The benefits of noise in neural systems: bridging theory and experiment

Memristors for the Curious Outsiders

We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.

Dissipative phase transitions in systems with nonreciprocal effective interactions

The reciprocity of effective interparticle forces can be violated in various open and nonequilibrium systems, in particular, in colloidal suspensions and complex (dusty) plasmas. Here, we obtain a criterion under which a nonreciprocal system can be strictly reduced to a pseudo-Hamiltonian system with a detailed dynamic equilibrium. In particular, the criterion is satisfied for catalytically active colloids interacting via nonreciprocal diffusiophoretic forces. However, in the general case, when this criterion is not satisfied, the steady state is determined by the interplay between dissipation and the energy source due to the nonreciprocity of interactions. The results indicate the realization of bistability and dissipative spinodal decomposition in a broad class of systems with nonreciprocal effective interactions.

Improved lower bound for the mutual information between signal and neural spike count

The mutual information between a stimulus signal and the spike count of a stochastic neuron is in many cases difficult to determine. Therefore, it is often approximated by a lower bound formula that involves linear correlations between input and output only. Here, we improve the linear lower bound for the mutual information by incorporating nonlinear correlations. For the special case of a Gaussian output variable with nonlinear signal dependencies of mean and variance we also derive an exact integral formula for the full mutual information. In our numerical analysis, we first compare the linear and nonlinear lower bounds and the exact integral formula for two different Gaussian models and show under which conditions the nonlinear lower bound provides a significant improvement to the linear approximation. We then inspect two neuron models, the leaky integrate-and-fire model with white Gaussian noise and the Na-K model with channel noise. We show that for certain firing regimes and for intermediate signal strengths the nonlinear lower bound can provide a substantial improvement compared to the linear lower bound. Our results demonstrate the importance of nonlinear input-output correlations for neural information transmission and provide a simple nonlinear approximation for the mutual information that can be applied to more complicated neuron models as well as to experimental data.

Learning while multitasking: short and long-term benefits of brain stimulation

We employed a simulated production task that mimics the real-world skill acquisition required of operators working in control rooms of power plants to assess short and long-term effects of transcranial random noise stimulation (tRNS). tRNS has shown potential for enhancing learning and performance of cognitive skills. Forty subjects (24 female) learned how to execute the simulated production task during the training phase and were required to perform a secondary task during the skill acquisition phase while they received active (12 min) or sham tRNS on DLPFC. After 2 weeks they had to recall the task again without any stimulation. The results demonstrate that tRNS promoted better multitasking as reflected by better performance in a secondary task during and immediately after tRNS. However, 2 weeks later, beneficial effect of tRNS on retention was moderated by general mental ability. Particularly, tRNS benefited those with lower general mental ability. Practitioner summary: By using a simulated production task, we assessed the effects of tRNS on learning and skill retention. The study indicates that neurostimulation can enhance the learning of multiple complex tasks. Moreover, it shows that retention of those tasks can be supported by neurostimulation, especially for those with lower general mental ability.

Noisy Galvanic Vestibular Stimulation Sustainably Improves Posture in Bilateral Vestibulopathy

Patients with bilateral vestibulopathy (BV) suffer from persistent postural imbalance, leading to a marked decrease in quality of life and a higher risk of falls. However, so far, the effective treatments for BV are very limited. We examined whether long-term noisy galvanic vestibular stimulation (nGVS) keeps improving body balance after the cessation of the stimulus in BV patients. Thirteen BV patients received nGVS for 30 min with a lower intensity than the intensity at which they feel any cutaneous sensations, and their postural movement was monitored for 6 h after the stimuli. The same session was repeated at 14-day intervals. Stance tasks on two legs were performed with eyes closed. The velocity of the center of pressure (COP) movement, the area enclosed by the COP movement, and the root mean square of the displacement of the COP were measured. The power spectrum of the COP movement was assessed. Subjective improvement of body balance was graded as worsened (−2), slightly worsened (−1), unchanged (0), slightly improved (+1) and improved (+2) in comparison with that without nGVS. In each session, the velocity of the COP movement was significantly improved for 6 h after the stimulus had ceased (P < 0.01). Concomitantly, the mean frequency of the COP power spectrum was significantly reduced in the anterior-posterior axis (P < 0.05). Subjective symptoms of imbalance were improved during the post-stimulation effect (P < 0.05). nGVS leads to an improvement in body balance that lasts for several hours after the end of the stimulus in BV patients with a reduction in the high-frequency components of their postural movement. This trial was registered with the University Hospital Medical Information Network (UMIN) Clinical Trials Registry (UMINCTR: UMIN000028054).

Dynamics of spontaneous alpha activity correlate with language ability in young children

Early childhood is a period of tremendous growth in both language ability and brain maturation. To understand the dynamic interplay between neural activity and spoken language development, we used resting-state EEG recordings to explore the relation between alpha oscillations (7-10 Hz) and oral language ability in 4- to 6-year-old children with typical development (N = 41). Three properties of alpha oscillations were investigated: a) alpha power using spectral analysis, b) flexibility of the alpha frequency quantified via the oscillation's moment-to-moment fluctuations, and c) scaling behavior of the alpha oscillator investigated via the long-range temporal correlation in the alpha-amplitude time course. All three properties of the alpha oscillator correlated with children's oral language abilities. Higher language scores were correlated with lower alpha power, greater flexibility of the alpha frequency, and longer temporal correlations in the alpha-amplitude time course. Our findings demonstrate a cognitive role of several properties of the alpha oscillator that has largely been overlooked in the literature.

