,研究了弱噪声对下丘(IC)神经元强度．放电率函数(RIF)的影响。实验在9只昆明小鼠(Musmusculus Km)上进行,在自由声场刺激条件下,分别记录短纯音刺激以及同步输出短纯音阂下5dB包络白噪声刺激时IC神经元的RIF,共获112个IC神经元,测量了其中44个神经元在加入噪声前(W／O)后(w)的RIF。以加入噪声前后RIF的声强动力学范围(DR)、斜率、以及不同声刺激强度的放电率抑制百分比变化为指标,比较分析发现：弱噪声对神经元发放率的影响呈三种类型,即抑制(39／44,88．6％)、易化(2／44,4．6％)和无影响(3／44,6．8％),但只有抑制性影响有显著性意义(P＜0．001,n=39);弱噪声对阂反应的抑制效应最强,并随纯音强度的增加而逐步减弱(P＜0．01301,n=39);此外,弱噪声的抑制作用还使大部分神经元的(31／39,79．5％)DR变窄(P＜0．01,,l=31)、RIF的斜率增加(P＜0．01,n=31)。上述结果提示,弱噪声参与下丘神经元声强敏感性的动态调制过程。这一观察为人们深入了解自然听环境中声信号提取的中枢机制提供了新认识。     

动态神经元的网络模型——Ⅲ.电路实现

在动态神经元网络数学模型的基础上,利用模拟有源器件与数学开关电路组成的硬件系统来达到模拟生物神经元的目的。模拟的结果表明：这个硬件具有神经元脉冲发放的动态过程,系统中每部分的输出信号分别对应于突触后电位、感受器电位、始段分级电位和轴突上脉冲发放等波形,与生物实验资料相似,是一个比计算机仿真更接近实际的连续模型,它将为小型神经元网络的动态特性分析提供了更直观,更可靠的手段。     

电突触电流在单个吸气性气管运动神经元中的选择性显示

在呼吸相关神经元或其它任何类型的神经元中,与生理性自发活动相对应的电突触电流（gap junction currents,GJCs）尚未在单个神经元中被记录到,因此电突触如何参与呼吸相关的或其它类型的生理性活动,目前所知甚少。在本研究中,我们假设GJCs可在电压钳记录条件下通过消除跨膜电化学梯度在单个神经元实现选择性记录,并在单个吸气性气管迷走神经节前神经元（inspiratory tracheal preganglionic vagal motor neurons,I-TPVMs）进行验证。结果显示,用这种方法在所有I-TPVMs中均记录到GJCs,且这些神经元的GJCs可被节律性中枢吸气活动所激活。此法可用于快速探测具自发活动的神经元网络中的GJCs。     

PCDHA基因簇及其影响神经发育的研究进展

原钙黏连素(PCDHs)家族属于Ca2+依赖的细胞黏着糖蛋白,在脑神经元网络搭建中扮演至关重要的角色.PCDHs家族在染色体上呈现簇状和非簇状分布,簇内众多可变外显子在神经元内随机表达,其丰富的蛋白变体组合锚定在神经元表面,作为特有信号"密码",识别并介导轴突或树突之间的连接.该文综述了近些年国内外的研究报道,阐述家族...     

Membrane Resonance and Stochastic Resonance Modulate Firing Patterns of Thalamocortical Neurons

We examined the interactions of subthreshold membrane resonance and stochastic resonance using whole-cell patch clamp recordings in thalamocortical neurons of rat brain slices, as well as with a Hodgkin-Huxley-type mathematical model of thalamocortical neurons. The neurons exhibited the subthreshold resonance when stimulated with small amplitude sine wave currents of varying frequency, and stochastic resonance when noise was added to sine wave inputs. Stochastic resonance was manifest as a maximum in signal-to-noise ratio of output response to subthreshold periodic input combined with noise. Stochastic resonance in conjunction with subthreshold resonance resulted in action potential patterns that showed frequency selectivity for periodic inputs. Stochastic resonance was maximal near subthreshold resonance frequency and a high noise level was required for detection of high frequency signals. We speculate that combined membrane and stochastic resonances have physiological utility in coupling synaptic activity to preferred firing frequency and in network synchronization under noise.     

