Subthreshold Signal Research Articles

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.

Divide and conquer: resonance induced by competitive interactions

We study an Ising model in a network with disorder induced by the presence of both attractive and repulsive links and subjected to a periodic subthreshold signal. By means of numerical simulations and analytical calculations we give evidence that the global response of the system reaches a maximum value for a given fraction of the number of repulsive interactions. The model can represent a network of spin-like neurons with excitatory and inhibitory couplings, or a simple opinion spreading model. In this context, attractive/repulsive links represent friends and enemies. “Divide and Conquer” refers to the fact that in order to force a society to adopt a new point of view, it helps to break its homogeneity by fostering enmities amongst its members.

Stochastic Resonance in Continuous and Spiking Neuron Models With Levy Noise

Levy noise can help neurons detect faint or subthreshold signals. Levy noise extends standard Brownian noise to many types of impulsive jump-noise processes found in real and model neurons as well as in models of finance and other random phenomena. Two new theorems and the ItO calculus show that white Levy noise will benefit subthreshold neuronal signal detection if the noise process's scaled drift velocity falls inside an interval that depends on the threshold values. These results generalize earlier "forbidden interval" theorems of neuronal "stochastic resonance" (SR) or noise-injection benefits. Global and local Lipschitz conditions imply that additive white Levy noise can increase the mutual information or bit count of several feedback neuron models that obey a general stochastic differential equation (SDE). Simulation results show that the same noise benefits still occur for some infinite-variance stable Levy noise processes even though the theorems themselves apply only to finite-variance Levy noise. The proves the two ItO-theoretic lemmas that underlie the new Levy noise-benefit theorems.

Noise-driven synchronization of a FitzHugh–Nagumo ring with phase-repulsive coupling: A perspective from the system’s nonequilibrium potential

We study a one-dimensional array of N autonomous units with excitable FitzHugh–Nagumo dynamics coupled in phase-repulsive way to form a ring, and submitted to a common subthreshold harmonic signal and independent Gaussian white noises with a common intensity η . By varying η , two macroscopic regimes are observed. For some value of noise intensity, a transition from the rest state to an activated one–with almost half of the neurons excited forming an “...–activated–inhibited–activated–... ” structure along the ring–takes place. For larger values of η , the inverse transition is also observed, and both states alternate in a synchronized way with the signal. Moreover, measures of activation and coherent behavior become maximal for intermediate values of η . The origin of these collective effects is explained in terms of the system’s nonequilibrium potential. In particular, the levels of noise for activation and synchronization are theoretically estimated.

Detection of subthreshold pulses in neurons with channel noise

Neurons are subject to various kinds of noise. In addition to synaptic noise, the stochastic opening and closing of ion channels represents an intrinsic source of noise that affects the signal-processing properties of the neuron. We study the response of a stochastic Hodgkin-Huxley neuron to transient input subthreshold pulses. It is found that the average response time decreases but variance increases as the amplitude of channel noise increases. In the case of single-pulse detection, we show that channel noise enables one neuron to detect the subthreshold signals and an optimal membrane area (or channel noise intensity) exists for a single neuron to achieve optimal performance. However, the detection ability of a single neuron is limited by large errors. Here, we test a simple neuronal network that can enhance the pulse-detecting abilities of neurons and find that dozens of neurons can perfectly detect subthreshold pulses. The phenomenon of intrinsic stochastic resonance is also found at both the level of single neurons and the level of networks. At the network level, the detection ability of networks can be optimized for the number of neurons comprising the network.

Predicting single spikes and spike patterns with the Hindmarsh–Rose model

Most simple neuron models are only able to model traditional spiking behavior. As physiologists discover and classify different electrical phenotypes, computational neuroscientists become interested in using simple phenomenological models that can exhibit these different types of spiking patterns. The Hindmarsh-Rose model is a three-dimensional relaxation oscillator which can show both spiking and bursting patterns and has a chaotic regime. We test the predictive powers of the Hindmarsh-Rose model on two different test databases. We show that the Hindmarsh-Rose model can predict the spiking response of rat layer 5 neocortical pyramidal neurons on a stochastic input signal with a precision comparable to the best known spiking models. We also show that the Hindmarsh-Rose model can capture qualitatively the electrical footprints in a database of different types of neocortical interneurons. When the model parameters are fit from sub-threshold measurements only, the model still captures well the electrical phenotype, which suggests that the sub-threshold signals contain information about the firing patterns of the different neurons.

