Random fluctuations and synaptic plasticity enhance working memory activities in the neuron-astrocyte network

Random fluctuations are inescapable feature in biological systems, but appropriate intensity of randomness can effectively facilitate information transfer and memory encoding within the nervous system. In the study, a modified spiking neuron-astrocyte network model with excitatory-inhibitory balance and synaptic plasticity is established. This model considers external input noise, and allows investigating the effects of intrinsic random fluctuations on working memory tasks. It is found that the astrocyte network, acting as a low-pass filter, reduces the noise component of the total input currents and improves the recovered images. The memory performance is enhanced by selecting appropriate intensity of random fluctuations, while excessive intensity can inhibit signal transmission of network. As the intensity of random fluctuations gradually increases, there exists a maximum value of the working memory performance. The cued recall of the network markedly decreases excessive input noise relative to test images. Meanwhile, a greater contrast effect is observed as the external input noise increases. In addition, synaptic plasticity reduces the firing rates and firing peaks of neurons, thus stabilizing the working memory activity during the test. The outcomes of this study may provide some inspirations for comprehending the role of random fluctuations in working memory mechanisms and neural information processing within the cerebral cortex.

A Stochastic Resonance P- and T-wave Detection Algorithm

An algorithm to detect P- and T-waves in an electrocardiogram (ECG) signal is presented. The algorithm has physical origins inspired by weak signal detection by leveraging stochastic resonance (SR) in a well potential. Specifically, a particle inside an underdamped monostable well is introduced with the ECG signal. The parameters defining the well and system characteristics are optimized towards enhancing the P-, R-, and T -waves while suppressing the other portions including noise-only sections. The enhanced features are detected by thresholding. Based on the performance obtained from the QT database, the algorithm achieves an average sensitivity of 99.97% for P-waves and an average sensitivity of 99.35% for T-waves, better than most P- and T-wave detection algorithms reported. Clinical Relevance- The proposed SR algorithm achieves high P- and T-wave detection performance and can potentially be integrated with implantable long-term cardiac monitors for patients experiencing rare symptoms without deteriorating the battery life.

Pulse-frequency-dependent resonance in a population of pyramidal neuron models

Stochastic resonance is known as a phenomenon whereby information transmission of weak signal or subthreshold stimuli can be enhanced by additive random noise with a suitable intensity. Another phenomenon induced by applying deterministic pulsatile electric stimuli with a pulse frequency, commonly used for deep brain stimulation (DBS), was also shown to improve signal-to-noise ratio in neuron models. The objective of this study was to test the hypothesis that pulsatile high-frequency stimulation could improve the detection of both sub- and suprathreshold synaptic stimuli by tuning the frequency of the stimulation in a population of pyramidal neuron models. Computer simulations showed that mutual information estimated from a population of neural spike trains displayed a typical resonance curve with a peak value of the pulse frequency at 80-120 Hz, similar to those utilized for DBS in clinical situations. It is concluded that a "pulse-frequency-dependent resonance" (PFDR) can enhance information transmission over a broad range of synaptically connected networks. Since the resonance frequency matches that used clinically, PFDR could contribute to the mechanism of the therapeutic effect of DBS.

A Stochastic Resonance Electrocardiogram Enhancement Algorithm for Robust QRS Detection

This study presents a new QRS detection algorithm making use of the background noise that is inevitably present in electrocardiogram (ECG) recordings. The algorithm suppresses noise, enhances the QRS-waves, and applies a threshold for QRS detection. Noise suppression and QRS enhancement are performed by a band-pass filter stage followed by a nonlinear stage based on the interaction of a particle inside an underdamped monostable potential well. The nonlinear stage maximizes the output when there is a QRS-wave and minimizes the output otherwise. One of the instruments that the nonlinear stage uses to enhance the QRS-waves is stochastic resonance, where the output is maximized for a non-zero intensity background noise. In terms of QRS-wave detection F1 score, which ranges from 98.87% to 99.99% on four major benchmarking databases (MIT-BIH Arrhythmia, QT, European ST-T, and MIT-BIH Noise Stress Test), the algorithm outperforms all existing ECG processing algorithms. The study, for the first time, demonstrates QRS-enhancement by facilitating stochastic resonance while suppressing in-band noise of ECG signals. Detecting QRS-waves as the ECG data streams, having a complexity of O(n), and not requiring any training data make the algorithm convenient for real-time ECG monitoring applications with limited computational resources.

