Noise Normalizes Firing Output of Mouse Lateral Geniculate Nucleus Neurons

The output of individual neurons is dependent on both synaptic and intrinsic membrane properties. While it is clear that the response of an individual neuron can be facilitated or inhibited based on the summation of its constituent synaptic inputs, it is not clear whether subthreshold activity, (e.g. synaptic “noise”- fluctuations that do not change the mean membrane potential) also serve a function in the control of neuronal output. Here we studied this by making whole-cell patch-clamp recordings from 29 mouse thalamocortical relay (TC) neurons. For each neuron we measured neuronal gain in response to either injection of current noise, or activation of the metabotropic glutamate receptor-mediated cortical feedback network (synaptic noise). As expected, injection of current noise via the recording pipette induces shifts in neuronal gain that are dependent on the amplitude of current noise, such that larger shifts in gain are observed in response to larger amplitude noise injections. Importantly we show that shifts in neuronal gain are also dependent on the intrinsic sensitivity of the neuron tested, such that the gain of intrinsically sensitive neurons is attenuated divisively in response to current noise, while the gain of insensitive neurons is facilitated multiplicatively by injection of current noise- effectively normalizing the output of the dLGN as a whole. In contrast, when the cortical feedback network was activated, only multiplicative gain changes were observed. These network activation-dependent changes were associated with reductions in the slow afterhyperpolarization (sAHP), and were mediated at least in part, by T-type calcium channels. Together, this suggests that TC neurons have the machinery necessary to compute multiple output solutions to a given set of stimuli depending on the current level of network stimulation.     

Estimation of the Distribution of a Genetically Determined Enzyme Polymorphism by a Stochastical Outside Criterion

The remaining cholinesterase activity after incubation with paraoxon (as an indicator of the activity of human serumparaoxonase) shows a distribution with three clusters which could be explained, after an investigation of families, by a simple two-allele-model. A Stochastical outside criterion is given by the constellations of the values within the families. By modification of an iteration method described by FANGMEYER (1964) it is possible to estimate the distributions and frequencies of the three groups. A simulation study shows that the combination of the used method with the pure cluster analysis is advantageous.     

Variable threshold as a model for selective attention, (de)sensitization,and anesthesia in associative neural networks

We study the influence of a variable neuronal threshold on fixed points and convergence rates of an associative neural network in the presence of noise. We allow a random distribution in the activity levels of the patterns stored, and a modification to the standard Hebbian learning rule is proposed for this purpose. There is a threshold at which the retrieval ability, including the average final overlap and the convergence rate, is optimized for patterns with a particular activity level at a given noise level. This type of selective attention to one class of patterns with a certain activity level may be obtained at the cost of reducing the retrieval ability of the network for patterns with different activity levels. The effects of a constant threshold independent of noise, time, and pattern are discussed. For high-(low-) activity patterns, the average final overlap is shown to be increased at high noise levels and decreased at low noise levels by a negative (positive) constant threshold, whereas a positive (negative) threshold always reduces the final average overlap. When the magnitude of the constant threshold exceeds a critical value, there is no retrieval. Rates of convergence towards the stored pattern with negative (positive) thresholds are greater than those with positive (negative) thresholds. These results are related to (de)sensitization and anesthesia. For certain threshold values and patterns with certain activity levels, hysteresis appears in the plot of the average final overlap versus the noise level, even for first order interactions. We make the analogy between the pattern-dependent neuronal threshold proposed in the present paper and the task-related modulation in neuronal excitability determined by cognitive factors, such as the attentional state of a higher animal. A constant threshold is associated with overall changes in neuronal excitability caused, e.g., by various drugs and physical injuries. Neurophysiological evidence of a dynamically variable neuronal threshold, such as accommodation and potentiation, is presented.     

Characterization of single channel currents using digital signal processing techniques based on Hidden Markov Models.