Enhanced Synchronization of Intercellular Calcium Oscillations by Noise Contaminated Signals

We consider the dynamics of locally coupled calcium oscillation systems, each cell is subjected to extracellular contaminated signal, which contains common sub-threshold signal and independent Gaussian noise. It is found that intermediate noise can enhance synchronized oscillations of calcium ions, where the frequency of noise-induced oscillations is matched with the one of sub-threshold external signal. We show that synchronization is enhanced as a result of the entrainment of external signal. Furthermore, the effect of coupling strength is considered. We find above-mentioned phenomenon exists only when coupling strength is very small. Our findings may exhibit that noise can enhance the detection of feeble external signal through the mechanism of synchronization of intercellular calcium ions.

Collective temporal coherence for subthreshold signal encoding on a stochastic small-world Hodgkin–Huxley neuronal network

We study the collective temporal coherence of a small-world network of coupled stochastic Hodgkin–Huxley neurons. Previous reports have shown that network coherence in response to a subthreshold periodic stimulus, thus subthreshold signal encoding, is maximal for a specific range of the fraction of randomly added shortcuts relative to all possible shortcuts, p, added to an initially locally connected network. We investigated this behavior further as a function of channel noise, stimulus frequency and coupling strength. We show that temporal coherence peaks when the frequency of the external stimulus matches that of the intrinsic subthreshold oscillations. We also find that large values of the channel noise, corresponding to small cell sizes, increases coherence for optimal values of the stimulus frequency and the topology parameter p. For smaller values of the channel noise, thus larger cell sizes, network coherence becomes insensitive to these parameters. Finally, the degree of coupling between neurons in the network modulates the sensitivity of coherence to topology, such that for stronger coupling the peak coherence is achieved with fewer added short cuts.

Evidence of stochastic resonance in the mating behavior of Nezara viridula (L.)

We investigate the role of the noise in the mating behavior between individuals of Nezara viridula (L.), by analyzing the temporal and spectral features of the non-pulsed type female calling song emitted by single individuals.We have measured the threshold level for the signal detection, by performing experiments with the calling signal at different intensities and analyzing the insect response by directionality tests performed on a group of male individuals. By using a sub-threshold signal and an acoustic Gaussian noise source, we have investigated the insect response for different levels of noise, finding behavioral activation for suitable noise intensities. In particular, the percentage of insects which react to the sub-threshold signal, shows a non-monotonic behavior, characterized by the presence of a maximum, for increasing levels of the noise intensity. This constructive interplay between external noise and calling signal is the signature of the non-dynamical stochastic resonance phenomenon. Finally, we describe the behavioral activation statistics by a soft threshold model which shows stochastic resonance. We find that the maximum of the ensemble average of the input-output cross-correlation occurs at a value of the noise intensity very close to that for which the behavioral response has a maximum.

Noise-invoked resonances near a homoclinic bifurcation in the glow discharge plasma

Stochastic resonance (SR) and coherence resonance (CR) have been studied experimentally in discharge plasmas close to a homoclinic bifurcation. For the SR phenomenon, it is observed that a superimposed subthreshold periodic signal can be recovered via stochastic modulations of the discharge voltage. Furthermore, it is realized that even in the absence of a subthreshold deterministic signal, the system dynamics can be recovered and optimized using noise. This effect is defined as CR in the literature. In the present experiments, induction of SR and CR is quantified using the absolute mean difference and normalized variance techniques, respectively.

Amplified Signal Response by Neuronal Diversity on Complex Networks

The effect of diversity on dynamics of coupled FitzHugh–Nagumo neurons on complex networks is numerically investigated, where each neuron is subjected to an external subthreshold signal. With the diversity the network is a mixture of excitable and oscillatory neurons, and the diversity is determined by the variance of the system's parameter. The complex network is constructed by randomly adding long-range connections (shortcuts) on a nearest-neighbouring coupled one-dimensional chain. Numerical results show that external signals are maximally magnified at an intermediate value of the diversity, as in the case of well-known stochastic resonance. Furthermore, the effects of the number of shortcuts and coupled strength on the diversity-induced phenomena are also discussed. These findings exhibit that the diversity may play a constructive role in response to external signal, and highlight the importance of the diversity on such complex networks.

