背景噪声对人感知声音时间信息的影响

对声音时间信息的分辨在人和动物感知声音信息的过程中至关重要.在自然声环境中,声音信息总处于一定的噪声背景下.文章以间隔探测阈值为指标测定了人对纯音和噪声的间隔探测阈值,以及持续噪声背景对间隔探测阈值的影响.声音信号采用1000～10000 Hz的纯音信号和白噪声信号,声音强度为70 dB SPL.背景噪声为持续白噪声,强度分别为45、55、65 dB SPL.结果表明,对纯音信号,随着背景噪声强度增加,间隔探测阈值有升高的趋势.对噪声信号来说,45、55 dB SPL的背景噪声对噪声信号的间隔探测阈值无显著影响,但65 dB SPL的背景噪声使间隔探测阈值显著升高.研究结果提示,背景噪声能够在一定程度上影响人对声音时间信息的感知,影响的程度与背景噪声的强度有关.     

Optimum signal in a simple neuronal model with signal-dependent noise

How does the information about a signal in neural threshold crossings depend on the noise acting upon it? Two models are explored, a binary McCulloch and Pitts (threshold exceedance) model and a model of waiting time to exceedance--a discrete-time version of interspike intervals. If noise grows linearly with the signal, we find the best identification of the signal in terms of the Fisher information is for signals that do not reach the threshold in the absence of noise. Identification attains the same precision under weak and strong signals, but the coding range decreases at both extremes of signal level. We compare the results obtained for Fisher information with those using related first and second moment measures. The maximum obtainable information is plotted as a function of the ratio of noise to signal.     

Information content of molecular structures

For a completely enumerated set of conformers of a macromolecule or for exhaustive lattice walks of model polymers it is straightforward to use Shannon information theory to deduce the information content of the ensemble. It is also practicable to develop numerical measures of the information content of sets of exact distance constraints applied to specific conformational ensembles. We examine the effects of experimental uncertainties by considering "noisy" constraints. The introduction of noise requires additional assumptions about noise distribution and conformational clustering protocols that make the problem of measuring information content more complex. We make use of a standard concept in communication theory, the "noise sphere," to link uncertainty in measurements to information loss. Most of our numerical results are derived from two-dimensional lattice ensembles. Expressing results in terms of information per degree of freedom removes almost all of the chain length dependence. We also explore off-lattice polyalanine chains that yield surprisingly similar results.     

Balance between Noise and Information Flow Maximizes Set Complexity of Network Dynamics

Boolean networks have been used as a discrete model for several biological systems, including metabolic and genetic regulatory networks. Due to their simplicity they offer a firm foundation for generic studies of physical systems. In this work we show, using a measure of context-dependent information, set complexity, that prior to reaching an attractor, random Boolean networks pass through a transient state characterized by high complexity. We justify this finding with a use of another measure of complexity, namely, the statistical complexity. We show that the networks can be tuned to the regime of maximal complexity by adding a suitable amount of noise to the deterministic Boolean dynamics. In fact, we show that for networks with Poisson degree distributions, all networks ranging from subcritical to slightly supercritical can be tuned with noise to reach maximal set complexity in their dynamics. For networks with a fixed number of inputs this is true for near-to-critical networks. This increase in complexity is obtained at the expense of disruption in information flow. For a large ensemble of networks showing maximal complexity, there exists a balance between noise and contracting dynamics in the state space. In networks that are close to critical the intrinsic noise required for the tuning is smaller and thus also has the smallest effect in terms of the information processing in the system. Our results suggest that the maximization of complexity near to the state transition might be a more general phenomenon in physical systems, and that noise present in a system may in fact be useful in retaining the system in a state with high information content.     

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

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Fisher information as a metric of locally optimal processing and stochastic resonance

The origins of Fisher information are in its use as a performance measure for parametric estimation. We augment this and show that the Fisher information can characterize the performance in several other significant signal processing operations. For processing of a weak signal in additive white noise, we demonstrate that the Fisher information determines (i) the maximum output signal-to-noise ratio for a periodic signal; (ii) the optimum asymptotic efficacy for signal detection; (iii) the best cross-correlation coefficient for signal transmission; and (iv) the minimum mean square error of an unbiased estimator. This unifying picture, via inequalities on the Fisher information, is used to establish conditions where improvement by noise through stochastic resonance is feasible or not.     