Techniques for extracting small, single channel ion currents from background noise are described and tested. It is assumed that single channel currents are generated by a first-order, finite-state, discrete-time, Markov process to which is added 'white' background noise from the recording apparatus (electrode, amplifiers, etc). Given the observations and the statistics of the background noise, the techniques described here yield a posteriori estimates of the most likely signal statistics, including the Markov model state transition probabilities, duration (open- and closed-time) probabilities, histograms, signal levels, and the most likely state sequence. Using variations of several algorithms previously developed for solving digital estimation problems, we have demonstrated that: (1) artificial, small, first-order, finite-state, Markov model signals embedded in simulated noise can be extracted with a high degree of accuracy, (2) processing can detect signals that do not conform to a first-order Markov model but the method is less accurate when the background noise is not white, and (3) the techniques can be used to extract from the baseline noise single channel currents in neuronal membranes. Some studies have been included to test the validity of assuming a first-order Markov model for biological signals. This method can be used to obtain directly from digitized data, channel characteristics such as amplitude distributions, transition matrices and open- and closed-time durations.     

Noise controlled synchronization in potassium coupled neural models

The paper applies biologically plausible models to investigate how noise input to small ensembles of neurons, coupled via the extracellular potassium concentration, can influence their firing patterns. Using the noise intensity and the volume of the extracellular space as control parameters, we show that potassium induced depolarization underlies the formation of noise-induced patterns such as delayed firing and synchronization. These phenomena are associated with the appearance of new time scales in the distribution of interspike intervals that may be significant for the spatio-temporal oscillations in neuronal ensembles.     

噪声对海马CA3区神经元电活动及突触超微结构的影响

本文用电生理学方法及电镜技术研究105dB(A)白噪声对海马CA3区神经元电活动及突触超微结构的影响。结果表明:大鼠在强噪声暴露期间(5min),其神经元放电出现减频反应(占53.3％),增频反应(占20％)和基本无反应(占26.7％)。而强噪声定时重复暴露(每天1h共50天)后,单位放电频率极显著地低于对照组,以及高频单位消失而低频单位增加;同时,突触超微结构(大鼠和豚鼠)也出现小泡不集中于突触前膜和线粒体空泡化增多等不利于突触功能的变化。表明强噪声对海马CA3区神经元的影响是明显的,且以抑制性作用更为显著。本文结合本室以往工作进行讨论,认为噪声影响学习功能可能有通过影响海马的活动而作用的机制。     

Population Decoding in Rat Barrel Cortex: Optimizing the Linear Readout of Correlated Population Responses

Sensory information is encoded in the response of neuronal populations. How might this information be decoded by downstream neurons? Here we analyzed the responses of simultaneously recorded barrel cortex neurons to sinusoidal vibrations of varying amplitudes preceded by three adapting stimuli of 0, 6 and 12 µm in amplitude. Using the framework of signal detection theory, we quantified the performance of a linear decoder which sums the responses of neurons after applying an optimum set of weights. Optimum weights were found by the analytical solution that maximized the average signal-to-noise ratio based on Fisher linear discriminant analysis. This provided a biologically plausible decoder that took into account the neuronal variability, covariability, and signal correlations. The optimal decoder achieved consistent improvement in discrimination performance over simple pooling. Decorrelating neuronal responses by trial shuffling revealed that, unlike pooling, the performance of the optimal decoder was minimally affected by noise correlation. In the non-adapted state, noise correlation enhanced the performance of the optimal decoder for some populations. Under adaptation, however, noise correlation always degraded the performance of the optimal decoder. Nonetheless, sensory adaptation improved the performance of the optimal decoder mainly by increasing signal correlation more than noise correlation. Adaptation induced little systematic change in the relative direction of signal and noise. Thus, a decoder which was optimized under the non-adapted state generalized well across states of adaptation.     