Subthreshold auditory inputs to extrastriate visual neurons are responsive to parametric changes in stimulus quality: Sensory-specific versus non-specific coding

A new subthreshold form of multisensory processing has been recently identified that results from the convergence of suprathreshold excitatory inputs from one modality with subthreshold inputs from another. Because of the subthreshold nature of the second modality, descriptive measures of sensory features such as receptive field properties or location are not directly apparent as they are for traditional bimodal neurons. This raises the question of whether or not subthreshold signals actually convey sensory-specific receptive field information as seen in their bimodal counterparts, or if they represent non-specific effects such as arousal. The present experiment addressed this issue in visually-responsive neurons from the cat posterolateral lateral suprasylvian cortex (PLLS). Single-unit electrophysiological techniques were used to record neuronal responses to visual, auditory and combined visual–auditory stimuli while the intensity of stimulation in the subthreshold auditory modality was systematically altered. The results showed that subthreshold multisensory neurons were sensitive to changes in auditory stimulus intensity. These receptive field sensitivities are similar to those observed in bimodal neurons and thereby represent sensory-specific, not arousal-related responses. In addition, these results provide further support for the notion that multisensory processing occurs along a dynamic continuum of neuronal convergence patterns from bimodal to purely sensory-specific.

Functional Structure of the Mitral Cell Dendritic Tuft in the Rat Olfactory Bulb

The input-output transform performed by mitral cells, the principal projection neurons of the olfactory bulb, is one of the key factors in understanding olfaction. We used combined calcium and voltage imaging from the same neuron and computer modeling to investigate signal processing in the mitral cells, focusing on the glomerular dendritic tuft. The main finding was that the dendritic tuft functions as a single electrical compartment for subthreshold signals within the range of amplitudes detectable by voltage-sensitive dye recording. These evoked EPSPs had uniform characteristics throughout the glomerular tuft. The Ca(2+) transients associated with spatially uniform subthreshold synaptic potentials were comparable but not equal in amplitude in all regions. The average range of normalized amplitudes of the EPSP-driven Ca(2+) signals from different locations on dendritic branches in the glomerular tuft was relatively narrow and appeared to be independent of the dendritic surface-to-volume ratio. The computer simulations constrained by the imaging data indicated that a synchronized activation of approximately 100 synapses randomly distributed on tuft branches was sufficient to generate spatially homogenous EPSPs. This number of activated synapses is consistent with the data from anatomical studies. Furthermore, voltage attenuation of the EPSP along the primary dendrite at physiological temperature was weak compared with other cell types. In the model, weak attenuation of the EPSP along the primary dendrite could be accounted for by passive electrical properties of the mitral cell.

Suprathreshold Stochastic Resonance in Mechanoelectrical Transduction of Hair Cells

Haircell, the sound signal sensor in the auditory system, works in an environment full of noise. This noise has been demonstrated before to be a factor beneficial to the transfer of subthreshold weak signal. We further theoretically demonstrate that in suprathreshold signal situation, noise can still be a beneficial factor and enhance the haircell nonlinear transfer of suprathreshold signal. This reveals an additional constructive function of noise in hearing and provides theoretic foundation for relevant applications.

Charles bonnet syndrome associated with age-related macular degeneration.

The visual hallucinations in Charles Bonnet syndrome (CBS) are most commonly attributed to release phenomena that arise due to a paucity of sensory input to visual association areas of the cerebral cortex (Cogan's sensory deprivation theory of visual hallucinations). The authors wished to gain further insight into this mechanism behind CBS by examining a patient who experienced reversible visual hallucinations. The authors present a 77-year-old man with a history of severe age-related macular degeneration who experienced formed and moving visual hallucinations associated with CBS shortly after a retinal pigment epithelial detachment (RPED) in his right eye. His visual acuity had been reduced to 20/400 in the right eye. Visual acuity improved to 20/50 in the right eye after a series of Macugen injections led to RPED resolution as evidenced by optical coherence tomography and fluorescein angiography. The visual hallucinations abruptly ceased after this dramatic improvement in visual acuity. Reestablishment of high resolution visual input leads to a dramatic and almost immediate increase in the stimulus threshold of visual cortex cells as reflected by resolution of symptoms. The subthreshold signals that reach consciousness to produce visual hallucinations in CBS are thus immediately mitigated.

