What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology

A unified model for the dynamics of driven ribbon with strain and magnetic order parameters

We develop a unified model to explain the dynamics of driven one dimensional ribbon for materials with strain and magnetic order parameters. We show that the model equations in their most general form explain several results on driven magnetostrictive metallic glass ribbons such as the period doubling route to chaos as a function of a dc magnetic field in the presence of a sinusoidal field, the quasiperiodic route to chaos as a function of the sinusoidal field for a fixed dc field, and induced and suppressed chaos in the presence of an additional low amplitude near resonant sinusoidal field. We also investigate the influence of a low amplitude near resonant field on the period doubling route. The model equations also exhibit symmetry restoring crisis with an exponent close to unity. The model can be adopted to explain certain results on magnetoelastic beam and martensitic ribbon under sinusoidal driving conditions. In the latter case, we find interesting dynamics of a periodic one orbit switching between two equivalent wells as a function of an ac magnetic field that eventually makes a direct transition to chaos under resonant driving condition. The model is also applicable to magnetomartensites and materials with two order parameters.

Synaptic variability in a cortical neuromorphic circuit

Variable behavior has been observed in several mechanisms found in biological neurons, resulting in changes in neural behavior that might be useful to capture in neuromorphic circuits. This paper presents a neuromorphic cortical neuron with synaptic neurotransmitter-release variability, which is designed to be used in neural networks as part of the Biomimetic Real-Time Cortex project. This neuron has been designed and simulated using carbon nanotube (CNT) transistors, which is one of several nanotechnologies under consideration to meet the challenges of scale presented by the cortex. Some research results suggest that some instances of variability are stochastic, while others indicate that some instances of variability are chaotic. In this paper, both possible sources of variability are considered by embedding either Gaussian noise or a chaotic signal into the neuromorphic or synaptic circuit and observing the simulation results. In order to embed chaotic behavior into the neuromorphic circuit, a chaotic signal generator circuit is presented, implemented with CNT transistors that could be embedded in the electronic neural circuit, and simulated using CNT SPICE models. The circuit uses a chaotic piecewise linear 1-D map implemented by switched-current circuits. The simulation results presented in this paper illustrate that neurotransmitter-release variability plays a beneficial role in the reliability of spike generation. In an examination of this reliability, the precision of spike timing in the CNT circuit simulations is found to be dependent on stimulus (postsynaptic potential) transients. Postsynaptic potentials with low neurotransmitter release variability or without neurotransmitter release variability produce imprecise spike trains, whereas postsynaptic potentials with high neurotransmitter-release variability produce spike trains with reproducible timing.

Biologically inspired information processing and synchronization in ensembles of non-identical threshold-potential nanostructures

Nanotechnology produces basic structures that show a significant variability in their individual physical properties. This experimental fact may constitute a serious limitation for most applications requiring nominally identical building blocks. On the other hand, biological diversity is found in most natural systems. We show that reliable information processing can be achieved with heterogeneous groups of non-identical nanostructures by using some conceptual schemes characteristic of biological networks (diversity, frequency-based signal processing, rate and rank order coding, and synchronization). To this end, we simulate the integrated response of an ensemble of single-electron transistors (SET) whose individual threshold potentials show a high variability. A particular experimental realization of a SET is a metal nanoparticle-based transistor that mimics biological spiking synapses and can be modeled as an integrate-and-fire oscillator. The different shape and size distributions of nanoparticles inherent to the nanoscale fabrication procedures result in a significant variability in the threshold potentials of the SET. The statistical distributions of the nanoparticle physical parameters are characterized by experimental average and distribution width values. We consider simple but general information processing schemes to draw conclusions that should be of relevance for other threshold-based nanostructures. Monte Carlo simulations show that ensembles of non-identical SET may show some advantages over ensembles of identical nanostructures concerning the processing of weak signals. The results obtained are also relevant for understanding the role of diversity in biophysical networks.

YURAGI Synthesis for Ultrasonic Human Brain Imaging

This paper proposes YURAGI synthesis for brain imaging under the skull. The advantage of the proposed method over conventional methods is that, using YURAGI synthesis, it is possible to obtain the effective results without image registration. Image registration is generally needed when more than two images are to be synthesized into one image. YURAGI synthesis does not need image registration; thus, its method is simpler than other methods that need image synthesis. The effectiveness of the proposed method was confirmed by comparing its error rate and accuracy with those of other methods. YURAGI leads the simple and energy-saving system with performing autoregulation. Autoregulation is utilized in many biological systems. In this study, YURAGI was applied to an ultrasound-based diagnostic medical imaging technique. The experimental results using YURAGI were superior to those using othermethods. Thus, YURAGI is useful for visualizing the human brain.1

1. This paper has been reviewed and accepted as a regular paper. The paper was invited and incorporated into the Special Issue on Advances in Fuzzy Inference and its Related Techniques.