Modeling effects of neural fluctuations and inter-scale interactions

One of the greatest challenges to science, in particular, to neuroscience, is to understand how processes at different levels of organization are related to each other. In connection with this problem is the question of the functional significance of fluctuations, noise, and chaos. This paper deals with three related issues: (1) how processes at different organizational levels of neural systems might be related, (2) the functional significance of non-linear neurodynamics, including oscillations, chaos, and noise, and (3) how computational models can serve as useful tools in elucidating these types of issues. In order to capture and describe phenomena at different micro (molecular), meso (cellular), and macro (network) scales, the computational models need to be of appropriate complexity making use of available experimental data. I exemplify by two major types of computational models, those of Hans Braun and colleagues and those of my own group, which both aim at bridging gaps between different levels of neural systems. In particular, the constructive role of noise and chaos in such systems is modelled and related to functions, such as sensation, perception, learning/memory, decision making, and transitions between different (un-)conscious states. While there is, in general, a focus on upward causation, I will also discuss downward causation, where higher level activity may affect the activity at lower levels, which should be a condition for any functional role of consciousness and free will, often considered to be problematic to science.

Activity patterns with silent states in a heterogeneous network of gap-junction coupled Huber-Braun model neurons

A mathematical model of a network of nearest neighbor gap-junction coupled neurons has been used to examine the impact of neuronal heterogeneity on the networks' activity during increasing coupling strength. Heterogeneity has been introduced by Huber-Braun model neurons with randomization of the temperature as a scaling factor. This leads to neurons of an enormous diversity of impulse pattern, including burst discharges, chaotic activity, and two different types of tonic firing-all of them experimentally observed in the peripheral as well as central nervous system. When the network is composed of all these types of neurons, randomly selected, a particular phenomenon can be observed. At a certain coupling strength, the network goes into a completely silent state. Examination of voltage traces and inter-spike intervals of individual neurons suggests that all neurons, irrespective of their original pattern, go through a well-known bifurcation scenario, resembling those of single neurons especially on external current injection. All the originally spontaneously firing neurons can achieve constant membrane potentials at which all intrinsic and gap-junction currents are balanced. With limited diversity, i.e., taking out neurons of specific patterns from the lower and upper temperature range, spontaneous firing can be reinstalled with further increasing coupling strength, especially when the tonic firing regimes are missing. Reinstalled firing develops from slowly increasing subthreshold oscillations leading to tonic firing activity with already fairly well synchronized action potentials, while the subthreshold potentials can still be significantly different. Full in phase synchronization is not achieved. Additional studies are needed elucidating the underlying mechanisms and the conditions under which such particular transitions can appear.

Infinitely large, randomly wired sensors cannot predict their input unless they are close to deterministic

Building predictive sensors is of paramount importance in science. Can we make a randomly wired sensor “good enough” at predicting its input simply by making it larger? We show that infinitely large, randomly wired sensors are nonspecific for their input, and therefore nonpredictive of future input, unless they are close to deterministic. Nearly deterministic, randomly wired sensors can capture ∼ 10% of the predictive information of their inputs for “typical” environments.

Chance, long tails, and inference in a non-Gaussian, Bayesian theory of vocal learning in songbirds

Traditional theories of sensorimotor learning posit that animals use sensory error signals to find the optimal motor command in the face of Gaussian sensory and motor noise. However, most such theories cannot explain common behavioral observations, for example, that smaller sensory errors are more readily corrected than larger errors and large abrupt (but not gradually introduced) errors lead to weak learning. Here, we propose a theory of sensorimotor learning that explains these observations. The theory posits that the animal controls an entire probability distribution of motor commands rather than trying to produce a single optimal command and that learning arises via Bayesian inference when new sensory information becomes available. We test this theory using data from a songbird, the Bengalese finch, that is adapting the pitch (fundamental frequency) of its song following perturbations of auditory feedback using miniature headphones. We observe the distribution of the sung pitches to have long, non-Gaussian tails, which, within our theory, explains the observed dynamics of learning. Further, the theory makes surprising predictions about the dynamics of the shape of the pitch distribution, which we confirm experimentally.

Intrinsic stochastic resonance via set-point variation

In the present paper, the possibility of invoking stochastic resonance (SR, periodic and aperiodic) by regulating the operating value of an appropriate parameter is explored. The operating values of these parameters are defined as the set point of the system throughout the present paper. Brusselator, a mathematical model [I. Prigogine and R. Lefever, J. Chem. Phys. 48, 1695 (1968)JCPSA60021-960610.1063/1.1668896] of nonlinear chemical reactions, is used for this purpose. We consider the effect of intrinsic noise in the Brusselator due to the Markovian nature of the chemical reactions. The stochastic time evolution is studied using the Gillespie algorithm [D. T. Gillespie, J. Comput. Phys. 22, 403 (1976)JCTPAH0021-999110.1016/0021-9991(76)90041-3], which is an exact stochastic simulation algorithm. We analyze the dependence of the resonance point on both the strength of the intrinsic noise as well as the distance from the bifurcation point. Subsequently, the phenomena of SR is explored using both periodic and aperiodic stimulus. It was found that, for a given system size, in both cases, SR is achieved by variation of the set point. Set-point variation can be achieved by regulating either the source concentration or the rate constants. Resonance is observed in both cases. However, this resonance occurs at different values of the set point, even with a fixed system size. This is clearly seen in the set-point versus system-size plane, where the resonance line has different slopes for the two scenarios. Our semianalytic treatment points to the fact that for a given system size intrinsic noise is affected differently for different methods involving the variation of the set point. This is explained by writing the corresponding chemical Langevin equation and comparing the various intrinsic noise sources.

