Detection Of Subthreshold Signals Research Articles

When Noise meets Chaos: Stochastic Resonance in Neurochaos Learning

Chaos and Noise are ubiquitous in the Brain. Inspired by the chaotic firing of neurons and the constructive role of noise in neuronal models, we for the first time connect chaos, noise and learning. In this paper, we demonstrate Stochastic Resonance (SR) phenomenon in Neurochaos Learning (NL). SR manifests at the level of a single neuron of NL and enables efficient subthreshold signal detection. Furthermore, SR is shown to occur in single and multiple neuronal NL architecture for classification tasks — both on simulated and real-world spoken digit datasets, and in architectures with 1D chaotic maps as well as Hindmarsh–Rose spiking neurons. Intermediate levels of noise in neurochaos learning enable peak performance in classification tasks thus highlighting the role of SR in AI applications, especially in brain inspired learning architectures.

Stochastic multiresonance in coupled excitable FHN neurons.

In this paper, effects of noise on Watts-Strogatz small-world neuronal networks, which are stimulated by a subthreshold signal, have been investigated. With the numerical simulations, it is surprisingly found that there exist several optimal noise intensities at which the subthreshold signal can be detected efficiently. This indicates the occurrence of stochastic multiresonance in the studied neuronal networks. Moreover, it is revealed that the occurrence of stochastic multiresonance has close relationship with the period of subthreshold signal Te and the noise-induced mean period of the neuronal networks T0. In detail, we find that noise could induce the neuronal networks to generate stochastic resonance for M times if Te is not very large and falls into the interval (M×T0,(M+1)×T0) with M being a positive integer. In real neuronal system, subthreshold signal detection is very meaningful. Thus, the obtained results in this paper could give some important implications on detecting subthreshold signal and propagating neuronal information in neuronal systems.

Detection of sub-threshold periodic signal by multiplicative and additive cross-correlated sine-Wiener noises in the FitzHugh–Nagumo neuron

We study the effects of multiplicative and additive cross-correlated sine-Wiener (CCSW) noises on the performance of sub-threshold periodic signal detection in the FitzHugh–Nagumo (FHN) neuron by calculating Fourier coefficients Q for measuring synchronization between sub-threshold input signal and the response of system. CCSW noises-induced transitions of electrical activity in the FHN neuron model can be observed. Moreover, the performance of sub-threshold periodic signal detection is achieved at moderate noise strength, cross-correlation time and cross-correlation strength of CCSW noises, which indicate the occurrence of CCSW noises-induced stochastic resonance. Furthermore, the performance of sub-threshold signal detection is strongly sensitive to cross-correlation time of CCSW noises. Therefore, the performance can be effectively controlled by regulating cross-correlation time of CCSW noises. These results provide a possible mechanism for amplifying or detecting the sub-threshold signal in the nervous system.

The heterospecific calling song can improve conspecific signal detection in a bushcricket species

In forest clearings of the Malaysian rainforest, chirping and trilling Mecopoda species often live in sympatry. We investigated whether a phenomenon known as stochastic resonance (SR) improved the ability of individuals to detect a low-frequent signal component typical of chirps when members of the heterospecific trilling species were simultaneously active. This phenomenon may explain the fact that the chirping species upholds entrainment to the conspecific song in the presence of the trill. Therefore, we evaluated the response probability of an ascending auditory neuron (TN-1) in individuals of the chirping Mecopoda species to triple-pulsed 2, 8 and 20 kHz signals that were broadcast 1 dB below the hearing threshold while increasing the intensity of either white noise or a typical triller song.Our results demonstrate the existence of SR over a rather broad range of signal-to-noise ratios (SNRs) of input signals when periodic 2 kHz and 20 kHz signals were presented at the same time as white noise. Using the chirp-specific 2 kHz signal as a stimulus, the maximum TN-1 response probability frequently exceeded the 50% threshold if the trill was broadcast simultaneously. Playback of an 8 kHz signal, a common frequency band component of the trill, yielded a similar result. Nevertheless, using the trill as a masker, the signal-related TN-1 spiking probability was rather variable. The variability on an individual level resulted from correlations between the phase relationship of the signal and syllables of the trill. For the first time, these results demonstrate the existence of SR in acoustically-communicating insects and suggest that the calling song of heterospecifics may facilitate the detection of a subthreshold signal component in certain situations. The results of the simulation of sound propagation in a computer model suggest a wide range of sender-receiver distances in which the triller can help to improve the detection of subthreshold signals in the chirping species.

