Random Spikes Research Articles

Population and individual firing behaviors in sparsely synchronized rhythms in the hippocampal dentate gyrus.

We investigate population and individual firing behaviors in sparsely synchronized rhythms (SSRs) in a spiking neural network of the hippocampal dentate gyrus (DG). The main encoding granule cells (GCs) are grouped into lamellar clusters. In each GC cluster, there is one inhibitory (I) basket cell (BC) along with excitatory (E) GCs, and they form the E-I loop. Winner-take-all competition, leading to sparse activation of the GCs, occurs in each GC cluster. Such sparsity has been thought to enhance pattern separation performed in the DG. During the winner-take-all competition, SSRs are found to appear in each population of the GCs and the BCs through interaction of excitation of the GCs with inhibition of the BCs. Sparsely synchronized spiking stripes appear successively with the population frequency Hz) in the raster plots of spikes. We also note that excitatory hilar mossy cells (MCs) control the firing activity of the GC-BC loop by providing excitation to both the GCs and the BCs. SSR also appears in the population of MCs via interaction with the GCs (i.e., GC-MC loop). Population behaviors in the SSRs are quantitatively characterized in terms of the synchronization measures. In addition, we investigate individual firing activity of GCs, BCs, and MCs in the SSRs. Individual GCs exhibit random spike skipping, leading to a multi-peaked inter-spike-interval histogram, which is well characterized in terms of the random phase-locking degree. In this case, population-averaged mean-firing-rate (MFR) is less than the population frequency . On the other hand, both BCs and MCs show "intrastripe" burstings within stripes, together with random spike skipping. Thus, the population-averaged MFR ( MC and BC) is larger than , in contrast to the case of the GCs. MC loss may occur during epileptogenesis. With decreasing the fraction of the MCs, changes in the population and individual firings in the SSRs are also studied. Finally, quantitative association between the population/individual firing behaviors in the SSRs and the winner-take-all competition is discussed.

Replica symmetry breaking in coherent and incoherent random lasing modes.

We investigate intensity fluctuations of a weakly scattering optofluidic random laser having coherent and incoherent emission fractions. The coherent part comprises random spikes, whereas the incoherent part forms a broad pedestal in the emission spectra. Evaluating the fractional ratio of the coherent and incoherent parts of the emission, a replica symmetry breaking phase transition is observed independently in both coherent and incoherent parts of the intensity. Also, the incoherent component has higher non-zero correlation values compared to those of the coherent part, implying a larger contribution to mode coupling. Moreover, survival function analysis reveals a significant contribution of the incoherent part on determining the decay profile of lasing intensity.

Thermophoretic Random Walks and Enhancement of Diffusion.

The contribution of the stochastic thermodiffusion to the diffusion enhancement is studied. The thermodiffusion of particles suspended in a liquid may hold place when the spontaneous endo- or exothermal nanoscale events similar to elementary acts of enzymatic reactions occur as the random series in the space and time. In these events, the energy can be emitted or absorbed at nanoscale during few to hundreds of picoseconds. It may cause local spontaneous temperature spikes spreading quickly in the space and decaying with time. The random local temperature spikes create local transient temperature gradients, where thermodiffusion of the molecules and particles holds place as well as the change in the physical properties of the suspending medium due to heating. These thermodiffusion random walks may appear as the enhanced usual Stokes-Einstein diffusion when the energy absorption/generation is high enough. The evaluated relative contribution of the mentioned effect to the molecular mobility is shown to be in agreement with experimental data on enzymatic reactions from the literature.

Pulsed field transmission by atomic frequency combs and random spike media: The prominent role of dispersion

A theory of pulse transmission is presented in which the medium through which the pulse propagates is characterized by an inhomogeneous distribution that is either an atomic frequency comb (AFC) or a medium of randomly spaced frequency spikes (RSM). For an AFC, we obtain analytic expressions for the transmitted field amplitude, which is composed of the (partially) transmitted incident pulse, plus a train of equally spaced echoes. For RSM, we derive expressions for the average transmitted field amplitude and field intensity. In the limit that the spike width is much less than the spike separation, normally encountered with AFC, the overall atom-field dynamics is dominated by dispersion---absorption plays a negligible role. The importance of the average group time delay of the pulse is discussed.