Humans control stride-to-stride stepping movements differently for walking and running, independent of speed

As humans walk or run, external (environmental) and internal (physiological) disturbances induce variability. How humans regulate this variability from stride-to-stride can be critical to maintaining balance. One cannot infer what is "controlled" based on analyses of variability alone. Assessing control requires quantifying how deviations are corrected across consecutive movements. Here, we assessed walking and running, each at two speeds. We hypothesized differences in speed would drive changes in variability, while adopting different gaits would drive changes in how people regulated stepping. Ten healthy adults walked/ran on a treadmill under four conditions: walk or run at comfortable speed, and walk or run at their predicted walk-to-run transition speed. Time series of relevant stride parameters were analyzed to quantify variability and stride-to-stride error-correction dynamics within a Goal-Equivalent Manifold (GEM) framework. In all conditions, participants' stride-to-stride control respected a constant-speed GEM strategy. At each consecutively faster speed, variability tangent to the GEM increased (p ≤ 0.031), while variability perpendicular to the GEM decreased (p ≤ 0.044). There were no differences (p ≥ 0.999) between gaits at the transition speed. Differences in speed determined how stepping variability was structured, independent of gait, confirming our first hypothesis. For running versus walking, measures of GEM-relevant statistical persistence were significantly less (p ≤ 0.004), but showed minimal-to-no speed differences (0.069 ≤ p ≤ 0.718). When running, people corrected deviations both more quickly and more directly, each indicating tighter control. Thus, differences in gait determined how stride-to-stride fluctuations were regulated, independent of speed, confirming our second hypothesis.

Sub-threshold signal encoding in coupled FitzHugh-Nagumo neurons

Despite intensive research, the mechanisms underlying the neural code remain poorly understood. Recent work has focused on the response of a single neuron to a weak, sub-threshold periodic signal. By simulating the stochastic FitzHugh-Nagumo (FHN) model and then using a symbolic method to analyze the firing activity, preferred and infrequent spike patterns (defined by the relative timing of the spikes) were detected, whose probabilities encode information about the signal. As not individual neurons but neuronal populations are responsible for sensory coding and information transfer, a relevant question is how a second neuron, which does not perceive the signal, affects the detection and the encoding of the signal, done by the first neuron. Through simulations of two stochastic FHN neurons we show that the encoding of a sub-threshold signal in symbolic spike patterns is a plausible mechanism. The neuron that perceives the signal fires a spike train that, despite having an almost random temporal structure, has preferred and infrequent patterns which carry information about the signal. Our findings could be relevant for sensory systems composed by two noisy neurons, when only one detects a weak external input.

The impact of auditory white noise on semantic priming

It has been proposed that white noise can improve cognitive performance for some individuals, particularly those with lower attention, and that this effect may be mediated by dopaminergic circuitry. Given existing evidence that semantic priming is modulated by dopamine, this study investigated whether white noise can facilitate semantic priming. Seventy-eight adults completed an auditory semantic priming task with and without white noise, at either a short or long inter-stimulus interval (ISI). Measures of both direct and indirect semantic priming were examined. Analysis of the results revealed significant direct and indirect priming effects at each ISI in noise and silence, however noise significantly reduced the magnitude of indirect priming. Analyses of subgroups with higher versus lower attention revealed a reduction to indirect priming in noise relative to silence for participants with lower executive and orienting attention. These findings suggest that white noise focuses automatic spreading activation, which may be driven by modulation of dopaminergic circuitry.

Electro-tactile stimulation of the posterior neck induces body anteropulsion during upright stance

Sensory information conveyed along afferent fibers from muscle and joint proprioceptors play an important role in the control of posture and gait in humans. In particular, proprioceptive information from the neck is fundamental in supplying the central nervous system with information about the orientation and movement of the head relative to the rest of the body. The previous studies have confirmed that proprioceptive afferences originating from the neck region, evoked via muscle vibration, lead to strong body-orienting effects during static conditions (e.g., leaning of the body forwards or backwards, depending on location of vibration). However, it is not yet certain in humans, whether the somatosensory receptors located in the deep skin (cutaneous mechanoreceptors) have a substantive contribution to postural control, as vibratory stimulation encompasses the receptive field of all the somatosensory receptors from the skin to the muscles. The aim of this study was to investigate the postural effect of cutaneous mechanoreceptor afferences using electro-tactile stimulation applied to the neck. Ten healthy volunteers (8M, 2F) were evaluated. The average position of their centre of foot pressure (CoP) was acquired before, during, and after a subtle electro-tactile stimulation over their posterior neck (mean ± SD = 5.1 ± 2.3 mA at 100 Hz—140% of the perception threshold) during upright stance with their eyes closed. The electro-tactile stimulation led to a body-orienting effect with the subjects consistently leaning forward. An average shift of the CoP of 12.1 ± 11.9 mm (mean ± SD) was reported, which significantly (p < 0.05) differed from its average position under a control condition (no stimulation). These results indicate that cutaneous mechanoreceptive inflow from the neck is integrated to control stance. The findings are relevant for the exploitation of electro-tactile stimulation for rehabilitation interventions where induced anteropulsion of the body is desired.

A practical comparison of algorithms for the measurement of multiscale entropy in neural time series data

There is a broad family of statistical methods for capturing time series regularity, with increasingly widespread adoption by the neuroscientific community. A common feature of these methods is that they permit investigators to quantify the entropy of brain signals - an index of unpredictability/complexity. Despite the proliferation of algorithms for computing entropy from neural time series data there is scant evidence concerning their relative stability and efficiency. Here we evaluated several different algorithmic implementations (sample, fuzzy, dispersion and permutation) of multiscale entropy in terms of their stability across sessions, internal consistency and computational speed, accuracy and precision using a combination of electroencephalogram (EEG) and synthetic 1/ƒ noise signals. Overall, we report fair to excellent internal consistency and longitudinal stability over a one-week period for the majority of entropy estimates, with several caveats. Computational timing estimates suggest distinct advantages for dispersion and permutation entropy over other entropy estimates. Considered alongside the psychometric evidence, we suggest several ways in which researchers can maximize computational resources (without sacrificing reliability), especially when working with high-density M/EEG data or multivoxel BOLD time series signals.