Synchronous Clusters in a Noisy Inhibitory Neural Network

We study the stability and information encoding capacity of synchronized states in a neuronal network model that represents part of thalamic circuitry. Our model neurons have a Hodgkin-Huxley-type low-threshold calcium channel, display postinhibitory rebound, and are connected via GABAergic inhibitory synapses.We find that there is a threshold in synaptic strength, _c, below which there are no stable spiking network states. Above threshold the stable spiking state is a cluster state, where different groups of neurons fire consecutively, and each neuron fires with the same cluster each time. Weak noise destabilizes this state, but stronger noise drives the system into a different, self-organized, stochastically synchronized state. Neuronal firing is still organized in clusters, but individual neurons can hop from cluster to cluster. Noise can actually induce and sustain such a state below the threshold of synaptic strength. We do find a qualitative difference in the firing patterns between small (10 neurons) and large (1000 neurons) networks.We determine the information content of the spike trains in terms of two separate contributions: the spike-time jitter around cluster firing times, and the hopping from cluster to cluster. We quantify the information loss due to temporally correlated interspike intervals. Recent experiments on the locust olfactory system and striatal neurons suggest that the nervous system may actually use these two channels to encode separate and unique information.     

A model of stochastic variations of postsynaptic activity

Stochastic oscillations imitating postsynaptic activity in the excitatory neurons are produced by a nonlinear difference equation which does not contain any sources of noise. The given back inhibition via inhibitory interneurons presents a negative feedback loop due to which oscillations in the model system are realized. By means of variation of parameters of the system the patterns of stochastic oscillations can be changed in wide range of physiologically meaningful patterns of the neuronal activity.     

Reactive oxygen species and the modulation of stroke

Reactive oxygen species and oxidative state are slowly gaining acceptance in having a physiological relevance rather than just being the culprits in pathophysiological processes. The control of the redox environment of the cell provides for additional regulation in relation to critical cellular signal transduction pathways. Conversely, aberrant regulation of oxidative state manifesting as oxidative stress can predispose a cell to adverse outcome. The PI3-kinase/Akt pathway is one such pathway that is partially regulated via oxidative state and, in an oxidative stress paradigm such as ischemic reperfusion injury, may be inactivated, which can lead to potentiation and or exacerbation of cell death. Activation of NF(kappa)B has also been associated with oxidative stress. The role of NF(kappa)B in neuronal cell death is widely debated, with major studies highlighting both a pro- and an antiapoptotic role for NF(kappa)B with the outcome being region, stimulus, dose, and duration specific. This review hopes to make clear that oxidative state plays a key role in the regulation and control of numerous signal transduction pathways in the cell and that elucidating the mechanisms behind oxidative stress-mediated neuronal cell death is important in identifying potential putative targets for the treatment of neuropathologies such as stroke.     

On the Origins of Signal Variance in FMRI of the Human Midbrain at High Field

Functional Magnetic Resonance Imaging (fMRI) in the midbrain at 7 Tesla suffers from unexpectedly low temporal signal to noise ratio (TSNR) compared to other brain regions. Various methodologies were used in this study to quantitatively identify causes of the noise and signal differences in midbrain fMRI data. The influence of physiological noise sources was examined using RETROICOR, phase regression analysis, and power spectral analyses of contributions in the respiratory and cardiac frequency ranges. The impact of between-shot phase shifts in 3-D multi-shot sequences was tested using a one-dimensional (1-D) phase navigator approach. Additionally, the effects of shared noise influences between regions that were temporally, but not functionally, correlated with the midbrain (adjacent white matter and anterior cerebellum) were investigated via analyses with regressors of ‘no interest’. These attempts to reduce noise did not improve the overall TSNR in the midbrain. In addition, the steady state signal and noise were measured in the midbrain and the visual cortex for resting state data. We observed comparable steady state signals from both the midbrain and the cortex. However, the noise was 2–3 times higher in the midbrain relative to the cortex, confirming that the low TSNR in the midbrain was not due to low signal but rather a result of large signal variance. These temporal variations did not behave as known physiological or other noise sources, and were not mitigated by conventional strategies. Upon further investigation, resting state functional connectivity analysis in the midbrain showed strong intrinsic fluctuations between homologous midbrain regions. These data suggest that the low TSNR in the midbrain may originate from larger signal fluctuations arising from functional connectivity compared to cortex, rather than simply reflecting physiological noise.     