Interlayer Connectivity Affects the Coherence Resonance and Population Activity Patterns in Two-Layered Networks of Excitatory and Inhibitory Neurons

The firing patterns of neuronal populations often exhibit emergent collective oscillations, which can display substantial regularity even though the dynamics of individual elements is very stochastic. One of the many phenomena that is often studied in this context is coherence resonance, where additional noise leads to improved regularity of spiking activity in neurons. In this work, we investigate how the coherence resonance phenomenon manifests itself in populations of excitatory and inhibitory neurons. In our simulations, we use the coupled FitzHugh-Nagumo oscillators in the excitable regime and in the presence of neuronal noise. Formally, our model is based on the concept of a two-layered network, where one layer contains inhibitory neurons, the other excitatory neurons, and the interlayer connections represent heterotypic interactions. The neuronal activity is simulated in realistic coupling schemes in which neurons within each layer are connected with undirected connections, whereas neurons of different types are connected with directed interlayer connections. In this setting, we investigate how different neurophysiological determinants affect the coherence resonance. Specifically, we focus on the proportion of inhibitory neurons, the proportion of excitatory interlayer axons, and the architecture of interlayer connections between inhibitory and excitatory neurons. Our results reveal that the regularity of simulated neural activity can be increased by a stronger damping of the excitatory layer. This can be accomplished with a higher proportion of inhibitory neurons, a higher fraction of inhibitory interlayer axons, a stronger coupling between inhibitory axons, or by a heterogeneous configuration of interlayer connections. Our approach of modeling multilayered neuronal networks in combination with stochastic dynamics offers a novel perspective on how the neural architecture can affect neural information processing and provide possible applications in designing networks of artificial neural circuits to optimize their function via noise-induced phenomena.

Adjunct high-frequency transcranial random noise stimulation over the lateral prefrontal cortex improves negative symptoms of schizophrenia: A randomized, double-blind, sham-controlled pilot study

High-frequency transcranial random noise stimulation (hf-tRNS) is a non-invasive neuromodulatory technique capable of increasing human cortex excitability. There were only published case reports on the use of hf-tRNS targeting the lateral prefrontal cortex in treating negative symptoms of schizophrenia, thus necessitating systematic investigation. We designed a randomized, double-blind, sham-controlled trial in a cohort of stabilized schizophrenia patients to examine the efficacy of add-on hf-tRNS (100-640 Hz; 2 mA; 20 min) using a high definition 4 × 1 electrode montage (anode AF3, cathodes AF4, F2, F6, and FC4) in treating negative symptoms (ClinicalTrials.gov ID: NCT04038788). Participants received either active hf-tRNS or sham twice daily for 5 consecutive weekdays. Primary outcome measure was the change over time in the Positive and Negative Syndrome Scale Factor Score for Negative Symptoms (PANSS-FSNS), which was measured at baseline, after 10-session stimulation, and at one-week and one-month follow-ups. Among 36 randomized patients, 35 (97.2%) completed the trial. Intention-to-treat analysis showed a significantly greater decrease in PANSS-FSNS score after active (-17.11%) than after sham stimulation (-1.68%), with a large effect size (Cohen's d = 2.16, p < 0.001). The beneficial effect lasted for up to one month. In secondary-outcome analyses, the authors observed improvements with hf-tRNS of disorganization symptoms, unawareness of negative symptoms, subjective response to taking antipsychotics, and antipsychotic-induced extrapyramidal symptoms. No effects were observed on the neurocognitive performance and other outcome measures. Overall, hf-tRNS was safe and efficacious in improving negative symptoms. Our promising findings should be confirmed in a larger sample of patients with predominant negative symptoms.