It's Not (Only) the Mean that Matters: Variability, Noise and Exploration in Skill Learning

Mastering a motor skill is typified by a decrease in variability. However, variability is much more than the undesired signature of discoordination: structure in both its distributional properties and temporal sequence can reveal control priorities. Extending from the notion that signal-dependent noise corrupts information transmission in the neuromotor system, this review tracks more recent recognitions that the complex dynamic motor system in its interaction with task constraints creates high-dimensional spaces with multiple equivalent solutions. Further analysis differentiates these solutions to have different degrees of noise-sensitivity, goal-relevance or additional costs. Practice proceeds from exploration of these solution spaces to then exploitation with further channeling of noise. Extended practice leads to fine-tuning of skill brought about by reducing noise. These distinct changes in variability are suggested as a way to characterize stages of learning. Capitalizing on the sensitivity of the CNS to noise, interventions can add extrinsic or amplify intrinsic noise to guide (re-)learning desired behaviors. The persistence and generalization of acquired skill is still largely understudied, although an essential element of skill. Consistent with advances in the physical sciences, there is increasing realization that noise can have beneficial effects. Analysis of the non-random structure of variability may reveal more than analysis of only its mean.

Galvanic Vestibular Stimulation (GVS) Augments Deficient Pedunculopontine Nucleus (PPN) Connectivity in Mild Parkinson's Disease: fMRI Effects of Different Stimuli

Falls and balance difficulties remain a major source of morbidity in Parkinson's Disease (PD) and are stubbornly resistant to therapeutic interventions. The mechanisms of gait impairment in PD are incompletely understood but may involve changes in the Pedunculopontine Nucleus (PPN) and its associated connections. We utilized fMRI to explore the modulation of PPN connectivity by Galvanic Vestibular Stimulation (GVS) in healthy controls (n = 12) and PD subjects even without overt evidence of Freezing of Gait (FOG) while on medication (n = 23). We also investigated if the type of GVS stimuli (i.e., sinusoidal or stochastic) differentially affected connectivity. Approximate PPN regions were manually drawn on T1 weighted images and 58 other cortical and subcortical Regions of Interest (ROI) were obtained by automatic segmentation. All analyses were done in the native subject's space without spatial transformation to a common template. We first used Partial Least Squares (PLS) on a subject-by-subject basis to determine ROIs across subjects that covaried significantly with the voxels within the PPN ROI. We then performed functional connectivity analysis on the PPN-ROI connections. In control subjects, GVS did not have a significant effect on PPN connectivity. In PD subjects, baseline overall magnitude of PPN connectivity was negatively correlated with UPDRS scores (p < 0.05). Both noisy and sinusoidal GVS increased the overall magnitude of PPN connectivity (p = 6 × 10⁻⁵, 3 × 10⁻⁴, respectively) in PD, and increased connectivity with the left inferior parietal region, but had opposite effects on amygdala connectivity. Noisy stimuli selectively decreased connectivity with basal ganglia and cerebellar regions. Our results suggest that GVS can enhance deficient PPN connectivity seen in PD in a stimulus-dependent manner. This may provide a mechanism through which GVS assists balance in PD, and may provide a biomarker to develop individualized stimulus parameters.

Clustering promotes switching dynamics in networks of noisy neurons

Macroscopic variability is an emergent property of neural networks, typically manifested in spontaneous switching between the episodes of elevated neuronal activity and the quiescent episodes. We investigate the conditions that facilitate switching dynamics, focusing on the interplay between the different sources of noise and heterogeneity of the network topology. We consider clustered networks of rate-based neurons subjected to external and intrinsic noise and derive an effective model where the network dynamics is described by a set of coupled second-order stochastic mean-field systems representing each of the clusters. The model provides an insight into the different contributions to effective macroscopic noise and qualitatively indicates the parameter domains where switching dynamics may occur. By analyzing the mean-field model in the thermodynamic limit, we demonstrate that clustering promotes multistability, which gives rise to switching dynamics in a considerably wider parameter region compared to the case of a non-clustered network with sparse random connection topology.

Multiscale energy reallocation during low-frequency steady-state brain response

Traditional task-evoked brain activations are based on detection and estimation of signal change from the mean signal. By contrast, the low-frequency steady-state brain response (lfSSBR) reflects frequency-tagging activity at the fundamental frequency of the task presentation and its harmonics. Compared to the activity at these resonant frequencies, brain responses at nonresonant frequencies are largely unknown. Additionally, because the lfSSBR is defined by power change, we hypothesize using Parseval's theorem that the power change reflects brain signal variability rather than the change of mean signal. Using a face recognition task, we observed power increase at the fundamental frequency (0.05 Hz) and two harmonics (0.1 and 0.15 Hz) and power decrease within the infra-slow frequency band (<0.1 Hz), suggesting a multifrequency energy reallocation. The consistency of power and variability was demonstrated by the high correlation (r > .955) of their spatial distribution and brain-behavior relationship at all frequency bands. Additionally, the reallocation of finite energy was observed across various brain regions and frequency bands, forming a particular spatiotemporal pattern. Overall, results from this study strongly suggest that frequency-specific power and variability may measure the same underlying brain activity and that these results may shed light on different mechanisms between lfSSBR and brain activation, and spatiotemporal characteristics of energy reallocation induced by cognitive tasks.

Effects of channel blocking on information transmission and energy efficiency in squid giant axons

Action potentials are the information carriers of neural systems. The generation of action potentials involves the cooperative opening and closing of sodium and potassium channels. This process is metabolically expensive because the ions flowing through open channels need to be restored to maintain concentration gradients of these ions. Toxins like tetraethylammonium can block working ion channels, thus affecting the function and energy cost of neurons. In this paper, by computer simulation of the Hodgkin-Huxley neuron model, we studied the effects of channel blocking with toxins on the information transmission and energy efficiency in squid giant axons. We found that gradually blocking sodium channels will sequentially maximize the information transmission and energy efficiency of the axons, whereas moderate blocking of potassium channels will have little impact on the information transmission and will decrease the energy efficiency. Heavy blocking of potassium channels will cause self-sustained oscillation of membrane potentials. Simultaneously blocking sodium and potassium channels with the same ratio increases both information transmission and energy efficiency. Our results are in line with previous studies suggesting that information processing capacity and energy efficiency can be maximized by regulating the number of active ion channels, and this indicates a viable avenue for future experimentation.