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.     

Different types of noise in leaky integrate-and-fire model of neuronal dynamics with discrete periodical input

Different variants of stochastic leaky integrate-and-fire model for the membrane depolarisation of neurons are investigated. The model is driven by a constant input and equidistant pulses of fixed amplitude. These two types of signal are considered under the influence of three types of noise: white noise, jitter on interpulse distance, and noise in the amplitude of pulses. The results of computational experiments demonstrate the enhancement of the signal by noise in subthreshold regime and deterioration of the signal if it is sufficiently strong to carry the information in absence of noise. Our study holds mainly to central neurons that process discrete pulses although an application in sensory system is also available.     

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．     

Frequency encoded biochemical regulation is more accurate than amplitude dependent control

It is generally recognised in engineering that encoding information in a frequency provides resistance to degradation by noise and an enhanced precision of control. This paper demonstrates how the same arguments can be applied to biochemical control networks. It shows that the conversion of an analogue demand signal to an oscillation is stable against corruption by noise in the input and even against corruption by certain internal chaotic motions. The paper also argues that intracellular transmission of frequency encoded information is robust against noise. These arguments are proposed as a partial explanation of why so many biological regulatory systems are periodic.     

A common goodness-of-fit framework for neural population models using marked point process time-rescaling

The noisy threshold regime, where even a small set of presynaptic neurons can significantly affect postsynaptic spike-timing, is suggested as a key requisite for computation in neurons with high variability. It also has been proposed that signals under the noisy conditions are successfully transferred by a few strong synapses and/or by an assembly of nearly synchronous synaptic activities. We analytically investigate the impact of a transient signaling input on a leaky integrate-and-fire postsynaptic neuron that receives background noise near the threshold regime. The signaling input models a single strong synapse or a set of synchronous synapses, while the background noise represents a lot of weak synapses. We find an analytic solution that explains how the first-passage time (ISI) density is changed by transient signaling input. The analysis allows us to connect properties of the signaling input like spike timing and amplitude with postsynaptic first-passage time density in a noisy environment. Based on the analytic solution, we calculate the Fisher information with respect to the signaling input’s amplitude. For a wide range of amplitudes, we observe a non-monotonic behavior for the Fisher information as a function of background noise. Moreover, Fisher information non-trivially depends on the signaling input’s amplitude; changing the amplitude, we observe one maximum in the high level of the background noise. The single maximum splits into two maximums in the low noise regime. This finding demonstrates the benefit of the analytic solution in investigating signal transfer by neurons.     

Encoding of illusory continuity in primary auditory cortex

When interfering objects occlude a scene, the visual system restores the occluded information. Similarly, when a sound of interest (a "foreground" sound) is interrupted (occluded) by loud noise, the auditory system restores the occluded information. This process, called auditory induction, can be exploited to create a continuity illusion. When a segment of a foreground sound is deleted and loud noise fills the missing portion, listeners incorrectly report hearing the foreground continuing through the noise. Here we reveal the neurophysiological underpinnings of illusory continuity in single-neuron responses from awake macaque monkeys' primary auditory cortex (A1). A1 neurons represented the missing segment of occluded tonal foregrounds by responding to discontinuous foregrounds interrupted by intense noise as if they were responding to the complete foregrounds. By comparison, simulated peripheral responses represented only the noise and not the occluded foreground. The results reveal that many A1 single-neuron responses closely follow the illusory percept.     

A communication channel model for information transmission in the blowfly photoreceptor

Biological photoreceptors transduce and communicate information about visual stimuli to other neurons through a series of signal transformations among physical states such as concentration of a chemical species, current, or the number of open ion channels. We present a communication channel model to quantify the transmission and degradation of visual information in the blowfly photoreceptor cell. The model is a cascade of linear transfer functions and noise sources that are derived from fundamental principles whenever possible, and whose parameters are estimated from physiological data. We employ the model to calculate the information capacity of blowfly phototransduction; our results compare favorably with estimates of the capacity derived from experimental measurements by de Ruyter van Steveninck and Laughlin (Nature 379 (1996) 642-645) and Juusola (J. Gen. Physiol. 104 (1994) 593-621). The model predicts that photon shot noise and ion channel noise are the dominant noise sources that limits information transmission in the blowfly photoreceptor.     