Stochastic facilitation of artificial tactile sensation in primates

Artificial sensation via electrical or optical stimulation of brain sensory areas offers a promising treatment for sensory deficits. For a brain-machine-brain interface, such artificial sensation conveys feedback signals from a sensorized prosthetic limb. The ways neural tissue can be stimulated to evoke artificial sensation and the parameter space of such stimulation, however, remain largely unexplored. Here we investigated whether stochastic facilitation (SF) could enhance an artificial tactile sensation produced by intracortical microstimulation (ICMS). Two rhesus monkeys learned to use a virtual hand, which they moved with a joystick, to explore virtual objects on a computer screen. They sought an object associated with a particular artificial texture (AT) signaled by a periodic ICMS pattern delivered to the primary somatosensory cortex (S1) through a pair of implanted electrodes. During each behavioral trial, aperiodic ICMS (i.e., noise) of randomly chosen amplitude was delivered to S1 through another electrode pair implanted 1 mm away from the site of AT delivery. Whereas high-amplitude noise worsened AT detection, moderate noise clearly improved the detection of weak signals, significantly raising the proportion of correct trials. These findings suggest that SF could be used to enhance prosthetic sensation.

Identifying positive roles for endogenous stochastic noise during computation in neural systems

Information processing in nonlinear systems can sometimes be enhanced by the presence of stochastic fluctuations, or noise. Although the electrical properties of neurons and synapses are known to be influenced by intrinsic stochastic variability, it remains an open question as to whether living systems exploit this noise during neuronal information processing. This is despite various forms of noise-enhanced processing, such as classical stochastic resonance, having been observed in mathematical models of neural systems and in data acquired experimentally. We recently argued that advancing our understanding of the potential roles of random noise in assisting neuronal information processing will require specific focus on a concrete hypothesis about the computational roles of a specific neural system that can then be tested experimentally using signals and metrics relevant to the hypothesis. In this invited symposium paper, we argue why most existing approaches to studying stochastic resonance based on classical definitions and methods are highly limited in their applicability, since they impose an implied computational hypothesis that may have little relevance for real neurobiological systems.

Hierarchic stochastic modelling applied to intracellular Ca(2+) signals

Important biological processes like cell signalling and gene expression have noisy components and are very complex at the same time. Mathematical analysis of such systems has often been limited to the study of isolated subsystems, or approximations are used that are difficult to justify. Here we extend a recently published method (Thurley and Falcke, PNAS 2011) which is formulated in observable system configurations instead of molecular transitions. This reduces the number of system states by several orders of magnitude and avoids fitting of kinetic parameters. The method is applied to Ca(2+) signalling. Ca(2+) is a ubiquitous second messenger transmitting information by stochastic sequences of concentration spikes, which arise by coupling of subcellular Ca(2+) release events (puffs). We derive analytical expressions for a mechanistic Ca(2+) model, based on recent data from live cell imaging, and calculate Ca(2+) spike statistics in dependence on cellular parameters like stimulus strength or number of Ca(2+) channels. The new approach substantiates a generic Ca(2+) model, which is a very convenient way to simulate Ca(2+) spike sequences with correct spiking statistics.

Fluctuating noise drives Brownian transport

The transport properties of Brownian ratchet were studied in the presence of stochastic intensity noise in both overdamped and underdamped regimes. In the overdamped case, an analytical solution using the matrix-continued fraction method revealed the existence of a maximum current when the noise intensity fluctuates on intermediate timescale regions. Similar effects were observed for the underdamped case by Monte Carlo simulations. The optimal time-correlation for Brownian transport coincided with the experimentally observed time-correlation of the extrinsic noise in Escherichia coli gene expression and implied the importance of environmental noise for molecular mechanisms.

Unreliable evoked responses in autism

Autism has been described as a disorder of general neural processing, but the particular processing characteristics that might be abnormal in autism have mostly remained obscure. Here, we present evidence of one such characteristic: poor evoked response reliability. We compared cortical response amplitude and reliability (consistency across trials) in visual, auditory, and somatosensory cortices of high-functioning individuals with autism and controls. Mean response amplitudes were statistically indistinguishable across groups, yet trial-by-trial response reliability was significantly weaker in autism, yielding smaller signal-to-noise ratios in all sensory systems. Response reliability differences were evident only in evoked cortical responses and not in ongoing resting-state activity. These findings reveal that abnormally unreliable cortical responses, even to elementary nonsocial sensory stimuli, may represent a fundamental physiological alteration of neural processing in autism. The results motivate a critical expansion of autism research to determine whether (and how) basic neural processing properties such as reliability, plasticity, and adaptation/habituation are altered in autism.

Effect of the heterogeneous neuron and information transmission delay on stochastic resonance of neuronal networks

We study the effect of heterogeneous neuron and information transmission delay on stochastic resonance of scale-free neuronal networks. For this purpose, we introduce the heterogeneity to the specified neuron with the highest degree. It is shown that in the absence of delay, an intermediate noise level can optimally assist spike firings of collective neurons so as to achieve stochastic resonance on scale-free neuronal networks for small and intermediate α(h), which plays a heterogeneous role. Maxima of stochastic resonance measure are enhanced as α(h) increases, which implies that the heterogeneity can improve stochastic resonance. However, as α(h) is beyond a certain large value, no obvious stochastic resonance can be observed. If the information transmission delay is introduced to neuronal networks, stochastic resonance is dramatically affected. In particular, the tuned information transmission delay can induce multiple stochastic resonance, which can be manifested as well-expressed maximum in the measure for stochastic resonance, appearing every multiple of one half of the subthreshold stimulus period. Furthermore, we can observe that stochastic resonance at odd multiple of one half of the subthreshold stimulus period is subharmonic, as opposed to the case of even multiple of one half of the subthreshold stimulus period. More interestingly, multiple stochastic resonance can also be improved by the suitable heterogeneous neuron. Presented results can provide good insights into the understanding of the heterogeneous neuron and information transmission delay on realistic neuronal networks.