Heterogeneous sensors-based feature optimisation and deep learning for tool wear prediction

During machining processes, accurate prediction of cutting tool wear is prominent to prevent ineffective tool utilisation and significant resource waste. Tool wear conditions and progression involve complex physical mechanisms, and a promising approach is to deploy heterogeneous sensors and design a deep learning algorithm to conduct real-time tool wear monitoring and precious prediction. To tackle the challenge of deep learning algorithms in processing complex signals from heterogeneous sensors, in this paper, a systematic methodology is designed to combine signal de-noising, feature extraction, feature optimisation and deep learning-based prediction. In more details, the methodology is comprised of the following three steps: (i) signal de-noising is carried out by a designed Hampel filter-based method to eradicate random spikes and outliers in the signals for raw data quality enhancement; (ii) features extracted from heterogeneous sensors in the time and frequency domains are optimised using designed recursive feature elimination and cross-validation (RFECV)-based and Isomap-based methods; (iii) a convolutional neural networks (CNN) algorithm is devised to process the optimised features to implement tool wear prediction. In this paper, a case study showed that 80% features were reduced from the originally extracted features and 86% prediction accuracy was achieved based on the developed methodology. The presented methodology was benchmarked with several main-stream methodologies, and the superior performance of the methodology over those comparative methodologies in terms of prediction accuracy was exhibited.

XFEL and HHG interaction with matter: Effects of ultrashort pulses and random spikes

The theory of photoionization describing the interaction of x-ray free-electron laser (XFEL) pulses and high-harmonic-generated (HHG) radiation is generalized to ultrashort laser pulses, where the concept of the standard ionization probability per unit time in Fermi’s golden rule and in Einstein’s theory breaks down. Numerical calculations carried out in terms of a generalized photoionization probability for the total duration of pulses in the near-threshold regime demonstrate essentially nonlinear behavior, while absolute values may change by orders of magnitude for typical XFEL and HHG pulses. XFEL self-amplified spontaneous emission pulses are analyzed to reveal general features of photoionization for random and regular spikes: the dependences of the nonlinear photoionization probability on carrier frequency and spike duration are very similar, allowing an analytical expectation value approach that is valid even when there is only limited knowledge of random and regular parameters. Numerical simulations carried out for typical parameters demonstrate excellent agreement.

State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach

State of charge (SOC) estimation constitutes a critical task of battery management systems. Conventional SOC estimation methods designed for dynamic profiles have difficulties in estimating SOC for LiFePO4 batteries due to their flat open circuit voltage characteristics in the middle range of SOC. In this study, a deep neural network (DNN) based method is proposed to estimate SOC with only 10-min charging voltage and current data as the input. This method enables fast and accurate SOC estimation with an error of less than 2.03% over the entire battery SOC range. Thus, it can be used to calibrate the SOC estimation for the Ampere-hour counting method. We also demonstrate that by incorporating the DNN into a Kalman filter, the robustness of SOC estimation against random noises and error spikes can be improved. In the case of significant disturbances, the method still maintains a root mean square error of 0.385%. Moreover, the trained DNN can quickly adapt to various scenarios, including different ageing states and battery types charged at different rates, thanks to the transfer learning nature. Compared with developing a new DNN, transfer learning can provide more accurate estimation results at less training costs. By only fine-tuning one layer of the pre-trained DNN, the root mean square error can be less than 3.146% and 2.315% for aged batteries and different battery types, respectively. When more layers are fine-tuned, superior performance can be achieved.

Characterizing variability and predictability for air pollutants with stochastic models.

We investigate the dynamics of particulate matter, nitrogen oxides, and ozone concentrations in Hong Kong. Using fluctuation functions as a measure for their variability, we develop several simple data models and test their predictive power. We discuss two relevant dynamical properties, namely, the scaling of fluctuations, which is associated with long memory, and the deviations from the Gaussian distribution. While the scaling of fluctuations can be shown to be an artifact of a relatively regular seasonal cycle, the process does not follow a normal distribution even when corrected for correlations and non-stationarity due to random (Poissonian) spikes. We compare predictability and other fitted model parameters between stations and pollutants.