Inferences about membrane properties from electrical noise measurements

Four sources of electrical noise in biological membranes, each with a different physical basis, are discussed; the analysis of each type of noise potentially yields a different sort of information about membrane properties. (a) From the thermal noise spectrum, the passive membrane impedance may be obtained, so that thermal noise measurements are essentially equivalent to the type of since wave analysis carried out by Cole and Curtis. (b) If adequately high frequency measurements could be made, the shot noise spectrum should give information about the average motion of a single ion within the membrane. (c) The number of charge carriers and single ion mobilities within the membrane can possibly be inferred from measurements of noise with a 1/f spectrum. Available data indicate, for example, that increases in axon membrane conductance are not achieved by modulations in the mobility of ions within the membrane. (d) Fluctuations arising from the mechanisms normally responsible for membrane conductance changes can produce a type of electrical noise. Analysis of such conductance fluctuations provides a way to assess the validity of various microscopic models for the behavior of individual channels. Two different probabilistic interpretations of the Hodgkin-Huxley equations are investigated here and shown to yield different predictions about the spectrum of conductance fluctuations; thus, appropriate noise measurements may serve to eliminate certain classes of microscopic models for membrane conductance changes. Further, it is shown how the analysis of conductance fluctuations can, in some circumstances, provide an estimate of the conductance of a single channel.     

Extracting abundance information from DNA-based data

The accurate extraction of species-abundance information from DNA-based data (metabarcoding, metagenomics) could contribute usefully to diet analysis and food-web reconstruction, the inference of species interactions, the modelling of population dynamics and species distributions, the biomonitoring of environmental state and change, and the inference of false positives and negatives. However, multiple sources of bias and noise in sampling and processing combine to inject error into DNA-based data sets. To understand how to extract abundance information, it is useful to distinguish two concepts. (i) Within-sample across-species quantification describes relative species abundances in one sample. (ii) Across-sample within-species quantification describes how the abundance of each individual species varies from sample to sample, such as over a time series, an environmental gradient or different experimental treatments. First, we review the literature on methods to recover across-species abundance information (by removing what we call “species pipeline biases”) and within-species abundance information (by removing what we call “pipeline noise”). We argue that many ecological questions can be answered with just within-species quantification, and we therefore demonstrate how to use a “DNA spike-in” to correct for pipeline noise and recover within-species abundance information. We also introduce a model-based estimator that can be used on data sets without a physical spike-in to approximate and correct for pipeline noise.     

Information and discriminability as measures of reliability of sensory coding

Response variability is a fundamental issue in neural coding because it limits all information processing. The reliability of neuronal coding is quantified by various approaches in different studies. In most cases it is largely unclear to what extent the conclusions depend on the applied reliability measure, making a comparison across studies almost impossible. We demonstrate that different reliability measures can lead to very different conclusions even if applied to the same set of data: in particular, we applied information theoretical measures (Shannon information capacity and Kullback-Leibler divergence) as well as a discrimination measure derived from signal-detection theory to the responses of blowfly photoreceptors which represent a well established model system for sensory information processing. We stimulated the photoreceptors with white noise modulated light intensity fluctuations of different contrasts. Surprisingly, the signal-detection approach leads to a safe discrimination of the photoreceptor response even when the response signal-to-noise ratio (SNR) is well below unity whereas Shannon information capacity and also Kullback-Leibler divergence indicate a very low performance. Applying different measures, can, therefore, lead to very different interpretations concerning the system's coding performance. As a consequence of the lower sensitivity compared to the signal-detection approach, the information theoretical measures overestimate internal noise sources and underestimate the importance of photon shot noise. We stress that none of the used measures and, most likely no other measure alone, allows for an unbiased estimation of a neuron's coding properties. Therefore the applied measure needs to be selected with respect to the scientific question and the analyzed neuron's functional context.     