Fractal fluctuations in quiet standing predict the use of mechanical information for haptic perception

Movement science has traditionally understood high-dimensional fluctuations as either antithetical or irrelevant to low-dimensional control. However, fluctuations incident to changeful, sometimes unpredictable stimulation must somehow reshape low-dimensional aspects of control through perception. The movement system's fluctuations may reflect cascade dynamics in which many-sized events interact nonlinearly across many scales. Cascades yield fractal fluctuations, and fractality of fluctuations may provide a window on the interactions across scale supporting perceptual processes. To test these ideas, we asked adult human participants to judge whole or partial length for unseen rods (with and without added masses). The participants' only experience with the objects came from supporting them across their shoulders during quiet standing. First, the degree of fractal temporal correlations in trial-by-trial series of planar Euclidean displacements in center of pressure (COP) significantly improved prediction of subsequent trial-by-trial judgments, above and beyond prediction by traditional predictors of haptic perception and conventional measures of COP variability. Second, comparison with linear surrogate data indicated the presence of nonlinear interactions across scale in these time series. These results demonstrate that high-dimensional fluctuations may serve a crucial role in the cascade dynamics supporting apparently low-dimensional control strategies.

Spectrum analysis in postural strategy on static tests in a healthy population

INTRODUCTION AND OBJECTIVES

The power spectral density can be used to find a hidden behaviour in a physical system. We studied postural behaviour in a healthy population, by means of power spectral density, and compared it with a situation of complete immobility.

METHODS

A group of healthy volunteers carried out the modified Clinical Test for Sensory Interaction in Balance. A fibre optic gyroscope device was used to record the data. We compared the power spectral density in these tests and with another recording where the device was motionless. We looked for significant differences using the ANOVA test.

RESULTS

A power peak appeared at 8.4 Hz in all static tests. The static group showed significant differences with all the other tests. We found no statistically significant differences when visual input discriminated between 2 tests. There were statistically significant differences for the proprioceptive input.

CONCLUSIONS

Brief movements are needed to maintain the quiet stance. The distinctive feature of these movements is a fundamental frequency at 1.4 Hz and a harmonic frequency at 8.4 Hz. Proprioceptive input is essential for balance.

Dynamics of strain bifurcations in a magnetostrictive ribbon

We develop a coupled nonlinear oscillator model involving magnetization and strain to explain several experimentally observed dynamical features exhibited by forced magnetostrictive ribbon. Here we show that the model recovers the observed period-doubling route to chaos as function of the dc field for a fixed ac field and quasiperiodic route to chaos as a function of the ac field, keeping the dc field constant. The model also predicts induced and suppressed chaos under the influence of an additional small-amplitude near-resonant ac field. Our analysis suggests rich dynamics in coupled order-parameter systems such as magnetomartensitic and magnetoelectric materials.

Weak temporal signals can synchronize and accelerate the transition dynamics of biopolymers under tension

In addition to thermal noise, which is essential to promote conformational transitions in biopolymers, the cellular environment is replete with a spectrum of athermal fluctuations that are produced from a plethora of active processes. To understand the effect of athermal noise on biological processes, we studied how a small oscillatory force affects the thermally induced folding and unfolding transition of an RNA hairpin, whose response to constant tension had been investigated extensively in both theory and experiments. Strikingly, our molecular simulations performed under overdamped condition show that even at a high (low) tension that renders the hairpin (un)folding improbable, a weak external oscillatory force at a certain frequency can synchronously enhance the transition dynamics of RNA hairpin and increase the mean transition rate. Furthermore, the RNA dynamics can still discriminate a signal with resonance frequency even when the signal is mixed among other signals with nonresonant frequencies. In fact, our computational demonstration of thermally induced resonance in RNA hairpin dynamics is a direct realization of the phenomena called stochastic resonance and resonant activation. Our study, amenable to experimental tests using optical tweezers, is of great significance to the folding of biopolymers in vivo that are subject to the broad spectrum of cellular noises.

A brief history of the resting state: the Washington University perspective

We present a history of the concepts and developments that have led us to focus on the resting state as an object of study. We then discuss resting state research performed in our laboratory since 2005 with an emphasis on papers of particular interest.

Good vibrations: oscillatory phase shapes perception

In the current study, we provide compelling evidence to answer the long-standing question whether perception is continuous or periodic. Spontaneous brain oscillations are assumed to be the underlying mechanism of periodic perception. Depending on the phase angle of the oscillations, an identical stimulus results in different perceptual outcomes. Past results, however, can only account for a correlation of perception with the phase of the ongoing brain oscillations. Therefore, it is desirable to demonstrate a causal relation between phase and perception. One way to address this question is to entrain spontaneous brain oscillations by applying an external oscillation and then demonstrate behavioral consequences of this oscillation. We conducted an auditory detection experiment with humans, recorded the electroencephalogram (EEG) concurrently and simultaneously applied oscillating transcranial direct current stimulation at 10Hz (α-tDCS). Our approach revealed that detection thresholds were dependent on the phase of the oscillation that was entrained by α-tDCS. This behavioral effect was accompanied by an electrophysiological effect: α-power was enhanced after α-tDCS as compared to a pre-stimulation period. By showing a causal relation between phase and perception, our results extend findings of previous studies that were only able to demonstrate a correlation. We found that manipulation of the phase resulted in different detection thresholds, which supports the notion that perception can be periodically modulated by oscillatory processes. This demonstrates that tDCS can serve as a tool in neuroscience to extend the knowledge of the functional significance of brain oscillations.