Mean and variability in functional brain activations differentially predict executive function in older adults: an investigation employing functional near-infrared spectroscopy

OBJECTIVE

although the preponderance of research on functional brain activity investigates mean group differences, mounting evidence suggests that variability in neural activity is beneficial for optimal central nervous system (CNS) function. Independent of mean signal estimates, recent findings have shown that neural variability diminishes with age and is positively associated with cognitive performance, underscoring its adaptive nature. The present investigation sought to employ functional near infrared spectroscopy (fNIRS) to derive two operationalizations of cerebral oxygenation, representing mean and variability [using standard deviation (SD)] in neural activity, and to specifically contrast these mean- and SD-oxyhemoglobin (HbO) estimates as predictors of cognitive function.

METHOD

a total of 25 older adults (71 to 81 years of age) completed a test of cognitive interference (Multisource Interference Task) while undergoing fNIRS recording using a multichannel continuous-wave optical imaging system (TechEn CW6) over bilateral prefrontal cortex (PFC). Time-varying covariation models were employed to simultaneously estimate the within- and between-person effects of cerebral oxygenation on behavioral performance fluctuations.

RESULTS

mean effects were predominantly observed at the between-person level and suggest that greater concentrations of HbO are associated with slower and less accurate performance. Greater HbO variability at the between-person level was associated with slower performance, but was associated with faster performance at the within-person level.

CONCLUSIONS

these findings are in keeping with assertions that mean and variability confer complementary (as opposed to redundant) sources of information regarding the effective functioning of a neural system and suggest that fNIRS is a viable methodology for capturing meaningful variance in the hemodynamic response that is characteristic of adaptive CNS function.

High internal noise and poor external noise filtering characterize perception in autism spectrum disorder

An emerging hypothesis postulates that internal noise is a key factor influencing perceptual abilities in autism spectrum disorder (ASD). Given fundamental and inescapable effects of noise on nearly all aspects of neural processing, this could be a critical abnormality with broad implications for perception, behavior, and cognition. However, this proposal has been challenged by both theoretical and empirical studies. A crucial question is whether and how internal noise limits perception in ASD, independently from other sources of perceptual inefficiency, such as the ability to filter out external noise. Here, we separately estimated internal noise and external noise filtering in ASD. In children and adolescents with and without ASD, we computationally modeled individuals' visual orientation discrimination in the presence of varying levels of external noise. The results revealed increased internal noise and worse external noise filtering in individuals with ASD. For both factors, we also observed high inter-individual variability in ASD, with only the internal noise estimates significantly correlating with severity of ASD symptoms. We provide evidence for reduced perceptual efficiency in ASD that is due to both increased internal noise and worse external noise filtering, while highlighting internal noise as a possible contributing factor to variability in ASD symptoms.

Coding of time-dependent stimuli in homogeneous and heterogeneous neural populations

We compare the information transmission of a time-dependent signal by two types of uncoupled neuron populations that differ in their sources of variability: i) a homogeneous population whose units receive independent noise and ii) a deterministic heterogeneous population, where each unit exhibits a different baseline firing rate ('disorder'). Our criterion for making both sources of variability quantitatively comparable is that the interspike-interval distributions are identical for both systems. Numerical simulations using leaky integrate-and-fire neurons unveil that a non-zero amount of both noise or disorder maximizes the encoding efficiency of the homogeneous and heterogeneous system, respectively, as a particular case of suprathreshold stochastic resonance. Our findings thus illustrate that heterogeneity can render similarly profitable effects for neuronal populations as dynamic noise. The optimal noise/disorder depends on the system size and the properties of the stimulus such as its intensity or cutoff frequency. We find that weak stimuli are better encoded by a noiseless heterogeneous population, whereas for strong stimuli a homogeneous population outperforms an equivalent heterogeneous system up to a moderate noise level. Furthermore, we derive analytical expressions of the coherence function for the cases of very strong noise and of vanishing intrinsic noise or heterogeneity, which predict the existence of an optimal noise intensity. Our results show that, depending on the type of signal, noise as well as heterogeneity can enhance the encoding performance of neuronal populations.

How does transient signaling input affect the spike timing of postsynaptic neuron near the threshold regime: an analytical study

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

Biological information systems: Evolution as cognition-based information management

An alternative biological synthesis is presented that conceptualizes evolutionary biology as an epiphenomenon of integrated self-referential information management. Since all biological information has inherent ambiguity, the systematic assessment of information is required by living organisms to maintain self-identity and homeostatic equipoise in confrontation with environmental challenges. Through their self-referential attachment to information space, cells are the cornerstone of biological action. That individualized assessment of information space permits self-referential, self-organizing niche construction. That deployment of information and its subsequent selection enacted the dominant stable unicellular informational architectures whose biological expressions are the prokaryotic, archaeal, and eukaryotic unicellular forms. Multicellularity represents the collective appraisal of equivocal environmental information through a shared information space. This concerted action can be viewed as systematized information management to improve information quality for the maintenance of preferred homeostatic boundaries among the varied participants. When reiterated in successive scales, this same collaborative exchange of information yields macroscopic organisms as obligatory multicellular holobionts. Cognition-Based Evolution (CBE) upholds that assessment of information precedes biological action, and the deployment of information through integrative self-referential niche construction and natural cellular engineering antecedes selection. Therefore, evolutionary biology can be framed as a complex reciprocating interactome that consists of the assessment, communication, deployment and management of information by self-referential organisms at multiple scales in continuous confrontation with environmental stresses.