Transition from noise-induced to self-sustained current spiking generated by a NbOx thin film threshold switch

A transition to self-sustained current oscillations was investigated in a circuit with a NbOx thin film device, acting as an artificial neuron. Above a threshold of the applied voltage, the circuit begins to demonstrate persistent but rather random current spiking, which becomes more regular with increasing applied voltage. Experimental measurements reveal two scaling regions of the interspike interval dependence on source voltage: an initial logarithmic dependence, which transitions to a power law with an exponent of -1/2. Using a numerical model of the artificial neuron circuit, we observe good agreement with our experimental data. We show that the logarithmic scaling behavior is associated with noise-induced spiking, while the power law is attributed to noisy self-sustained oscillations, which would exist in the system even with the absence of noise. These findings stress the importance of noise in the dynamics of NbOx threshold switches and shed light on their spiking response to variation of the input signals. The reported approach to spiking control in NbOx artificial oscillation neurons can help to develop an optimal design for neuromorphic neural networks and oscillator-based computing systems.

Biomimetic integrated olfactory sensory and olfactory bulb systems in vitro based on a chip

A variety of mammalian or insect behaviors rely on the recognition of relevant odor stimuli. The olfactory system detects and translates complex olfactory stimuli (odors) through the unique and reproducible dynamic ensembles of neuronal activities. This process is involved in various types of neurons of olfactory parts, thereby encoding olfactory information or predicting progression in some neuropsychiatric diseases. In this paper, we constructed a biomimetic model including olfactory sensing system and olfactory bulb processing system to map olfactory-associated ensembles of neuronal activity. The olfactory receptor neurons (ORNs) and olfactory bulb (OB) neurons were primarily cultured and the immunofluorescence images were performed to identify the types of neurons. Diacetyl solution was used as an odor stimulus, and the spike bursts and random spike firing patterns of concentration-dependent excitatory responses were obtained from the ORNs network. The spike waveform and feature parameters were extracted including the spike number and interval in per burst to program the stimulation unit and sequences. The sequences containing odor information were applied to the OB neuronal network for the simulation of the primary olfactory processing. The response pattern and change rule of the OB neuronal network were consistent with the OB neurons affected by the neurotransmitter, which is the carrier of olfactory information transmission in vivo. This biomimetic integrated olfactory sensory and processing system can serve as a novel model for studying the physiological and pathological mechanisms of olfaction, and the pharmacological application in vitro.

Deep learning model to detect various synchrophasor data anomalies

High-density synchrophasors provide valuable information for power grid situational awareness, operation and control. Unfortunately, due to factors including communication instability and hardware failure, their data quality can be greatly deteriorated by anomalies. Since the anomalies can impact the performance of the synchrophasor applications, it is of paramount significance to propose a model to detect anomalies in synchrophasor. In this study, a convolutional neural network model is established to detect and classify the anomalies in the synchrophasor measurements. Four types of anomalies observed in actual synchrophasors including erroneous patterns, random spikes, missing points and high-frequency interferences are considered in this study. The proposed model is extensively evaluated via field-collected measurements from the synchrophasor network in Jiangsu grid, China. The superior performance of the proposed model indicates the great potential of using deep learning for the detection of abnormal synchrophasor measurements.

Nanometer-Scale Uniform Conductance Switching in Molecular Memristors.

One common challenge highlighted in almost every review article on organic resistive memory is the lack of areal switching uniformity. This, in fact, is a puzzle because a molecular switching mechanism should ideally be isotropic and produce homogeneous current switching free from electroforming. Such a demonstration, however, remains elusive to date. The reports attempting to characterize a nanoscopic picture of switching in molecular films show random current spikes, just opposite to the expectation. Here, this longstanding conundrum is resolved by demonstrating 100% spatially homogeneous current switching (driven by molecular redox) in memristors based on Ru-complexes of azo-aromatic ligands. Through a concurrent nanoscopic spatial mapping using conductive atomic force microscopy and in operando tip-enhanced Raman spectroscopy (both with resolution <7 nm),it is shown that molecular switching in the films is uniform from hundreds of micrometers down to the nanoscale and that conductance value exactly correlates with spectroscopically determined molecular redox states. This provides a deterministic molecular route to obtain spatially homogeneous, forming-free switching that can conceivably overcome the chronic problems of robustness, consistency, reproducibility, and scalability in organic memristors.