Simple, fast and accurate implementation of the diffusion approximation algorithm for stochastic ion channels with multiple states

BACKGROUND

The phenomena that emerge from the interaction of the stochastic opening and closing of ion channels (channel noise) with the non-linear neural dynamics are essential to our understanding of the operation of the nervous system. The effects that channel noise can have on neural dynamics are generally studied using numerical simulations of stochastic models. Algorithms based on discrete Markov Chains (MC) seem to be the most reliable and trustworthy, but even optimized algorithms come with a non-negligible computational cost. Diffusion Approximation (DA) methods use Stochastic Differential Equations (SDE) to approximate the behavior of a number of MCs, considerably speeding up simulation times. However, model comparisons have suggested that DA methods did not lead to the same results as in MC modeling in terms of channel noise statistics and effects on excitability. Recently, it was shown that the difference arose because MCs were modeled with coupled gating particles, while the DA was modeled using uncoupled gating particles. Implementations of DA with coupled particles, in the context of a specific kinetic scheme, yielded similar results to MC. However, it remained unclear how to generalize these implementations to different kinetic schemes, or whether they were faster than MC algorithms. Additionally, a steady state approximation was used for the stochastic terms, which, as we show here, can introduce significant inaccuracies.

MAIN CONTRIBUTIONS

We derived the SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable--allowing an easy, transparent and efficient DA implementation, avoiding unnecessary approximations. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods, except when short time steps or low channel numbers were used.

Stochastic dynamic causal modelling of fMRI data: should we care about neural noise?

Dynamic causal modelling (DCM) was introduced to study the effective connectivity among brain regions using neuroimaging data. Until recently, DCM relied on deterministic models of distributed neuronal responses to external perturbation (e.g., sensory stimulation or task demands). However, accounting for stochastic fluctuations in neuronal activity and their interaction with task-specific processes may be of particular importance for studying state-dependent interactions. Furthermore, allowing for random neuronal fluctuations may render DCM more robust to model misspecification and finesse problems with network identification. In this article, we examine stochastic dynamic causal models (sDCM) in relation to their deterministic counterparts (dDCM) and highlight questions that can only be addressed with sDCM. We also compare the network identification performance of deterministic and stochastic DCM, using Monte Carlo simulations and an empirical case study of absence epilepsy. For example, our results demonstrate that stochastic DCM can exploit the modelling of neural noise to discriminate between direct and mediated connections. We conclude with a discussion of the added value and limitations of sDCM, in relation to its deterministic homologue.

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.

The modulation of BOLD variability between cognitive states varies by age and processing speed

Increasing evidence suggests that brain variability plays a number of important functional roles for neural systems. However, the relationship between brain variability and changing cognitive demands remains understudied. In the current study, we demonstrate experimental condition-based modulation in brain variability using functional magnetic resonance imaging. Within a sample of healthy younger and older adults, we found that blood oxygen level-dependent signal variability was an effective discriminator between fixation and external cognitive demand. Across a number of regions, brain variability increased broadly on task compared with fixation, particularly in younger and faster performing adults. Conversely, older and slower performing adults exhibited fewer changes in brain variability within and across experimental conditions and brain regions, indicating a reduction in variability-based neural specificity. Increases in brain variability on task may represent a more complex neural system capable of greater dynamic range between brain states, as well as an enhanced ability to efficiently process varying and unexpected external stimuli. The current results help establish the developmental and performance correlates of state-to-state brain variability-based transitions and offer a new line of inquiry in the study of rest versus task modes in the human brain.

Spike-interval triggered averaging reveals a quasi-periodic spiking alternative for stochastic resonance in catfish electroreceptors

Catfish detect and identify invisible prey by sensing their ultra-weak electric fields with electroreceptors. Any neuron that deals with small-amplitude input has to overcome sensitivity limitations arising from inherent threshold non-linearities in spike-generation mechanisms. Many sensory cells solve this issue with stochastic resonance, in which a moderate amount of intrinsic noise causes irregular spontaneous spiking activity with a probability that is modulated by the input signal. Here we show that catfish electroreceptors have adopted a fundamentally different strategy. Using a reverse correlation technique in which we take spike interval durations into account, we show that the electroreceptors generate a supra-threshold bias current that results in quasi-periodically produced spikes. In this regime stimuli modulate the interval between successive spikes rather than the instantaneous probability for a spike. This alternative for stochastic resonance combines threshold-free sensitivity for weak stimuli with similar sensitivity for excitations and inhibitions based on single interspike intervals.