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 MEASURED BY THEMUTUAL INFORMATION IN THE NEURONSTHAT TRANSMIT CHAOTIC SPIKE TRAINS

１ＩｎｔｒｏｄｕｃｔｉｏｎＩｔｉｓｗｅｌｌｋｎｏｗｎｔｈａｔｎｅｒｖｅｃｅｌｌｓｗｏｒｋｉｎｎｏｉｓｙｅｎｖｉｒｏｎｍｅｎｔ,ａｎｄｎｏｉｓｅｓｏｕｒｃｅｓｒａｎｇｉｎｇｆｒｏｍｉｎｔｅｒｎａｌｔｈｅｒｍａｌｎｏｉｓｅｔｏｅｘｔｅｒｎａｌｐｅｒｔｕｒｂａｔｉｏｎ．Ｏｎｅｐｕｚｚｌｉｎｇｐｒｏｂｌｅｍｉｓｈｏｗｄｏｎｅｒｖｅｃｅｌｌｓａｃｃｏｍｍｏｄａｔｅｎｏｉｓｅｉｎｃｏｄｉｎｇａｎｄｔｒａｎｓｆｏｒｍｉｎｇｉｎｆｏｒｍａｔｉｏｎ,ｒｅｃｅｎｔｒｅｓｅａｒｃｈｓｈｏｗｓｔｈａｔｎｏｉｓｅｍａｙｐ…     

Noise effects on robust synchronization of a small pacemaker neuronal ensemble via nonlinear controller: electronic circuit design

In this paper, we report on the synchronization of a pacemaker neuronal ensemble constituted of an AB neuron electrically coupled to two PD neurons. By the virtue of this electrical coupling, they can fire synchronous bursts of action potential. An external master neuron is used to induce to the whole system the desired dynamics, via a nonlinear controller. Such controller is obtained by a combination of sliding mode and feedback control. The proposed controller is able to offset uncertainties in the synchronized systems. We show how noise affects the synchronization of the pacemaker neuronal ensemble, and briefly discuss its potential benefits in our synchronization scheme. An extended Hindmarsh–Rose neuronal model is used to represent a single cell dynamic of the network. Numerical simulations and Pspice implementation of the synchronization scheme are presented. We found that, the proposed controller reduces the stochastic resonance of the network when its gain increases.     

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     

Noise-induced divisive gain control in neuron models

A recent computational study of gain control via shunting inhibition has shown that the slope of the frequency-versus-input (f-I) characteristic of a neuron can be decreased by increasing the noise associated with the inhibitory input (Neural Comput. 13, 227-248). This novel noise-induced divisive gain control relies on the concommittant increase of the noise variance with the mean of the total inhibitory conductance. Here we investigate this effect using different neuronal models. The effect is shown to occur in the standard leaky integrate-and-fire (LIF) model with additive Gaussian white noise, and in the LIF with multiplicative noise acting on the inhibitory conductance. The noisy scaling of input currents is also shown to occur in the one-dimensional theta-neuron model, which has firing dynamics, as well as a large scale compartmental model of a pyramidal cell in the electrosensory lateral line lobe of a weakly electric fish. In this latter case, both the inhibition and the excitatory input have Poisson statistics; noise-induced divisive inhibition is thus seen in f-I curves for which the noise increases along with the input I. We discuss how the variation of the noise intensity along with inputs is constrained by the physiological context and the class of model used, and further provide a comparison of the divisive effect across models.     

Neurofibromatosis type I tumor suppressor neurofibromin regulates neuronal differentiation via its GTPase-activating protein function toward Ras

Neurofibromin, the neurofibromatosis type 1 (NF1) gene product, contains a central domain homologous to a family of proteins known as Ras-GTPase-activating proteins (Ras-GAPs), which function as negative regulators of Ras. The loss of neurofibromin function has been thought to be implicated in the abnormal regulation of Ras in NF1-related pathogenesis. In this study, we found a novel role of neurofibromin in neuronal differentiation in conjunction with the regulation of Ras activity via its GAP-related domain (GRD) in neuronal cells. In PC12 cells, time-dependent increases in the GAP activity of cellular neurofibromin (NF1-GAP) were detected after NGF stimulation, which were correlated with the down-regulation of Ras activity during neurite elongation. Interestingly, the NF1-GAP increase was due to the induction of alternative splicing of NF1-GRD type I triggered by the NGF-induced Ras activation. Dominant-negative (DN) forms of NF1-GRD type I significantly inhibited the neurite extension of PC12 cells via regulation of the Ras state. NF1-GRD-DN also reduced axonal and dendritic branching/extension of rat embryonic hippocampal neurons. These results demonstrate that the mutual regulation of Ras and NF1-GAP is essential for normal neuronal differentiation and that abnormal regulation in neuronal cells may be implicated in NF1-related learning and memory disturbance.     