Frequency sensitivity in Hodgkin–Huxley systems

 The frequency sensitivity of weak periodic signal detection has been studied via numerical simulations for both a single neuron and a neuronal network. The dependence of the critical amplitude of the signal upon its frequency and a resonance between the intrinsic oscillations of a neuron and the signal could account for the frequency sensitivity. In the presence of both a subthreshold periodic signal and noise, the signal-to-noise ratio (SNR) of the output of either a single neuron or a neuronal network present the typical characteristics of stochastic resonance. In particular, there exists a frequency-sensitive range of 30–100 Hz, and for signals with frequencies within this range the SNRs have large values. This implies that the system under consideration (a single neuron or a neuronal network) is more sensitive to the detection of periodic signals, and the frequency sensitivity may be of a functional significance to signal processing. Received: 26 October 1999 / Accepted in revised form: 25 July 2000     

Signal detection theory,detectability and stochastic resonance effects

 Stochastic resonance is a phenomenon in which the performance of certain non-linear detectors can be enhanced by the addition of appropriate levels of random noise. Signal detection theory offers a powerful tool for analysing this type of system, through an ability to separate detection processes into reception and classification, with the former generally being linear and the latter always non-linear. Through appropriate measures of signal detectability it is possible to decide whether a local improvement in detection via stochastic resonance occurs due to the non-linear effects of the classification process. In this case, improvement of detection through the addition of noise can never improve detection beyond that of a corresponding adaptive system. Signal detection and stochastic resonance is investigated in several integrate-and-fire neuron models. It is demonstrated that the stochastic resonance observed in spiking models is caused by non-linear properties of the spike-generation process itself. The true detectability of the signal, as seen by the receiver part of the spiking neuron (the integrator part), decreases monotonically with input noise level for all signal and noise intensities. Received: 3 April 2001 / Accepted in revised form: 8 March 2002     

Weak electric fields detectability in a noisy neural network

We investigate the detectability of weak electric field in a noisy neural network based on Izhikevich neuron model systematically. The neural network is composed of excitatory and inhibitory neurons with similar ratio as that in the mammalian neocortex, and the axonal conduction delays between neurons are also considered. It is found that the noise intensity can modulate the detectability of weak electric field. Stochastic resonance (SR) phenomenon induced by white noise is observed when the weak electric field is added to the network. It is interesting that SR almost disappeared when the connections between neurons are cancelled, suggesting the amplification effects of the neural coupling on the synchronization of neuronal spiking. Furthermore, the network parameters, such as the connection probability, the synaptic coupling strength, the scale of neuron population and the neuron heterogeneity, can also affect the detectability of the weak electric field. Finally, the model sensitivity is studied in detail, and results show that the neural network model has an optimal region for the detectability of weak electric field signal.     

STOCHASTIC RESONANCE MEASURED BY THEMUTUAL INFORMATION IN THE NEURONSTHAT TRANSMIT CHAOTIC SPIKE TRAINS

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STOCHASTIC RESONANCE MEASURED BY THEMUTUAL INFORMATION IN THE NEURONSTHAT TRANSMIT CHAOTIC SPIKE TRAINS

The spike trains generated by a neuron model are studied by the methods of nonlinear time series analysis. The results show that the spike trains are chaotic. To investigate effect of noise on transmission of chaotic spike trains, this chaotic spike trains are used as a discrete subthreshold input signal to the integrate-and-fire neuronal model and the FitzHugh-Nagumo(FHN) neuronal model working in noisy environment. The mutual information between the input spike trains and the output spike trains is calculated, the result shows that the transformation of information encoded by the chaotic spike trains is optimized by some level of noise, and stochastic resonance(SR) measured by mutual information is a property available for neurons to transmit chaotic spike trains.     

Stochastic resonance in a single neuron model: theory and analog simulation.