Noisy galvanic vestibular stimulation promotes GABA release in the substantia nigra and improves locomotion in hemiparkinsonian rats

Background

The vestibular system is connected to spinal, cerebellar and cerebral motor control structures and can be selectively activated with external electrodes. The resulting sensation of disturbed balance can be avoided by using stochastic stimulation patterns. Adding noise to the nervous system sometimes improves function. Small clinical trials suggest that stochastic vestibular stimulation (SVS) may improve symptoms in Parkinson's disease. We have investigated this claim and possible mechanisms using the 6-hydroxydopamine (6-OHDA) hemilesion model of Parkinson's disease.

Methodology/Principal Findings

Animals were tested in the accelerating rod test and the Montoya staircase test of skilled forelimb use. In 6-OHDA hemilesioned animals, SVS improved rod performance by 56±11 s. At group level L-DOPA treatment had no effect, but positive responders improved time on rod by 60±19 s. Skilled forelimb use was not altered by SVS. To investigate how SVS may influence basal ganglia network activity, intracerebral microdialysis was employed in four regions of interest during and after SVS. In presence of the γ-amino buturic acid (GABA) transporter inhibitor NNC 711, SVS induced an increase in GABA to 150±15% of baseline in the substantia nigra (SN) of unlesioned animals, but had no effect in the pedunculopontine nucleus (PPN), the striatum or the ventromedial thalamus (VM). Dopamine release remained stable in all areas, as did GABA and amine concentrations in the SN of unstimulated controls. Following SVS, a sustained increase in GABA concentrations was observed in the ipsilesional, but not in the contralesional SN of 6-OHDA hemilesioned rats. In contrast, L-DOPA treatment produced a similar increase of GABA in the ipsi- and contra-lesional SN.

Conclusions/Significance

SVS improves rod performance in a rat model of Parkinson's disease, possibly by increasing nigral GABA release in a dopamine independent way. We propose that SVS could be useful for treating symptoms of Parkinson's disease.

Sleep and plasticity

While there is ample agreement that the cognitive role of sleep is explained by sleep-dependent synaptic changes, consensus is yet to be established as to the nature of these changes. Some researchers believe that sleep promotes global synaptic downscaling, leading to a non-Hebbian reset of synaptic weights that is putatively necessary for the acquisition of new memories during ensuing waking. Other investigators propose that sleep also triggers experience-dependent, Hebbian synaptic upscaling able to consolidate recently acquired memories. Here, I review the molecular and physiological evidence supporting these views, with an emphasis on the calcium signaling pathway. I argue that the available data are consistent with sleep promoting experience-dependent synaptic embossing, understood as the simultaneous non-Hebbian downscaling and Hebbian upscaling of separate but complementary sets of synapses, heterogeneously activated at the time of memory encoding and therefore differentially affected by sleep.

Emergent properties of interacting populations of spiking neurons

Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system. Here, we present and discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks is faithfully reflected by a set of non-linear rate equations, describing all interactions on the population level. These equations are similar in structure to Lotka-Volterra equations, well known by their use in modeling predator-prey relations in population biology, but abundant applications to economic theory have also been described. We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of interacting neuronal populations.

Far from equilibrium percolation, stochastic and shape resonances in the physics of life

Key physical concepts, relevant for the cross-fertilization between condensed matter physics and the physics of life seen as a collective phenomenon in a system out-of-equilibrium, are discussed. The onset of life can be driven by: (a) the critical fluctuations at the protonic percolation threshold in membrane transport; (b) the stochastic resonance in biological systems, a mechanism that can exploit external and self-generated noise in order to gain efficiency in signal processing; and (c) the shape resonance (or Fano resonance or Feshbach resonance) in the association and dissociation processes of bio-molecules (a quantum mechanism that could play a key role to establish a macroscopic quantum coherence in the cell).

Fisher-information condition for enhanced signal detection via stochastic resonance

Various situations where a signal is enhanced by noise through stochastic resonance are now known. This paper contributes to determining general conditions under which improvement by noise can be a priori decided as feasible or not. We focus on the detection of a known signal in additive white noise. Under the assumptions of a weak signal and a sufficiently large sample size, it is proved, with an inequality based on the Fisher information, that improvement by adding noise is never possible, generically, in these conditions. However, under less restrictive conditions, an example of signal detection is shown with favorable action of adding noise.

Attention induces conservative subjective biases in visual perception

Although attention usually enhances perceptual sensitivity, we found that it can also lead to relatively conservative detection biases and lower visibility ratings in discrimination tasks. These results are explained by a model in which attention reduces the trial-by-trial variability of the perceptual signal, and we determined how this model led to the observed behavior. These findings may partially reflect our impression of 'seeing' the whole visual scene despite our limited processing capacity outside of the focus of attention.

Voltage fluctuations in neurons: signal or noise?

Noise and variability are fundamental companions to ion channels and synapses and thus inescapable elements of brain function. The overriding unresolved issue is to what extent noise distorts and limits signaling on one hand and at the same time constitutes a crucial and fundamental enrichment that allows and facilitates complex adaptive behavior in an unpredictable world. Here we review the growing experimental evidence that functional network activity is associated with intense fluctuations in membrane potential and spike timing. We trace origins and consequences of noise and variability. Finally, we discuss noise-free neuronal signaling and detrimental and beneficial forms of noise in large-scale functional neural networks. Evidence that noise and variability in some cases go hand in hand with behavioral variability and increase behavioral choice, richness, and adaptability opens new avenues for future studies.