Vibrational resonance in a randomly connected neural network

A randomly connected network is constructed with similar characteristics (e.g., the ratio of excitatory and inhibitory neurons, the connection probability between neurons, and the axonal conduction delays) as that in the mammalian neocortex and the effects of high-frequency electrical field on the response of the network to a subthreshold low-frequency electrical field are studied in detail. It is found that both the amplitude and frequency of the high-frequency electrical field can modulate the response of the network to the low-frequency electric field. Moreover, vibrational resonance (VR) phenomenon induced by the two types of electrical fields can also be influenced by the network parameters, such as the neuron population, the connection probability between neurons and the synaptic strength. It is interesting that VR is found to be related with the ratio of excitatory neurons that are under high-frequency electrical stimuli. In summary, it is suggested that the interaction of excitatory and inhibitory currents is also an important factor that can influence the performance of VR in neural networks.

Brownian Optogenetic-Noise-Photostimulation on the Brain Amplifies Somatosensory-Evoked Field Potentials

Stochastic resonance (SR) is an inherent and counter-intuitive mechanism of signal-to-noise ratio (SNR) facilitation in biological systems associated with the application of an intermediate level of noise. As a first step to investigate in detail this phenomenon in the somatosensory system, here we examined whether the direct application of noisy light on pyramidal neurons from the mouse-barrel cortex expressing a light-gated channel channelrhodopsin-2 (ChR2) can produce facilitation in somatosensory evoked field potentials. Using anesthetized Thy1-ChR2-YFP transgenic mice, and a new neural technology, that we called Brownian optogenetic-noise-photostimulation (BONP), we provide evidence for how BONP directly applied on the barrel cortex modulates the SNR in the amplitude of whisker-evoked field potentials (whisker-EFP). In all transgenic mice, we found that the SNR in the amplitude of whisker-EFP (at 30% of the maximal whisker-EFP) exhibited an inverted U-like shape as a function of the BONP level. As a control, we also applied the same experimental paradigm, but in wild-type mice, as expected, we did not find any facilitation effects. Our results show that the application of an intermediate intensity of BONP on the barrel cortex of ChR2 transgenic mice amplifies the SNR of somatosensory whisker-EFPs. This result may be relevant to explain the improvements found in sensory detection in humans produced by the application of transcranial-random-noise-stimulation (tRNS) on the scalp.

Weak electric fields detectability in a noisy neural network

We investigate the detectability of weak electric field in a noisy neural network based on Izhikevich neuron model systematically. The neural network is composed of excitatory and inhibitory neurons with similar ratio as that in the mammalian neocortex, and the axonal conduction delays between neurons are also considered. It is found that the noise intensity can modulate the detectability of weak electric field. Stochastic resonance (SR) phenomenon induced by white noise is observed when the weak electric field is added to the network. It is interesting that SR almost disappeared when the connections between neurons are cancelled, suggesting the amplification effects of the neural coupling on the synchronization of neuronal spiking. Furthermore, the network parameters, such as the connection probability, the synaptic coupling strength, the scale of neuron population and the neuron heterogeneity, can also affect the detectability of the weak electric field. Finally, the model sensitivity is studied in detail, and results show that the neural network model has an optimal region for the detectability of weak electric field signal.

Reverse stochastic resonance in a hippocampal CA1 neuron model

Stochastic resonance (SR) is a ubiquitous and counter- intuitive phenomenon whereby the addition of noise to a non-linear system can improve the detection of sub-threshold signals. The "signal" is normally periodic or deterministic whereas the "noise" is normally stochastic. However, in neural systems, signals are often stochastic. Moreover, periodic signals are applied near neurons to control neural excitability (i.e. deep brain stimulation). We therefore tested the hypothesis that a quasi-periodic signal applied to a neural network could enhance the detection of a stochastic neural signal (reverse stochastic resonance). Using computational methods, a CA1 hippocampal neuron was simulated and a Poisson distributed subthreshold synaptic input ("signal") was applied to the synaptic terminals. A periodic or quasi periodic pulse train at various frequencies ("noise") was applied to an extracellular electrode located near the neuron. The mutual information and information transfer rate between the output and input of the neuron were calculated. The results display the signature of stochastic resonance with information transfer reaching a maximum value for increasing power (or frequency) of the "noise". This result shows that periodic signals applied extracellularly can improve the detection of subthreshold stochastic neural signals. The optimum frequency (110 Hz) is similar to that used in patients with Parkinson's suggesting that this phenomenon could play a role in the therapeutic effect of high frequency stimulation.