Here, we consider a noisy, bistable, single neuron model in the presence of periodic external modulation. The modulation induces a correlated switching between states driven by the noise. The information flow through the system, from the modulation, or signal, to the output switching events, leads to a succession of strong peaks in the power spectrum. The signal-to-noise ratio (SNR) obtained from this power spectrum is a measure of the information content in the neuron response. With increasing noise intensity, the SNR passes through a maximum: an effect which has been called stochastic resonance, and which was first advanced as a possible explanation of the observed periodicity in the recurrences of the Earth's ice ages. We treat the problem within the framework of a recently developed approximate theory, valid in the limits of weak noise intensity, weak periodic forcing and low forcing frequency, for both additive and multiplicative noise. Moreover, we have constructed an analog simulator of the neuron which demonstrates the stochastic resonance effect, and with which we have measured the SNRs for comparison with the theoretical results. Our model should be of interest in situations where a single inherently noisy neuron is the receptor of a periodic signal, which is itself noisy, either from the network or from an external source.     

Emergence of resonances in neural systems: the interplay between adaptive threshold and short-term synaptic plasticity

In this work we study the detection of weak stimuli by spiking (integrate-and-fire) neurons in the presence of certain level of noisy background neural activity. Our study has focused in the realistic assumption that the synapses in the network present activity-dependent processes, such as short-term synaptic depression and facilitation. Employing mean-field techniques as well as numerical simulations, we found that there are two possible noise levels which optimize signal transmission. This new finding is in contrast with the classical theory of stochastic resonance which is able to predict only one optimal level of noise. We found that the complex interplay between adaptive neuron threshold and activity-dependent synaptic mechanisms is responsible for this new phenomenology. Our main results are confirmed by employing a more realistic FitzHugh-Nagumo neuron model, which displays threshold variability, as well as by considering more realistic stochastic synaptic models and realistic signals such as poissonian spike trains.     

Noise-Induced Coherence and Network Oscillations in a Reduced Bursting Model

The dynamics of the Hindmarsh-Rose (HR) model of bursting thalamic neurons is reduced to a system of two linear differential equations that retains the subthreshold resonance properties of the HR model. Introducing a reset mechanism after a threshold crossing, we turn this system into a resonant integrate-and-fire (RIF) model. Using Monte-Carlo simulations and mathematical analysis, we examine the effects of noise and the subthreshold dynamic properties of the RIF model on the occurrence of coherence resonance (CR). Synchronized burst firing occurs in a network of such model neurons with excitatory pulse-coupling. The coherence level of the network oscillations shows a stochastic resonance-like dependence on the noise level. Stochastic analysis of the equations shows that the slow recovery from the spike-induced inhibition is crucial in determining the frequencies of the CR and the subthreshold resonance in the original HR model. In this particular type of CR, the oscillation frequency strongly depends on the intrinsic time scales but changes little with the noise intensity. We give analytical quantities to describe this CR mechanism and illustrate its influence on the emerging network oscillations. We discuss the profound physiological roles this kind of CR may have in information processing in neurons possessing a subthreshold resonant frequency and in generating synchronized network oscillations with a frequency that is determined by intrinsic properties of the neurons. PACS 05.45.-a, 05.40.Ca, 87.18.Sn, 87.19     

Noise enhances subthreshold oscillations in injured primary sensory neurons

Noise can play a constructive role in the detection of weak signals in various kinds of peripheral receptors and neurons. What the mechanism underlying the effect of noise is remains unclear. Here, the perforated patch-clamp technique was used on isolated cells from chronic compression of the dorsal root ganglion (DRG) model. Our data provided new insight indicating that, under conditions without external signals, noise can enhance subthreshold oscillations, which was observed in a certain type of neurons with high-frequency (20-100 Hz) intrinsic resonance from injured DRG neurons. The occurrence of subthreshold oscillation considerably decreased the threshold potential for generating repetitive firing. The above effects of noise can be abolished by blocking the persistent sodium current (I(Na, P)). Utilizing a mathematical neuron model we further simulated the effect of noise on subthreshold oscillation and firing, and also found that noise can enhance the electrical activity through autonomous stochastic resonance. Accordingly, we propose a new concept of the effects of noise on neural intrinsic activity, which suggests that noise may be an important factor for modulating the excitability of neurons and generation of chronic pain signals.