Noise amplification in human tumor suppression following gamma irradiation

The influence of noise on oscillatory motion is a subject of permanent interest, both for fundamental and practical reasons. Cells respond properly to external stimuli by using noisy systems. We have clarified the effect of intrinsic noise on the dynamics in the human cancer cells following gamma irradiation. It is shown that the large amplification and increasing mutual information with delay are due to coherence resonance. Furthermore, frequency domain analysis is used to study the mechanisms.

Physiological Signal Variability in hMT+ Reflects Performance on a Direction Discrimination Task

Our ability to perceive visual motion is critically dependent on the human motion complex (hMT+) in the dorsal visual stream. Extensive electrophysiological research in the monkey equivalent of this region has demonstrated how neuronal populations code for properties such as speed and direction, and that neurometric functions relate to psychometric functions within the individual monkey. In humans, the physiological correlates of inter-individual perceptual differences are still largely unknown. To address this question, we used functional magnetic resonance imaging (fMRI) while participants viewed translational motion in different directions, and we measured thresholds for direction discrimination of moving stimuli in a separate psychophysics experiment. After determining hMT+ in each participant with a functional localizer, we were able to decode the different directions of visual motion from it using pattern classification (PC). We also characterized the variability of fMRI signal in hMT+ during stimulus and rest periods with a generative model. Relating perceptual performance to physiology, individual direction discrimination thresholds were significantly correlated with the variability measure in hMT+, but not with PC accuracies. Individual differences in PC accuracy were driven by non-physiological sources of noise, such as head-movement, which makes this method a poor tool to investigate inter-individual differences. In contrast, variability analysis of the fMRI signal was robust to non-physiological noise, and variability characteristics in hMT+ correlated with psychophysical thresholds in the individual participants. Higher levels of fMRI signal variability compared to rest correlated with lower discrimination thresholds. This result is in line with theories on stochastic resonance in the context of neuronal populations, which suggest that endogenous or exogenous noise can increase the sensitivity of neuronal populations to incoming signals.

The locomotor central pattern generator of the rat spinal cord in vitro is optimally activated by noisy dorsal root waveforms

The spinal cord contains an intrinsic locomotor program driven by a central pattern generator that rhythmically activates flexor and extensor limb motor pools. Although long-lasting locomotor activity can be generated pharmacologically, trains of afferent stimuli trigger only few locomotor cycles. The present study investigated whether a new electrical stimulation protocol (termed FL istim) could elicit long-lasting fictive locomotion (FL) in the rat spinal cord in vitro. Thus, after first inducing FL by bath application of N-methyl-d-aspartate and serotonin, the recorded waveform obtained from a lumbar ventral root was digitized and then applied to either a lumbar dorsal root or the cauda equina following washout of pharmacological agents. Two FL istim cycles were the threshold input to evoke an episode of FL from ventral roots. Longer cycles (up to 1 min) induced sustained FL (up to 1 min) with stereotyped periodicity (2.2 ± 0.5 s), despite changing frequency (2–4 s) or cycle amplitude of FL istim. Gradual filtering out of the noise from FL istim trace concomitantly decreased the efficiency of FL so that stimulation with equivalent pure sinusoids produced asynchronous, irregular discharges only that could not be converted to FL by adding spontaneous basal activity. This study is the first demonstration that epochs of rhythmic locomotor-like oscillations applied to a dorsal root represent an efficient stimulus to evoke FL as long as they contain the electrophysiological noise produced within FL cycles. These observations suggest novel strategies to improve the efficiency of electrical stimulation delivered by clinical devices for neurorehabilitation after spinal injury.

Suprathreshold stochastic resonance in neural processing tuned by correlation

Suprathreshold stochastic resonance (SSR) is examined in the context of integrate-and-fire neurons, with an emphasis on the role of correlation in the neuronal firing. We employed a model based on a network of spiking neurons which received synaptic inputs modeled by Poisson processes stimulated by a stepped input signal. The smoothed ensemble firing rate provided an output signal, and the mutual information between this signal and the input was calculated for networks with different noise levels and different numbers of neurons. It was found that an SSR effect was present in this context. We then examined a more biophysically plausible scenario where the noise was not controlled directly, but instead was tuned by the correlation between the inputs. The SSR effect remained present in this scenario with nonzero noise providing improved information transmission, and it was found that negative correlation between the inputs was optimal. Finally, an examination of SSR in the context of this model revealed its connection with more traditional stochastic resonance and showed a trade-off between supratheshold and subthreshold components. We discuss these results in the context of existing empirical evidence concerning correlations in neuronal firing.