On how correlations between excitatory and inhibitory synaptic inputs maximize the information rate of neuronal firing

Cortical neurons receive barrages of excitatory and inhibitory inputs which are not independent, as network structure and synaptic kinetics impose statistical correlations. Experiments in vitro and in vivo have demonstrated correlations between inhibitory and excitatory synaptic inputs in which inhibition lags behind excitation in cortical neurons. This delay arises in feed-forward inhibition (FFI) circuits and ensures that coincident excitation and inhibition do not preclude neuronal firing. Conversely, inhibition that is too delayed broadens neuronal integration times, thereby diminishing spike-time precision and increasing the firing frequency. This led us to hypothesize that the correlation between excitatory and inhibitory synaptic inputs modulates the encoding of information of neural spike trains. We tested this hypothesis by investigating the effect of such correlations on the information rate (IR) of spike trains using the Hodgkin-Huxley model in which both synaptic and membrane conductances are stochastic. We investigated two different synaptic input regimes: balanced synaptic conductances and balanced currents. Our results show that correlations arising from the synaptic kinetics, τ, and millisecond lags, δ, of inhibition relative to excitation strongly affect the IR of spike trains. In the regime of balanced synaptic currents, for short time lags (δ ~ 1 ms) there is an optimal τ that maximizes the IR of the postsynaptic spike train. Given the short time scales for monosynaptic inhibitory lags and synaptic decay kinetics reported in cortical neurons under physiological contexts, we propose that FFI in cortical circuits is poised to maximize the rate of information transfer between cortical neurons. Our results also provide a possible explanation for how certain drugs and genetic mutations affecting the synaptic kinetics can deteriorate information processing in the brain.

White noise improves learning by modulating activity in dopaminergic midbrain regions and right superior temporal sulcus

In neural systems, information processing can be facilitated by adding an optimal level of white noise. Although this phenomenon, the so-called stochastic resonance, has traditionally been linked with perception, recent evidence indicates that white noise may also exert positive effects on cognitive functions, such as learning and memory. The underlying neural mechanisms, however, remain unclear. Here, on the basis of recent theories, we tested the hypothesis that auditory white noise, when presented during the encoding of scene images, enhances subsequent recognition memory performance and modulates activity within the dopaminergic midbrain (i.e., substantia nigra/ventral tegmental area, SN/VTA). Indeed, in a behavioral experiment, we can show in healthy humans that auditory white noise-but not control sounds, such as a sinus tone-slightly improves recognition memory. In an fMRI experiment, white noise selectively enhances stimulus-driven phasic activity in the SN/VTA and auditory cortex. Moreover, it induces stronger connectivity between SN/VTA and right STS, which, in addition, exhibited a positive correlation with subsequent memory improvement by white noise. Our results suggest that the beneficial effects of auditory white noise on learning depend on dopaminergic neuromodulation and enhanced connectivity between midbrain regions and the STS-a key player in attention modulation. Moreover, they indicate that white noise could be particularly useful to facilitate learning in conditions where changes of the mesolimbic system are causally related to memory deficits including healthy and pathological aging.

Dynamical structure underlying inverse stochastic resonance and its implications

We investigate inverse stochastic resonance (ISR), a recently reported phenomenon in which the spiking activity of a Hodgkin-Huxley model neuron subject to external noise exhibits a pronounced minimum as the noise intensity increases. We clarify the mechanism that underlies ISR and show that its most surprising features are a consequence of the dynamical structure of the model. Furthermore, we show that the ISR effect depends strongly on the procedures used to measure it. Our results are important for the experimentalist who seeks to observe the ISR phenomenon.