Emergent Bistability and Switching in a Nonequilibrium Crystal

Multistability is an inseparable feature of many physical, chemical, and biological systems which are driven far from equilibrium. In these nonequilibrium systems, stochastic dynamics often induces switching between distinct states on emergent time scales; for example, bistable switching is a natural feature of noisy, spatially extended systems that consist of bistable elements. Nevertheless, here we present experimental evidence that bistable elements are not required for the global bistability of a system. We observe temporal switching between a crystalline, condensed state and a gaslike, excited state in a spatially extended, quasi-two-dimensional system of charged microparticles. Accompanying numerical simulations show that conservative forces, damping, and stochastic noise are sufficient to prevent steady-state equilibrium, leading to switching between the two states over a range of time scales, from seconds to hours.

A random-perturbation therapy in chronic non-specific low-back pain patients: a randomised controlled trial

The purpose of the study was to assess the effectiveness of a specific rehabilitation therapy for chronic non-specific low-back pain patients, based on a random/irregular functional perturbation training induced by force disturbances to the spine. Forty patients (20 controls and 20 in the perturbation-based group) finished the whole experimental design. A random-perturbation exercise, which included variable and unpredictable disturbances, was implemented in the therapy of the perturbation-based group (13 weeks, two times per week and 1.5 h per session). The participants of the control group did not receive any specific training. Low-back pain, muscle strength, and neuromuscular control of spine stability were investigated before and after the therapy using the visual analog scale, maximal isometric and isokinetic contractions, nonlinear time series analysis, and by determining the stiffness and damping of the trunk after sudden perturbations. The perturbation-based therapy reduced patient's low-back pain (35%), increased muscle strength (15-22%), and trunk stiffness (13%), while no significant changes were observed in the control group. It can be concluded that the proposed therapy has the potential to enhance trunk muscle capability as well as sensory information processing within the motor system during sudden loading and, as a consequence, improve the stabilization of the trunk.

White noise enhances new-word learning in healthy adults

Research suggests that listening to white noise may improve some aspects of cognitive performance in individuals with lower attention. This study investigated the impact of white noise on new word learning in healthy young adults, and whether this effect was mediated by executive attention skills. Eighty participants completed a single training session to learn the names of twenty novel objects. The session comprised 5 learning phases, each followed by a recall test. A final recognition test was also administered. Half the participants listened to white noise during the learning phases, and half completed the learning in silence. The noise group demonstrated superior recall accuracy over time, which was not impacted by participant attentional capacity. Recognition accuracy was near ceiling for both groups. These findings suggest that white noise has the capacity to enhance lexical acquisition.

Stochastic resonance improves vision in the severely impaired

We verified whether a stochastic resonance paradigm (SR), with random interference (“noise”) added in optimal amounts, improves the detection of sub-threshold visual information by subjects with retinal disorder and impaired vision as it does in the normally sighted. Six levels of dynamic, zero-mean Gaussian noise were added to each pixel of images (13 contrast levels) in which alphabet characters were displayed against a uniform gray background. Images were presented with contrast below the subjective threshold to 14 visually impaired subjects (age: 22–53 yrs.). The fraction of recognized letters varied between 0 and 0.3 at baseline and increased in all subjects when noise was added in optimal amounts; peak recognition ranged between 0.2 and 0.8 at noise sigmas between 6 and 30 grey scale values (GSV) and decreased in all subjects at noise levels with sigma above 30 GSV. The results replicate in the visually impaired the facilitation of visual information processing with images presented in SR paradigms that has been documented in sighted subjects. The effect was obtained with low-level image manipulation and application appears readily possible: it would enhance the efficiency of today vision-improving aids and help in the development of the visual prostheses hopefully available in the future.

Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task

Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP) are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both reward modulated and non-reward modulated STDP and implemented multiple mechanisms for homeostatic regulation of synaptic efficacy, including heterosynaptic plasticity, gain control, output balancing, activity normalization of rewarded STDP and hard limits on synaptic strength. We found that addition of a hidden layer of neurons employing non-rewarded STDP created neurons that responded to the specific combinations of inputs and thus performed basic classification of the input patterns. When combined with a following layer of neurons implementing rewarded STDP, the network was able to learn, despite the absence of labeled training data, discrimination between rewarding patterns and the patterns designated as punishing. Synaptic noise allowed for trial-and-error learning that helped to identify the goal-oriented strategies which were effective in task solving. The study predicts a critical set of properties of the spiking neuronal network with STDP that was sufficient to solve a complex foraging task involving pattern classification and decision making.

This study explores how intelligent behavior emerges from the basic principles known at the cellular level of biological neuronal network dynamics. Compared to the approaches used in the artificial intelligence community, we applied biologically realistic modeling of neuronal dynamics and plasticity. The building blocks of the model are spiking neurons, spike-time dependent plasticity (STDP) and homeostatic rules, known experimentally, which are shown to play a fundamental role in both keeping the network stable and capable of continous learning. Our study predicts that a combination of these principles makes possible a foraging behavior in a previously unknown environment, including pattern classification to distinct between environment shapes which are rewarded and those which are punished and decision making to select the optimal strategy to acquire the maximal number of the rewarded elements. To solve this complex task we used multi-layer neuronal processing that implemented pattern generalization by unsupervised STDP at the earlier processing step, as commonly observed in the animal and human sensory processing, followed by reinforcement learning at the later steps. In the model, the intelligent behavior emerged spontaneously due to the network organization implementing both local unsupervised plasticity and reward feedback resulting from a successful behavior in the environment.