Molecular dynamics of alamethicin transmembrane channels from open-channel current noise analysis.

Conductance noise measurement of the open states of alamethicin transmembrane channels reveals excess noise attributable to cooperative low-frequency molecular dynamics that can generate fluctuations approximately 1 A rms in the effective channel pore radius. Single-channel currents through both persistent and nonpersistent channels with multiple conductance states formed by purified polypeptide alamethicin in artificial phospholipid bilayers isolated onto micropipettes with gigaohm seals were recorded using a voltage-clamp technique with low background noise (rms noise < 3 pA up to 20 kHz). Current noise power spectra between 100 Hz and 20 kHz of each open channel state showed little frequency dependence. Noise from undetected conductance state transitions was insignificant. Johnson and shot noises were evaluated. Current noise caused by electrolyte concentration fluctuation via diffusion was isolated by its dependence on buffer concentration. After removing these contributions, significant current noise remains in all persistent channel states and increases in higher conductance states. In nonpersistent channels, remaining noise occurs primarily in the lowest two states. These fluctuations of channel conductance are attributed to thermal oscillations of the channel molecular conformation and are modeled as a Langevin translational oscillation of alamethicin molecules moving radially from the channel pore, damped mostly by lipid bilayer viscosity.     

Conductance versus current noise in a neuronal model for noisy subthreshold oscillations and related spike generation

Biological systems are notoriously noisy. Noise, therefore, also plays an important role in many models of neural impulse generation. Noise is not only introduced for more realistic simulations but also to account for cooperative effects between noisy and nonlinear dynamics. Often, this is achieved by a simple noise term in the membrane equation (current noise). However, there are ongoing discussions whether such current noise is justified or whether rather conductance noise should be introduced because it is closer to the natural origin of noise. Therefore, we have compared the effects of current and conductance noise in a neuronal model for subthreshold oscillations and action potential generation. We did not see any significant differences in the model behavior with respect to voltage traces, tuning curves of interspike intervals, interval distributions or frequency responses when the noise strength is adjusted. These findings indicate that simple current noise can give reasonable results in neuronal simulations with regard to physiological relevant noise effects.     

Modeling electromagnetic fields detectability in a HH-like neuronal system: stochastic resonance and window behavior

Noise has already been shown to play a constructive role in neuronal processing and reliability, according to stochastic resonance (SR). Here another issue is addressed, concerning noise role in the detectability of an exogenous signal, here representing an electromagnetic (EM) field. A Hodgkin–Huxley like neuronal model describing a myelinated nerve fiber is proposed and validated, excited with a suprathreshold stimulation. EM field is introduced as an additive voltage input and its detectability in neuronal response is evaluated in terms of the output signal-to-noise ratio. Noise intensities maximizing spiking activity coherence with the exogenous EM signal are clearly shown, indicating a stochastic resonant behavior, strictly connected to the model frequency sensitivity. In this study SR exhibits a window of occurrence in the values of field frequency and intensity, which is a kind of effect long reported in bioelectromagnetic experimental studies. The spatial distribution of the modeled structure also allows to investigate possible effects on action potentials saltatory propagation, which results to be reliable and robust over the presence of an exogenous EM field and biological noise. The proposed approach can be seen as assessing biophysical bases of medical applications funded on electric and magnetic stimulation where the role of noise as a cooperative factor has recently gained growing attention. This work investigates the role of noise as a cooperative factor for the detection of an exogenous electromagnetic field in a compartimental model of a myelinated nerve fiber. The occurrence of stochastic resonance is discussed in relation to neuronal frequency sensitivity.