Differential Gating and Recruitment of P/Q-, N-, and R-Type Ca2+Channels in Hippocampal Mossy Fiber Boutons

Voltage-gated Ca2+ channels in presynaptic terminals initiate the Ca2+ inflow necessary for transmitter release. At a variety of synapses, multiple Ca2+ channel subtypes are involved in synaptic transmission and plasticity. However, it is unknown whether presynaptic Ca2+ channels differ in gating properties and whether they are differentially activated by action potentials or subthreshold voltage signals. We examined Ca2+ channels in hippocampal mossy fiber boutons (MFBs) by presynaptic recording, using the selective blockers omega-agatoxin IVa, omega-conotoxin GVIa, and SNX-482 to separate P/Q-, N-, and R-type components. Nonstationary fluctuation analysis combined with blocker application revealed a single MFB contained on average approximately 2000 channels, with 66% P/Q-, 26% N-, and 8% R-type channels. Whereas both P/Q-type and N-type Ca2+ channels showed high activation threshold and rapid activation and deactivation, R-type Ca2+ channels had a lower activation threshold and slower gating kinetics. To determine the efficacy of activation of different Ca2+ channel subtypes by physiologically relevant voltage waveforms, a six-state gating model reproducing the experimental observations was developed. Action potentials activated P/Q-type Ca2+ channels with high efficacy, whereas N- and R-type channels were activated less efficiently. Action potential broadening selectively recruited N- and R-type channels, leading to an equalization of the efficacy of channel activation. In contrast, subthreshold presynaptic events activated R-type channels more efficiently than P/Q- or N-type channels. In conclusion, single MFBs coexpress multiple types of Ca2+ channels, which are activated differentially by subthreshold and suprathreshold presynaptic voltage signals.

Stochastic resonance of a periodically driven neuron under non-Gaussian noise

We investigate the first-passage-time statistics of the integrate–fire neuron model driven by a sub-threshold harmonic signal superposed with a non-Gaussian noise. Here, we considered the noise as the result of a random multiplicative process displaced from the origin by an additive term. Such a mechanism generates a power-law distributed noise whose characteristic decay exponent can be finely tuned. We performed numerical simulations to analyze the influence of the noise non-Gaussian character on the stochastic resonance condition. We found that when the noise deviates from Gaussian statistics, the resonance condition occurs at weaker noise intensities, achieving a minimum at a finite value of the distribution function decay exponent. We discuss the possible relevance of this feature to the efficiency of the firing dynamics of biological neurons, as the present result indicates that neurons would require a lower noise level to detect a sub-threshold signal when its statistics departs from Gaussian.

Stochastic resonance in a parallel array of nonlinear dynamical elements

In an uncoupled parallel array of bistable dynamical elements subject to a common noisy subthreshold rectangular signal, the signal-to-noise ratio (SNR) gain can be improved by tuning the internally added array noise. A SNR gain above unity is observed in certain regions of the array noise intensity. The maximum SNR gain is obtained as the size of the array goes to infinity. This form of stochastic resonance (SR), i.e., array SR, is analytically described by introducing a quasi-stationary probability density model, yielding expressions for the transition probability and the stationary autocorrelation function. The analytical results have interpretative value for the mechanism of array SR, and may be of heuristic interest for applying array SR to related signal processing problems. Additionally, the analytical descriptions of an isolated bistable system agree well with numerical results of both conventional SR for a subthreshold rectangular input and residual SR for a slightly suprathreshold signal.

Effects of chemical synapses on the enhancement of signal propagation in coupled neurons near the canard regime

The response of three coupled FitzHugh-Nagumo neurons, under Gaussian white noise, to a subthreshold periodic signal is studied in this paper. By combining the canard dynamics, chemical coupling, and stochastic resonance together, the information transfer in this neural system is investigated. We find that chemical synaptic coupling is more efficient than the well-known linear coupling (gap junction) for local signal input, i.e., only one of the three neurons is subject to the periodic signal. This weak and local input is common in biological systems for the sake of low energy consumption.

Integration of sensory evidence in motion discrimination

To make perceptual judgments, the brain must decode the responses of sensory cortical neurons. The direction of visual motion is represented by the activity of direction-selective neurons. Because these neurons are often broadly tuned and their responses are inherently variable, the brain must appropriately integrate their responses to infer the direction of motion reliably. The optimal integration strategy is task dependent. For coarse direction discriminations, neurons tuned to the directions of interest provide the most reliable information, but for fine discriminations, neurons with preferred directions displaced away from the target directions are more informative. We measured coarse and fine direction discriminations with random-dot stimuli. Unknown to the observers, we added subthreshold motion signals of different directions to perturb the responses of different groups of direction-selective neurons. The pattern of biases induced by subthreshold signals of different directions indicates that subjects' choice behavior relied on the activity of neurons with a wide range of preferred directions. For coarse discriminations, observers' judgments were most strongly determined by neurons tuned to the target directions, but for fine discriminations, neurons with displaced preferred directions had the largest influence. We conclude that perceptual decisions rely on a population decoding strategy that takes the statistical reliability of sensory responses into account.