Energy-efficient neural information processing in individual neurons and neuronal networks

Brains are composed of networks of an enormous number of neurons interconnected with synapses. Neural information is carried by the electrical signals within neurons and the chemical signals among neurons. Generating these electrical and chemical signals is metabolically expensive. The fundamental issue raised here is whether brains have evolved efficient ways of developing an energy-efficient neural code from the molecular level to the circuit level. Here, we summarize the factors and biophysical mechanisms that could contribute to the energy-efficient neural code for processing input signals. The factors range from ion channel kinetics, body temperature, axonal propagation of action potentials, low-probability release of synaptic neurotransmitters, optimal input and noise, the size of neurons and neuronal clusters, excitation/inhibition balance, coding strategy, cortical wiring, and the organization of functional connectivity. Both experimental and computational evidence suggests that neural systems may use these factors to maximize the efficiency of energy consumption in processing neural signals. Studies indicate that efficient energy utilization may be universal in neuronal systems as an evolutionary consequence of the pressure of limited energy. As a result, neuronal connections may be wired in a highly economical manner to lower energy costs and space. Individual neurons within a network may encode independent stimulus components to allow a minimal number of neurons to represent whole stimulus characteristics efficiently. This basic principle may fundamentally change our view of how billions of neurons organize themselves into complex circuits to operate and generate the most powerful intelligent cognition in nature. © 2017 Wiley Periodicals, Inc.

The Role of Visual Noise in Influencing Mental Load and Fatigue in a Steady-State Motion Visual Evoked Potential-Based Brain-Computer Interface

As a spatial selective attention-based brain-computer interface (BCI) paradigm, steady-state visual evoked potential (SSVEP) BCI has the advantages of high information transfer rate, high tolerance to artifacts, and robust performance across users. However, its benefits come at the cost of mental load and fatigue occurring in the concentration on the visual stimuli. Noise, as a ubiquitous random perturbation with the power of randomness, may be exploited by the human visual system to enhance higher-level brain functions. In this study, a novel steady-state motion visual evoked potential (SSMVEP, i.e., one kind of SSVEP)-based BCI paradigm with spatiotemporal visual noise was used to investigate the influence of noise on the compensation of mental load and fatigue deterioration during prolonged attention tasks. Changes in α, θ, θ + α powers, θ/α ratio, and electroencephalography (EEG) properties of amplitude, signal-to-noise ratio (SNR), and online accuracy, were used to evaluate mental load and fatigue. We showed that presenting a moderate visual noise to participants could reliably alleviate the mental load and fatigue during online operation of visual BCI that places demands on the attentional processes. This demonstrated that noise could provide a superior solution to the implementation of visual attention controlling-based BCI applications.

Inverse stochastic resonance in networks of spiking neurons

Inverse Stochastic Resonance (ISR) is a phenomenon in which the average spiking rate of a neuron exhibits a minimum with respect to noise. ISR has been studied in individual neurons, but here, we investigate ISR in scale-free networks, where the average spiking rate is calculated over the neuronal population. We use Hodgkin-Huxley model neurons with channel noise (i.e., stochastic gating variable dynamics), and the network connectivity is implemented via electrical or chemical connections (i.e., gap junctions or excitatory/inhibitory synapses). We find that the emergence of ISR depends on the interplay between each neuron's intrinsic dynamical structure, channel noise, and network inputs, where the latter in turn depend on network structure parameters. We observe that with weak gap junction or excitatory synaptic coupling, network heterogeneity and sparseness tend to favor the emergence of ISR. With inhibitory coupling, ISR is quite robust. We also identify dynamical mechanisms that underlie various features of this ISR behavior. Our results suggest possible ways of experimentally observing ISR in actual neuronal systems.

Optically levitated nanoparticle as a model system for stochastic bistable dynamics

Nano-mechanical resonators have gained an increasing importance in nanotechnology owing to their contributions to both fundamental and applied science. Yet, their small dimensions and mass raises some challenges as their dynamics gets dominated by nonlinearities that degrade their performance, for instance in sensing applications. Here, we report on the precise control of the nonlinear and stochastic bistable dynamics of a levitated nanoparticle in high vacuum. We demonstrate how it can lead to efficient signal amplification schemes, including stochastic resonance. This work contributes to showing the use of levitated nanoparticles as a model system for stochastic bistable dynamics, with applications to a wide variety of fields.

Noise-enhanced coding in phasic neuron spike trains

The stochastic nature of neuronal response has lead to conjectures about the impact of input fluctuations on the neural coding. For the most part, low pass membrane integration and spike threshold dynamics have been the primary features assumed in the transfer from synaptic input to output spiking. Phasic neurons are a common, but understudied, neuron class that are characterized by a subthreshold negative feedback that suppresses spike train responses to low frequency signals. Past work has shown that when a low frequency signal is accompanied by moderate intensity broadband noise, phasic neurons spike trains are well locked to the signal. We extend these results with a simple, reduced model of phasic activity that demonstrates that a non-Markovian spike train structure caused by the negative feedback produces a noise-enhanced coding. Further, this enhancement is sensitive to the timescales, as opposed to the intensity, of a driving signal. Reduced hazard function models show that noise-enhanced phasic codes are both novel and separate from classical stochastic resonance reported in non-phasic neurons. The general features of our theory suggest that noise-enhanced codes in excitable systems with subthreshold negative feedback are a particularly rich framework to study.

Control of in vivo ictogenesis via endogenous synaptic pathways

The random nature of seizures poses difficult challenges for epilepsy research. There is great need for a reliable method to control the pathway to seizure onset, which would allow investigation of the mechanisms of ictogenesis and optimization of treatments. Our hypothesis is that increased random afferent synaptic activity (i.e. synaptic noise) within the epileptic focus is one endogenous method of ictogenesis. Building upon previous theoretical and in vitro work showing that synaptic noise can induce seizures, we developed a novel in vivo model of ictogenesis. By increasing the excitability of afferent connections to the hippocampus, we control the risk of temporal lobe seizures during a specific time period. The afferent synaptic activity in the hippocampus was modulated by focal microinjections of potassium chloride into the nucleus reuniens, during which the risk of seizure occurrence increased substantially. The induced seizures were qualitatively and quantitatively indistinguishable from spontaneous ones. This model thus allows direct control of the temporal lobe seizure threshold via endogenous pathways, providing a novel tool in which to investigate the mechanisms and biomarkers of ictogenesis, test for seizure threshold, and rapidly tune antiseizure treatments.