Adaptive stochastic resonance for unknown and variable input signals

All sensors have a threshold, defined by the smallest signal amplitude that can be detected. The detection of sub-threshold signals, however, is possible by using the principle of stochastic resonance, where noise is added to the input signal so that it randomly exceeds the sensor threshold. The choice of an optimal noise level that maximizes the mutual information between sensor input and output, however, requires knowledge of the input signal, which is not available in most practical applications. Here we demonstrate that the autocorrelation of the sensor output alone is sufficient to find this optimal noise level. Furthermore, we demonstrate numerically and analytically the equivalence of the traditional mutual information approach and our autocorrelation approach for a range of model systems. We furthermore show how the level of added noise can be continuously adapted even to highly variable, unknown input signals via a feedback loop. Finally, we present evidence that adaptive stochastic resonance based on the autocorrelation of the sensor output may be a fundamental principle in neuronal systems.

Response of autaptic Hodgkin–Huxley neuron with noise to subthreshold sinusoidal signals

Abstract In this work, we investigated the response of a stochastic Hodgkin–Huxley (HH) neuron with an autapse to subthreshold sinusoidal signals. It is found that the autapse not only adjusts the stochastic responses, but also improves the detection of subthreshold signals. In the case of weak noise, the autapse facilitates the response of neuron to the subthreshold sinusoidal signals with a small parameter region in t d e l a y - ω space. The increased noise intensity enlarges this parameter region and increases the corresponding response frequency in such range. As the autaptic intensity increases, however, this parameter region shrunks. We also observed that there is an optimal range of the delay time of autapse, within which the stochastic HH neuron fires action potentials with high frequency. The corresponding response spike train for the optimal delay time is nearly a regular sequence with the interspike intervals approximated to the delay time. The current results reveal a novel resonance phenomenon facilitated by autapse, named autaptic delay-induced coherence resonance.

The Role of Coincidence-Detector Neurons in the Reliability and Precision of Subthreshold Signal Detection in Noise

Subthreshold signal detection is an important task for animal survival in complex environments, where noise increases both the external signal response and the spontaneous spiking of neurons. The mechanism by which neurons process the coding of signals is not well understood. Here, we propose that coincidence detection, one of the ways to describe the functionality of a single neural cell, can improve the reliability and the precision of signal detection through detection of presynaptic input synchrony. Using a simplified neuronal network model composed of dozens of integrate-and-fire neurons and a single coincidence-detector neuron, we show how the network reads out the subthreshold noisy signals reliably and precisely. We find suitable pairing parameters, the threshold and the detection time window of the coincidence-detector neuron, that optimize the precision and reliability of the neuron. Furthermore, it is observed that the refractory period induces an oscillation in the spontaneous firing, but the neuron can inhibit this activity and improve the reliability and precision further. In the case of intermediate intrinsic states of the input neuron, the network responds to the input more efficiently. These results present the critical link between spiking synchrony and noisy signal transfer, which is utilized in coincidence detection, resulting in enhancement of temporally sensitive coding scheme.

Fast random rewiring and strong connectivity impair subthreshold signal detection in excitable networks

We study dynamical responses in locally paced networks consisting of diffusively coupled excitable units with dynamically adjusted connectivity. It is shown that for weak subthreshold pacing, excessive or strong connectivity impairs the reliable response of a network to the stimulus. Fast random dynamic rewiring of the network also acts detrimentally on signal detection by enforcing a faster relaxation upon the paced unit. Our results indicate that efficient signal processing on excitable complex networks requires tight correspondence between the dynamics of connectivity and the dynamical processes taking place on the network. This, in turn, suggests the existence of ‘function-follows-form’ principles for systems described within this framework.