The importance of being variable

New work suggests that blood oxygen level-dependent (BOLD) signal variability can be a much more powerful index of human age than mean activation, and that older brains are actually less variable than younger brains. However, little is known of how BOLD variability and task performance may relate. In the current study, we examined BOLD variability in relation to age, and reaction time speed and consistency in healthy younger (20-30 years) and older (56-85 years) adults on three cognitive tasks (perceptual matching, attentional cueing, and delayed match-to-sample). Results indicated that younger, faster, and more consistent performers exhibited significantly higher brain variability across tasks, and showed greater variability-based regional differentiation compared with older, poorer-performing adults. Also, when we compared brain variability- and typical mean-based effects, the respective spatial patterns were essentially orthogonal across brain measures, and any regions that did overlap were largely opposite in directionality of effect. These findings help establish the functional basis of BOLD variability, and further support the statistical and spatial differentiation between BOLD variability and BOLD mean. We thus argue that the precise nature of relations between aging, cognition, and brain function is underappreciated by using mean-based brain measures exclusively.

Vibration-enhanced posture stabilization achieved by tactile supplementation: may blind individuals get extra benefits?

Diminished balance ability poses a serious health risk due to the increased likelihood of falling, and impaired postural stability is significantly associated with blindness and poor vision. Noise stimulation (by improving the detection of sub-threshold somatosensory information) and tactile supplementation (i.e., additional haptic information provided by an external contact surface) have been shown to improve the performance of the postural control system. Moreover, vibratory noise added to the source of tactile supplementation (e.g., applied to a surface that the fingertip touches) has been shown to enhance balance stability more effectively than tactile supplementation alone. In view of the above findings, in addition to the well established consensus that blind subjects show superior abilities in the use of tactile information, we hypothesized that blind subjects may take extra benefits from the vibratory noise added to the tactile supplementation and hence show greater improvements in postural stability than those observed for sighted subjects. If confirmed, this hypothesis may lay the foundation for the development of noise-based assistive devices (e.g., canes, walking sticks) for improving somatosensation and hence prevent falls in blind individuals.

Interaction between connectivity and oscillatory currents in a heterogeneous neuronal network

Intrinsic oscillations are thought to play important and distinct roles in cognitive processes across nearly all regions of the brain. Their specific roles are highly dependent on their properties: low-frequency θ is thought to be important in the gating of cognitive processes, while high-frequency γ is believed to be essential for binding and spike-timing-dependent plasticity. We investigated the role of an oscillatory drive for pattern formation of heterogeneous networks. Network heterogeneities were implemented as network regions having increased connectivity as compared to the rest of the network. We varied the properties of the oscillatory drive as well as network connectivity. We observed that the disparity in spatiotemporal patterning of activity between the structurally enhanced region and rest of the network was highly dependent on the frequency and amplitude of the oscillatory drive as well as network connectivity, generally favoring bigger enhancement of activity for high-frequency oscillations and phase locking with moderate enhancement of activity for lower-frequency oscillations. Thus, these results indicate that the specific role of the observed oscillations may depend on their dynamical interactions with the heterogeneous network.

Delay and diversity-induced synchronization transitions in a small-world neuronal network

The synchronized behaviors of a noisy small-world neuronal network with delay and diversity is numerically studied by calculating a synchronization measure and plotting firing pattern. We show that delay in the information transmission can induce fruitful synchronization transitions, including transition from phase locking to antiphase synchronization, and transition from antiphase synchronization to complete synchronization. Furthermore, the delay-induced complete synchronization can be changed by diversity, which causes the oscillatory-like transition between antiphase synchronization and complete synchronization.

Noise benefit in prepulse inhibition of the acoustic startle reflex

RATIONALE

Under some conditions, external sensory noise enhances cognitive functions, a phenomenon possibly involving stochastic resonance and/or enhanced central dopamine transmission. Prepulse inhibition (PPI) of the startle reflex is a robust measure of sensorimotor gating and can be modulated by activity in the cortex and basal ganglia, including the central dopamine pathways.

OBJECTIVES

Previous empirical studies suggest a differential effect of acoustic noise in normal children and children with attention-deficit hyperactivity disorder (ADHD). This study investigated the effect of acoustic noise on PPI and if dopamine transmission interacts with acoustic noise effects in a rat ADHD model.

METHODS

The effect of background acoustic noise on acoustic startle response and PPI were measured with a constant prepulse to background noise ratio of 9 dB(A). Spontaneously hypertensive (SH) rats were used as the ADHD model and compared with Wistar and Sprague-Dawley rats. Microdialysis, methylphenidate treatment and 6-OHDA lesions were used to investigate interaction with dopamine transmission.

RESULTS

Background noise facilitated PPI differently in SH rats and controls. The prefrontal cortex in SH rats had low basal dopamine concentrations, a high DOPAC/dopamine ratio and blunted dopamine release during PPI testing. Methylphenidate had small, but strain-specific, effects on startle and PPI. Bilateral 6-hydroxydopamine lesions did not alter startle or PPI.

CONCLUSIONS

Prefrontal dopamine transmission is altered in SH rats during the sensorimotor gating task of PPI of the acoustic startle, indicating increased dopamine reuptake in this ADHD rat model. We propose that noise benefit could be explored as a non-pharmacological alternative for treating neuropsychiatric disorders.