Chaotic Resonance in Typical Routes to Chaos in the Izhikevich Neuron Model

Chaotic resonance (CR), in which a system responds to a weak signal through the effects of chaotic activities, is a known function of chaos in neural systems. The current belief suggests that chaotic states are induced by different routes to chaos in spiking neural systems. However, few studies have compared the efficiency of signal responses in CR across the different chaotic states in spiking neural systems. We focused herein on the Izhikevich neuron model, comparing the characteristics of CR in the chaotic states arising through the period-doubling or tangent bifurcation routes. We found that the signal response in CR had a unimodal maximum with respect to the stability of chaotic orbits in the tested chaotic states. Furthermore, the efficiency of signal responses at the edge of chaos became especially high as a result of synchronization between the input signal and the periodic component in chaotic spiking activity.

Stochastic resonance in bistable atomic switches

We have investigated the conductance of bistable gold atomic switches as a function of periodic input voltages mixed with a random noise. With increasing noise amplitude, the atomic switches biased below the threshold voltage for conductance switching start exhibiting switching in conductance between two stable states. Clear synchronization between the input and output signals is observed when an optimized noise amplitude is mixed with the periodic input voltage, even when the atomic switches are driven by an input voltage as low as approximately 10% of the threshold voltage. The observed behavior can be explained in terms of the stochastic resonance. The results presented here indicate that utilization of noise can dramatically reduce the operation voltage of metal atomic switches.

Determining the Limitations and Benefits of Noise in Gene Regulation and Signal Transduction through Single Cell, Microscopy-Based Analysis

Stochastic fluctuations, termed "noise," in the level of biological molecules can greatly impact cellular functions. While biological noise can sometimes be detrimental, recent studies have provided an increasing number of examples in which biological noise can be functionally beneficial. Rather than provide an exhaustive review of the growing literature in this field, in this review, we focus on single-cell studies based on quantitative microscopy that have generated a deeper understanding of the sources, characteristics, limitations, and benefits of biological noise. Specifically, we highlight studies showing how noise can help coordinate the expression of multiple downstream target genes, impact the channel capacity of signaling networks, and interact synergistically with oscillatory dynamics to enhance the sensitivity of signal processing. We conclude with a discussion of current challenges and future opportunities.

Synchronous spikes are necessary but not sufficient for a synchrony code in populations of spiking neurons

Synchronous activity in populations of neurons potentially encodes special stimulus features. Selective readout of either synchronous or asynchronous activity allows formation of two streams of information processing. Theoretical work predicts that such a synchrony code is a fundamental feature of populations of spiking neurons if they operate in specific noise and stimulus regimes. Here we experimentally test the theoretical predictions by quantifying and comparing neuronal response properties in tuberous and ampullary electroreceptor afferents of the weakly electric fish Apteronotus leptorhynchus These related systems show similar levels of synchronous activity, but only in the more irregularly firing tuberous afferents a synchrony code is established, whereas in the more regularly firing ampullary afferents it is not. The mere existence of synchronous activity is thus not sufficient for a synchrony code. Single-cell features such as the irregularity of spiking and the frequency dependence of the neuron's transfer function determine whether synchronous spikes possess a distinct meaning for the encoding of time-dependent signals.

Double inverse stochastic resonance with dynamic synapses

We investigate the behavior of a model neuron that receives a biophysically realistic noisy postsynaptic current based on uncorrelated spiking activity from a large number of afferents. We show that, with static synapses, such noise can give rise to inverse stochastic resonance (ISR) as a function of the presynaptic firing rate. We compare this to the case with dynamic synapses that feature short-term synaptic plasticity and show that the interval of presynaptic firing rate over which ISR exists can be extended or diminished. We consider both short-term depression and facilitation. Interestingly, we find that a double inverse stochastic resonance (DISR), with two distinct wells centered at different presynaptic firing rates, can appear.

Electrosensory processing in Apteronotus albifrons: implications for general and specific neural coding strategies across wave-type weakly electric fish species

Understanding how the brain processes sensory input to generate behavior remains an important problem in neuroscience. Towards this end, it is useful to compare results obtained across multiple species to gain understanding as to the general principles of neural coding. Here we investigated hindbrain pyramidal cell activity in the weakly electric fish Apteronotus albifrons We found strong heterogeneities when looking at baseline activity. Additionally, ON- and OFF-type cells responded to increases and decreases of sinusoidal and noise stimuli, respectively. While both cell types displayed band-pass tuning, OFF-type cells were more broadly tuned than their ON-type counterparts. The observed heterogeneities in baseline activity as well as the greater broadband tuning of OFF-type cells were both similar to those previously reported in other weakly electric fish species, suggesting that they constitute general features of sensory processing. However, we found that peak tuning occurred at frequencies ∼15 Hz in A. albifrons, which is much lower than values reported in the closely related species Apteronotus leptorhynchus and the more distantly related species Eigenmannia virescens In response to stimuli with time-varying amplitude (i.e., envelope), ON- and OFF-type cells displayed similar high-pass tuning curves characteristic of fractional differentiation and possibly indicate optimized coding. These tuning curves were qualitatively similar to those of pyramidal cells in the closely related species A. leptorhynchus In conclusion, comparison between our and previous results reveals general and species-specific neural coding strategies. We hypothesize that differences in coding strategies, when observed, result from different stimulus distributions in the natural/social environment.