Application of Noise-Enhanced Detection of Subthreshold Signals for Communication Systems

A signal detection system using noise statistical processing is proposed. By approaching the problems of low voltage and high noise from miniaturization of a device from a stochastic point of view, a faint-signal receiving system that can effectively detect subthreshold and noise level signals has been developed. In addition, an alternative to statistical processing is proposed, and would be successfully implemented on a circuit. For the proposed signal detection method, the detection sensitivity was investigated using numerical simulation, and the detection sensitivity was sufficiently high to detect even a signal with a signal-to-inherent-noise ratio of -14dB. Thus, it is anticipated that the application of this system to an integrated circuit will have a significant impact on signal processing.

Noise-Enhanced Detection of Subthreshold Signals With Carbon Nanotubes

Electrical noise can help pulse-train signal detection at the nanolevel. Experiments on a single-walled carbon nanotube transistor confirmed that a threshold exhibited stochastic resonance (SR) for finite-variance and infinite-variance noise: small amounts of noise enhanced the nanotube detector's performance. The experiments used a carbon nanotube field-effect transistor to detect noisy subthreshold electrical signals. Two new SR hypothesis tests in the Appendix also confirmed the SR effect in the nanotube transistor. Three measures of detector performance showed the SR effect: Shannon's mutual information, the normalized correlation measure, and an inverted bit error rate compared the input and output discrete-time random sequences. The nanotube detector had a threshold-like input-output characteristic in its gate effect. It produced little current for subthreshold digital input voltages that fed the transistor's gate. Three types of synchronized white noise corrupted the subthreshold Bernoulli sequences that fed the detector. The Gaussian, the uniform, and the impulsive Cauchy noise combined with the random input voltage sequences to help the detector produce random output current sequences. The experiments observed the SR effect by measuring how well an output sequence matched its input sequence. Shannon's mutual information used histograms to estimate the probability densities and computed the entropies. The correlation measure was a scalar inner product of the input and output sequences. The inverted bit error rate computed how often the bits matched between the input and output sequences. The observed nanotube SR effect was robust: it persisted even when infinite-variance Cauchy noise corrupted the signal stream. Such noise-enhanced signal processing at the nanolevel promises applications to signal detection in wideband communication systems and biological and artificial neural networks

Stochastic resonance in the hippocampal CA3–CA1 model: a possible memory recall mechanism

Stochastic resonance (SR) in a hippocampal network model was investigated. The hippocampal model consists of two layers, CA3 and CA1. Pyramidal cells in CA3 are connected to pyramidal cells in CA1 through Schaffer collateral synapses. The CA3 network causes spontaneous irregular activity (broadband spectrum peaking at around 3 Hz), while the CA1 network does not. The activity of CA3 causes membrane potential fluctuations in CA1 pyramidal cells. The CA1 network also receives a subthreshold signal (2.5 or 50 Hz) through the perforant path (PP). The subthreshold PP signals can fire CA1 pyramidal cells in cooperation with the membrane potential fluctuations that work as noise. The firing of the CA1 network shows typical features of SR. When the frequency of the PP signal is in the gamma range (50 Hz), SR that takes place in the present model shows distinctive features. 50 Hz firing of CA1 pyramidal cells is modulated by the membrane potential fluctuations, resulting in bursts. Such burst firing in the CA1 network, which resembles the firing patterns observed in the real hippocampal CA1, improves performance of subthreshold signal detection in CA1. Moreover, memory embedded at Schaffer collateral synapses can be recalled by means of SR. When Schaffer collateral synapses in subregions of CA1 are augmented three-fold as a memory pattern, pyramidal cells in the subregions respond to the subthreshold PP signal due to SR, while pyramidal cells in the rest of CA1 do not fire.