Improving balance function using vestibular stochastic resonance: optimizing stimulus characteristics

Stochastic resonance (SR) is a phenomenon whereby the response of a non-linear system to a weak periodic input signal is optimized by the presence of a particular non-zero level of noise. Stochastic resonance using imperceptible stochastic vestibular electrical stimulation, when applied to normal young and elderly subjects, has been shown to significantly improve ocular stabilization reflexes in response to whole-body tilt; improved balance performance during postural disturbances and optimize covariance between the weak input periodic signals introduced via venous blood pressure receptors and the heart rate responses. In our study, 15 subjects stood on a compliant surface with their eyes closed. They were given low-amplitude binaural bipolar stochastic electrical stimulation of the vestibular organs in two frequency ranges of 1-2 and 0-30 Hz over the amplitude range of 0 to ±700 μA. Subjects were instructed to maintain an upright stance during 43-s trials, which consisted of baseline (zero amplitude) and stimulation (non-zero amplitude) periods. Measures of stability of the head and trunk using inertial motion unit sensors attached to these segments and the whole body using a force plate were measured and quantified in the mediolateral plane. Using a multivariate optimization criterion, our results show that the low levels of vestibular stimulation given to the vestibular organs improved balance performance in normal healthy subjects in the range of 5-26% consistent with the stochastic resonance phenomenon. In our study, 8 of 15 and 10 of 15 subjects were responsive for the 1-2- and 0-30-Hz stimulus signals, respectively. The improvement in balance performance did not differ significantly between the stimulations in the two frequency ranges. The amplitude of optimal stimulus for improving balance performance was predominantly in the range of ±100 to ±400 μA. A device based on SR stimulation of the vestibular system might be useful as either a training modality to enhance adaptability or skill acquisition, or as a miniature patch-type stimulator that may be worn by people with disabilities due to aging or disease to improve posture and locomotion function.

Model for biological communication in a nanofabricated cell-mimic driven by stochastic resonance

Cells offer natural examples of highly efficient networks of nanomachines. Accordingly, both intracellular and intercellular communication mechanisms in nature are looked to as a source of inspiration and instruction for engineered nanocommunication. Harnessing biological functionality in this manner requires an interdisciplinary approach that integrates systems biology, synthetic biology, and nanofabrication. Here, we present a model system that exemplifies the synergism between these realms of research. We propose a synthetic gene network for operation in a nanofabricated cell mimic array that propagates a biomolecular signal over long distances using the phenomenon of stochastic resonance. Our system consists of a bacterial quorum sensing signal molecule, a bistable genetic switch triggered by this signal, and an array of nanofabricated cell mimic wells that contain the genetic system. An optimal level of noise in the system helps to propagate a time-varying AHL signal over long distances through the array of mimics. This noise level is determined both by the system volume and by the parameters of the genetic network. Our proposed genetically driven stochastic resonance system serves as a testbed for exploring the potential harnessing of gene expression noise to aid in the transmission of a time-varying molecular signal.

Finite post synaptic potentials cause a fast neuronal response

A generic property of the communication between neurons is the exchange of pulses at discrete time points, the action potentials. However, the prevalent theory of spiking neuronal networks of integrate-and-fire model neurons relies on two assumptions: the superposition of many afferent synaptic impulses is approximated by Gaussian white noise, equivalent to a vanishing magnitude of the synaptic impulses, and the transfer of time varying signals by neurons is assessable by linearization. Going beyond both approximations, we find that in the presence of synaptic impulses the response to transient inputs differs qualitatively from previous predictions. It is instantaneous rather than exhibiting low-pass characteristics, depends non-linearly on the amplitude of the impulse, is asymmetric for excitation and inhibition and is promoted by a characteristic level of synaptic background noise. These findings resolve contradictions between the earlier theory and experimental observations. Here we review the recent theoretical progress that enabled these insights. We explain why the membrane potential near threshold is sensitive to properties of the afferent noise and show how this shapes the neural response. A further extension of the theory to time evolution in discrete steps quantifies simulation artifacts and yields improved methods to cross check results.

Temporal complexity in clinical manifestations of lung disease

In this review, we summarize results of recent research on the temporal variability of lung function, symptoms, and inflammatory biomarkers. Specifically, we demonstrate how fluctuation analysis borrowed from statistical physics can be used to gain insight into neurorespiratory control and complex chronic dynamic diseases such as asthma viewed as a system of interacting components (e.g., inflammatory, immunological, and mechanical). Fluctuation analysis tools are based on quantifying the distribution and the short- and long-term temporal history of tidal breathing and lung function parameters to assess neurorespiratory control and monitor chronic disease. The latter includes the assessment of severity and disease control, the impact of treatment and environmental triggers, the temporal characterization of disease phenotypes, and the individual risk of exacerbation. While in many cases specific mechanistic insight into the fluctuations still awaits further research, appropriate analyses of the fluctuations already impact on clinical science and practice.

Practical measures of integrated information for time-series data

A recent measure of 'integrated information', Φ(DM), quantifies the extent to which a system generates more information than the sum of its parts as it transitions between states, possibly reflecting levels of consciousness generated by neural systems. However, Φ(DM) is defined only for discrete Markov systems, which are unusual in biology; as a result, Φ(DM) can rarely be measured in practice. Here, we describe two new measures, Φ(E) and Φ(AR), that overcome these limitations and are easy to apply to time-series data. We use simulations to demonstrate the in-practice applicability of our measures, and to explore their properties. Our results provide new opportunities for examining information integration in real and model systems and carry implications for relations between integrated information, consciousness, and other neurocognitive processes. However, our findings pose challenges for theories that ascribe physical meaning to the measured quantities.