DC Arc Fault Detection For Grid-Connected Large-Scale Photovoltaic Systems

The detection and localization of dc arc faults is a major problem in large grid-connected photovoltaic systems. Hence, a time-domain technique based on the mathematical morphology called the decomposed open–close alternating sequence (DOCAS) is proposed in this article for the detection and localization of such fault conditions. The dc arc is usually sustained if sufficient fault current exists, and this phenomenon is captured by generating sustained random spikes at the output of the DOCAS algorithm that correlates to the rate of change in the dc arc current and voltage signals to detect incidences of dc arc faults when they occur. The proposed method further incorporates a technique for fault localization based on the increased effective resistance under dc arc fault conditions. Moreover, the effective fault resistance is further used as a noise filtering function for noise suppression. The method has been tested under different levels of low irradiances, fault location, and noise condition, and the results are presented to demonstrate the characteristics of the proposed technique.

Spontaneous random potential spike induction across a nonliving separator intervening two aqueous solutions and the implication of its physiological meaning

Membrane theory attributes the cause of action potential generation to the ion transport across the plasma membrane. It even states that the action potential generation is a unique phenomenon in the living cell. There have been some reports that the action potential-like potential spike is induced even in the nonliving system. Ling’s adsorption theory (LA theory) is a typical opposing concept to the membrane theory. The LA theory attributes the action potential generation mechanism to the ion adsorption-desorption process. The authors examined the LA theory and found that the membrane theory has a limitation. By contrast, the LA theory is in line with the experimental results. Therefore, it might be necessary to take into consideration the ion adsorption-desorption effect at least as a part of the action potential generation mechanism.

Spectral super-resolution spectroscopy using a random laser

Super-resolution microscopy refers to a powerful set of imaging techniques that overcome the diffraction limit. Some of these techniques, the importance of which was recognized by the 2014 Nobel Prize for chemistry, are based on the concept of image reconstruction by spatially sparse sampling. Here, we introduce the concept of super-resolution spectroscopy based on sparse sampling in the frequency domain, and show that this can be naturally achieved using a random laser source. In its chaotic regime, the emission spectrum of a random laser features sharp spikes at uncorrelated frequencies that are sparsely distributed over the emission bandwidth. These narrow lasing modes probe stochastically the spectral response of a sample, allowing it to be reconstructed with a resolution exceeding that of the spectrometer. We envision that the proposed technique will inspire a new generation of simple, cheap, high-resolution spectroscopy tools with a reduced footprint. Spectral super-resolution spectroscopy is realized by exploiting a random laser that chaotically produces sharply spiked spectral lines, representing a new generation of simple, compact and cost-effective spectroscopy tools.

A Simulation Model of Neural Activity During Hand Reaching Movement.

Introduction:The neural response is a noisy random process. The neural response to a sensory stimulus is completely equivalent to a list of spike times in the spike train. In previous studies, decreased neuronal response variability was observed in the cortex’s various areas during motor preparatory in reaching tasks. The reasons for the reduction in Neural Variability (NV) are unclear. It could be influenced by an increased firing rate, or it could result from the intrinsic characteristic of cells during the Reaction Time (RT).Methods:A neural response function with an underlying deterministic instantaneous firing rate signal and a random Poisson process spike generator was simulated in this research. Neural stimulation could help us understand the relationships between the complex data structures of cortical activities and their stability in detail during motor intention in arm-reaching tasks.Results:Our measurements indicated a similar pattern of results to the cortex, a sharp reduction of the normalized variance of simulated spike trains across all trials. We also observed a reverse relationship between activity and normalized variance.Conclusion:The present study findings could be applied to neural engineering and brain-machine interfaces for controlling external devices, like the movement of a robot arm.

Spatiotemporally random and diverse grid cell spike patterns contribute to the transformation of grid cell to place cell in a neural network model.