Temporal uncertainty in reading the neural code (proportional noise)

It is widely accepted that neural information is encoded in the timing of action potentials. However, such information can only be transmitted if the receiver (soma, muscle fibre) can measure time precisely. We argue that such timing is inevitably imprecise due to biological fluctuations in receiver function. We show that this leads to a receiver output, where the standard deviation of receiver output noise is proportional to the mean level of the receiver output. Proportional noise is fundamentally distinct from the common assumption of square-root relationship seen in renewal processes (e.g. Poisson noise), which is usually assumed in neural firing patterns. We show that proportional noise may be the limiting constraint in receiver function, and that it could account for such diverse perceptual and motor behaviours encompassed by Weber's and Fitt's laws, as well as the dynamic trajectories of rapid feed-forward motor behaviours such as arm reaching and saccades. We conclude that temporal uncertainty should be potentially a fundamental stochastic constraint on network function and behaviour.     

Compensation for nucleotide bias in a genome by representation as a discrete channel with noise

MOTIVATION: Calculation of the information content of motifs in genomes highly biased in nucleotide composition is likely to lead to overestimates of the amount of useful information in the motif. Calculating relative information can compensate for biases, however the resulting information content is the amount seen by an observer and not by a macromolecule binding to the motif. The latter is needed to calculate the discriminatory power of the motif and to compare motifs between species. RESULTS: By treating a biased genome as a discrete channel with noise, in accordance with Shannon Information Theory, we were able to remove both 'Distortion' and 'Noise' from the motif and recover a more instructive biological 'signal.' A Java application, LogoPaint, was developed to remove nucleotide bias distortion and triplet frequency noise from motifs, calculate information content and present the motif as a logo. We demonstrate how this technique can 'unmask' motifs in the translation initiation regions of bacteria that are obscured by strong sequence biases. AVAILABILITY: LogoPaint is available to all users from the authors as an executable JAR file. Source code is available by arrangement.     

Threshold fatigue and information transfer

Neurons in vivo must process sensory information in the presence of significant noise. It is thus plausible to assume that neural systems have developed mechanisms to reduce this noise. Theoretical studies have shown that threshold fatigue (i.e. cumulative increases in the threshold during repetitive firing) could lead to noise reduction at certain frequencies bands and thus improved signal transmission as well as noise increases and decreased signal transmission at other frequencies: a phenomenon called noise shaping. There is, however, no experimental evidence that threshold fatigue actually occurs and, if so, that it will actually lead to noise shaping. We analyzed action potential threshold variability in intracellular recordings in vivo from pyramidal neurons in weakly electric fish and found experimental evidence for threshold fatigue: an increase in instantaneous firing rate was on average accompanied by an increase in action potential threshold. We show that, with a minor modification, the standard Hodgkin–Huxley model can reproduce this phenomenon. We next compared the performance of models with and without threshold fatigue. Our results show that threshold fatigue will lead to a more regular spike train as well as robustness to intrinsic noise via noise shaping. We finally show that the increased/reduced noise levels due to threshold fatigue correspond to decreased/increased information transmission at different frequencies. Action Editor: David Golomb     

An Information-Based Approach to Change-Point Analysis with Applications to Biophysics and Cell Biology

This article describes the application of a change-point algorithm to the analysis of stochastic signals in biological systems whose underlying state dynamics consist of transitions between discrete states. Applications of this analysis include molecular-motor stepping, fluorophore bleaching, electrophysiology, particle and cell tracking, detection of copy number variation by sequencing, tethered-particle motion, etc. We present a unified approach to the analysis of processes whose noise can be modeled by Gaussian, Wiener, or Ornstein-Uhlenbeck processes. To fit the model, we exploit explicit, closed-form algebraic expressions for maximum-likelihood estimators of model parameters and estimated information loss of the generalized noise model, which can be computed extremely efficiently. We implement change-point detection using the frequentist information criterion (which, to our knowledge, is a new information criterion). The frequentist information criterion specifies a single, information-based statistical test that is free from ad hoc parameters and requires no prior probability distribution. We demonstrate this information-based approach in the analysis of simulated and experimental tethered-particle-motion data.