Synaptic noise improves detection of subthreshold signals in hippocampal CA1 neurons.

Stochastic resonance (SR) is a phenomenon whereby the detection of a low-level signal is enhanced in a nonlinear system by the introduction of noise. Studies of the effects of SR in neurons have suggested that noise could play a prominent role in improving detection of small signals. Most experimental SR research has focused on the role of noise in sensory neurons using physiological stimuli. Computer simulations show that signal detection in hippocampal neurons is improved by the addition of physiological levels of noise applied extracellularly to synaptic inputs. These results were confirmed experimentally. We now report that endogenous noise sources can also improve signal detection. The noise source was generated by modulating the random synaptic activity on the apical dendrites of CA1 cells in rat hippocampal slices using subthreshold cathodic current. Intracellular recordings of CA1 cells showed that even small increases of synaptic noise are able to greatly improve the detection of an independent, synaptic, subthreshold stimulus as predicted by the simulations. The noise variance in the CA1 cell was compared with the resting variance and with variance changes caused by several endogenous noise sources. In all cases, the increased noise variance was well within the physiological range. These results were supplemented and analyzed with a CA1 computer model. The improved signal detection with small amounts of endogenous noise suggests that the diverse inputs to CA1 are able to improve detection of subthreshold synaptic signals and could provide a means to modulate detection of specific inputs in the hippocampus.

Stochastic focusing: fluctuation-enhanced sensitivity of intracellular regulation.

Many regulatory molecules are present in low copy numbers per cell so that significant random fluctuations emerge spontaneously. Because cell viability depends on precise regulation of key events, such signal noise has been thought to impose a threat that cells must carefully eliminate. However, the precision of control is also greatly affected by the regulatory mechanisms' capacity for sensitivity amplification. Here we show that even if signal noise reduces the capacity for sensitivity amplification of threshold mechanisms, the effect on realistic regulatory kinetics can be the opposite: stochastic focusing (SF). SF particularly exploits tails of probability distributions and can be formulated as conventional multistep sensitivity amplification where signal noise provides the degrees of freedom. When signal fluctuations are sufficiently rapid, effects of time correlations in signal-dependent rates are negligible and SF works just like conventional sensitivity amplification. This means that, quite counterintuitively, signal noise can reduce the uncertainty in regulated processes. SF is exemplified by standard hyperbolic inhibition, and all probability distributions for signal noise are first derived from underlying chemical master equations. The negative binomial is suggested as a paradigmatic distribution for intracellular kinetics, applicable to stochastic gene expression as well as simple systems with Michaelis-Menten degradation or positive feedback. SF resembles stochastic resonance in that noise facilitates signal detection in nonlinear systems, but stochastic resonance is related to how noise in threshold systems allows for detection of subthreshold signals and SF describes how fluctuations can make a gradual response mechanism work more like a threshold mechanism.

Synchronized oscillatory discharges of mitral/tufted cells with overlapping molecular receptive ranges in the rabbit olfactory bulb

In this work, we investigated the response of a stochastic Hodgkin–Huxley (HH) neuron with an autapse to subthreshold sinusoidal signals. It is found that the autapse not only adjusts the stochastic responses, but also improves the detection of subthreshold signals. In the case of weak noise, the autapse facilitates the response of neuron to the subthreshold sinusoidal signals with a small parameter region in tdelay-ω space. The increased noise intensity enlarges this parameter region and increases the corresponding response frequency in such range. As the autaptic intensity increases, however, this parameter region shrunks. We also observed that there is an optimal range of the delay time of autapse, within which the stochastic HH neuron fires action potentials with high frequency. The corresponding response spike train for the optimal delay time is nearly a regular sequence with the interspike intervals approximated to the delay time. The current results reveal a novel resonance phenomenon facilitated by autapse, named autaptic delay-induced coherence resonance.