The medial entorhinal cortex and the hippocampus are brain regions specialized in spatial information processing. While an animal navigates around an environment, grid cells in the medial entorhinal cortex spike at multiple discrete locations, forming hexagonal grid patterns, and each grid cell is spatiotemporally dynamic with a different grid size, spacing, and orientation. In contrast, place cells in the hippocampus spike when an animal is at one or more specific locations, called a “place field”. While an animal traverses through a place field, the place cell’s spike phases relative to the hippocampal theta-frequency oscillation advance in phase, known as the “spike phase precession” phenomenon and each spike encodes the specific location within the place field. Interestingly, the medial entorhinal cortical grid cells and the hippocampal place cells are only one excitatory synapse apart. However, how the spatiotemporally dynamic multi-peaked grid cell activities are transformed into hippocampal place cell activities with spike phase precession phenomenon is yet unknown. To address this question, we construct an anatomically and physiologically realistic neural network model comprised of 10,000 grid cell models, each with a spatiotemporally dynamic grid patterns and a place cell model connected by excitatory synapses. Using this neural network model, we show that grid cells’ spike activities with spatiotemporally random and diverse grid orientation, spacing, and phases as inputs to place cell are able to generate a place field with spike phase precession. These results indicate that spatiotemporally random and diverse grid cell spike activities are essential for the formation of place cell activity observed in vivo.

Short-Term Forecasting of Electricity Spot Prices Containing Random Spikes Using a Time-Varying Autoregressive Model Combined With Kernel Regression

Forecasting spot prices of electricity is challenging because it not only contains seasonal variations, but also random, abrupt spikes, which depend on market conditions and network contingencies. In this paper, a hybrid model has been developed to forecast the spot prices of electricity in two main stages. In the first stage, the prices are forecasted using autoregressive time varying (ARXTV) model with exogenous variables. To improve the forecasting ability of the ARXTV model, the price variations in the training process have been smoothened using the wavelet technique. In the second stage, a kernel regression is used to estimate the price spikes, which are detected using support vector machine based model. In addition, mutual information technique is employed to select appropriate input variables for the model. A case study is carried out with the aid of price data obtained from the Australian energy market operator. It is demonstrated that the proposed hybrid method can accurately forecast electricity prices containing spikes.

Reinforcement Learning With Low-Complexity Liquid State Machines.

We propose reinforcement learning on simple networks consisting of random connections of spiking neurons (both recurrent and feed-forward) that can learn complex tasks with very little trainable parameters. Such sparse and randomly interconnected recurrent spiking networks exhibit highly non-linear dynamics that transform the inputs into rich high-dimensional representations based on the current and past context. The random input representations can be efficiently interpreted by an output (or readout) layer with trainable parameters. Systematic initialization of the random connections and training of the readout layer using Q-learning algorithm enable such small random spiking networks to learn optimally and achieve the same learning efficiency as humans on complex reinforcement learning (RL) tasks like Atari games. In fact, the sparse recurrent connections cause these networks to retain fading memory of past inputs, thereby enabling them to perform temporal integration across successive RL time-steps and learn with partial state inputs. The spike-based approach using small random recurrent networks provides a computationally efficient alternative to state-of-the-art deep reinforcement learning networks with several layers of trainable parameters.

An Algorithm for the Removal of Cosmic Ray Artifacts in Spectral Data Sets.

Cosmic ray artifacts may be present in all photo-electric readout systems. In spectroscopy, they present as random unidirectional sharp spikes that distort spectra and may have an affect on post-processing, possibly affecting the results of multivariate statistical classification. A number of methods have previously been proposed to remove cosmic ray artifacts from spectra but the goal of removing the artifacts while making no other change to the underlying spectrum is challenging. One of the most successful and commonly applied methods for the removal of comic ray artifacts involves the capture of two sequential spectra that are compared in order to identify spikes. The disadvantage of this approach is that at least two recordings are necessary, which may be problematic for dynamically changing spectra, and which can reduce the signal-to-noise (S/N) ratio when compared with a single recording of equivalent duration due to the inclusion of two instances of read noise. In this paper, a cosmic ray artefact removal algorithm is proposed that works in a similar way to the double acquisition method but requires only a single capture, so long as a data set of similar spectra is available. The method employs normalized covariance in order to identify a similar spectrum in the data set, from which a direct comparison reveals the presence of cosmic ray artifacts, which are then replaced with the corresponding values from the matching spectrum. The advantage of the proposed method over the double acquisition method is investigated in the context of the S/N ratio and is applied to various data sets of Raman spectra recorded from biological cells.