Phase-noise-induced resonance in arrays of coupled excitable neural models

Recently, it is observed that, in a single neural model, phase noise (time-varying signal phase) arising from an external stimulating signal can induce regular spiking activities even if the signal is subthreshold. In addition, it is also uncovered that there exists an optimal phase noise intensity at which the spiking rhythm coincides with the frequency of the subthreshold signal, resulting in a phase-noise-induced resonance phenomenon. However, neurons usually do not work alone, but are connected in the form of arrays or blocks. Therefore, we study the spiking activity induced by phase noise in arrays of globally and locally coupled excitable neural models. We find that there also exists an optimal phase noise intensity for generating large neural response and such an optimal value is significantly decreased compared to an isolated single neuron case, which means the detectability in response to the subthreshold signal of neurons is sharply improved because of the coupling. In addition, we reveal two new resonance behaviors in the neuron ensemble with the presence of phase noise: there exist optimal values of both coupling strength and system size, where the coupled neurons generate regular spikes under subthreshold stimulations, which are called as coupling strength and system size resonance, respectively. Finally, the dependence of phase-noise-induced resonance on signal frequency is also examined.

Control strategies for underactuated neural ensembles driven by optogenetic stimulation

Motivated by experiments employing optogenetic stimulation of cortical regions, we consider spike control strategies for ensembles of uncoupled integrate and fire neurons with a common conductance input. We construct strategies for control of spike patterns, that is, multineuron trains of action potentials, up to some maximal spike rate determined by the neural biophysics. We emphasize a constructive role for parameter heterogeneity, and find a simple rule for controllability in pairs of neurons. In particular, we determine parameters for which common drive is not limited to inducing synchronous spiking. For large ensembles, we determine how the number of controllable neurons varies with the number of observed (recorded) neurons, and what collateral spiking occurs in the full ensemble during control of the subensemble. While complete control of spiking in every neuron is not possible with a single input, we find that a degree of subensemble control is made possible by exploiting dynamical heterogeneity. As most available technologies for neural stimulation are underactuated, in the sense that the number of target neurons far exceeds the number of independent channels of stimulation, these results suggest partial control strategies that may be important in the development of sensory neuroprosthetics and other neurocontrol applications.

Effects of time delay on the stochastic resonance in small-world neuronal networks

The effects of time delay on stochastic resonance in small-world neuronal networks are investigated. Without delay, an intermediate intensity of additive noise is able to optimize the temporal response of the neural system to the subthreshold periodic signal imposed on all neurons constituting the network. The time delay in the coupling process can either enhance or destroy stochastic resonance of neuronal activity in the small-world network. In particular, appropriately tuned delays can induce multiple stochastic resonances, which appear intermittently at integer multiples of the oscillation period of weak external forcing. It is found that the delay-induced multiple stochastic resonances are most efficient when the forcing frequency is close to the global-resonance frequency of each individual neuron. Furthermore, the impact of time delay on stochastic resonance is largely independent of the small-world topology, except for resonance peaks. Considering that information transmission delays are inevitable in intra- and inter-neuronal communication, the presented results could have important implications for the weak signal detection and information propagation in neural systems.

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.

Stochastic resonance with a mixture of sub-and supra-threshold stimuli in a population of neuron models

This paper presents a novel type of stochastic resonance (SR) with a mixture of sub- and supra-threshold stimuli in a population of neuron models beyond regular SR and Supra-threshold SR (SSR) phenomena. We investigate through computer simulations if the novel type of SR can be observed or not, using the mutual information (MI) estimated from a population of neural spike trains as an index of information transmission. Computer simulations showed that the MI had a typical type of SR curves, even when the balance between sub-and supra-threshold stimuli was varied, suggesting the novel type of SR. Moreover, the peak of MI increased as the balance of supra-threshold stimuli got stronger, i.e., as the situation was getting close to the SSR from the regular SR. This finding could accelerate our understanding about how fluctuations play a role in processing information carried by a mixture of sub-and supra-threshold stimuli.