Random Spikes Research Articles

Electrophysiological features of cortical 3D networks are deeply modulated by scaffold properties.

Three-dimensionality (3D) was proven essential for developing reliable models for different anatomical compartments and many diseases. However, the neuronal compartment still poses a great challenge as we still do not understand precisely how the brain computes information and how the complex chain of neuronal events can generate conscious behavior. Therefore, a comprehensive model of neuronal tissue has not yet been found. The present work was conceived in this framework: we aimed to contribute to what must be a collective effort by filling in some information on possible 3D strategies to pursue. We compared directly different kinds of scaffolds (i.e., PDMS sponges, thermally crosslinked hydrogels, and glass microbeads) in their effect on neuronal network activity recorded using micro-electrode arrays. While the overall rate of spiking activity remained consistent, the type of scaffold had a notable impact on bursting dynamics. The frequency, density of bursts, and occurrence of random spikes were all affected. The examination of inter-burst intervals revealed distinct burst generation patterns unique to different scaffold types. Network burst propagation unveiled divergent trends among configurations. Notably, it showed the most differences, underlying that functional variations may arise from a different 3D spatial organization. This evidence suggests that not all 3D neuronal constructs can sustain the same level of richness of activity. Furthermore, we commented on the reproducibility, efficacy, and scalability of the methods, where the beads still offer superior performances. By comparing different 3D scaffolds, our results move toward understanding the best strategies to develop functional 3D neuronal units for reliable pre-clinical studies.

Dynamic assemblies of parvalbumin interneurons in brain oscillations

Brain oscillations are crucial for perception, memory, and behavior. Parvalbumin-expressing (PV) interneurons are critical for these oscillations, but their population dynamics remain unclear. Using voltage imaging, we simultaneously recorded membrane potentials in up to 26 PV interneurons invivo during hippocampal ripple oscillations in mice. We found that PV cells generate ripple-frequency rhythms by forming highly dynamic cell assemblies. These assemblies exhibit rapid and significant changes from cycle to cycle, varying greatly in both size and membership. Importantly, this variability is not just random spiking failures of individual neurons. Rather, the activities of other PV cells contain significant information about whether a PV cell spikes or not in a given cycle. This coordination persists without network oscillations, and it exists in subthreshold potentials even when the cells are not spiking. Dynamic assemblies of interneurons may provide a new mechanism to modulate postsynaptic dynamics and impact cognitive functions flexibly and rapidly.

Dual integration design of approximate random weight generator and computation-in-memory for event-based neuromorphic computing

This paper comprehensively analyses dual integration of approximate random weight generator (ARWG) and computation-in-memory for event-based neuromorphic computing. ARWG can generate approximate random weights and perform multiply-accumulate (MAC) operation for reservoir computing (RC) and random weight spiking neural network (SNN). Because of using device variation to generate random weights, ARWG does not require any random number generators (RNGs). Because RC and random weight SNN allow approximate randomness, ARWG only needs to generate approximate random weights, which does not require error-correcting code to correct weights to make the randomness accurate. Moreover, ARWG has a read port for MAC operation. In this paper, the randomness of random weights generated by the proposed ARWG is evaluated by Hamming distance and Hamming weight. As a result, this paper reveals that the randomness required for ARWG is much lower than that for physically unclonable functions and RNGs, and thus the proposed ARWG achieves high recognition accuracy.

Free will: An Example of the Dopaminergic System.

Neuroscience has convinced people that much of their behavior is determined by causes unknown to them and beyond their control. However, are advances in neuroscience truly a prerequisite for such beliefs? Robert Kane's theory of ultimate responsibility is libertarian theory. Its innovative nature makes it possible to discuss the neurophysiological basis of its postulates. Using the functions of the midbrain dopaminergic system as an example, this article provides an overview of this neurophysiological basis. According to Kane, if we are to be ultimately responsible for our wills as well as for our actions, some actions in our lives must lack sufficient motives and causes. These are self-forming actions. Dopamine is hypothesized to mediate self-forming action execution. Dopamine not only mediates action but also ensures synaptic plasticity in the brain, that is, learning from action; hence, dopamine changes the acting individual and provides the formation of our own wills. The basal ganglia, which are the main target of dopamine in the brain, act through parallel pathways and are involved in decision-making processes. Dopamine is also involved in the regulation of the neurodynamical properties of prefrontal cortex networks with random spiking noise. It can be assumed that the activity of the dopaminergic system is closely related to the physiological basis of free will.

Moving beyond the mean: an analysis of faecal corticosterone metabolites shows substantial variability both within and across white-tailed deer populations.

Glucocorticoid (GC) levels have significant impacts on the health and behaviour of wildlife populations and are involved in many essential body functions including circadian rhythm, stress physiology and metabolism. However, studies of GCs in wildlife often focus on estimating mean hormone levels in populations, or a subset of a population, rather than on assessing the entire distribution of hormone levels within populations. Additionally, explorations of population GC data are limited due to the tradeoff between the number of individuals included in studies and the amount of data per individual that can be collected. In this study, we explore patterns of GC level distributions in three white-tailed deer (Odocoileus virginianus) populations using a non-invasive, opportunistic sampling approach. GC levels were assessed by measuring faecal corticosterone metabolite levels ('fCMs') from deer faecal samples throughout the year. We found both population and seasonal differences in fCMs but observed similarly shaped fCM distributions in all populations. Specifically, all population fCM cumulative distributions were found to be very heavy-tailed. We developed two toy models of acute corticosterone elevation in an effort to recreate the observed heavy-tailed distributions. We found that, in all three populations, cumulative fCM distributions were better described by an assumption of large, periodic spikes in corticosterone levels every few days, as opposed to an assumption of random spikes in corticosterone levels. The analyses presented in this study demonstrate the potential for exploring population-level patterns of GC levels from random, opportunistically sampled data. When taken together with individual-focused studies of GC levels, such analyses can improve our understanding of how individual hormone production scales up to population-level patterns.

Demonstration of Pavlov Associative Memory by Implementation of Rate Coding Using Magnetic Tunnel Junction Neurons

In this paper, a fully functioning Pavlov associative memory system, one of the most famous examples of the biological brain function, is implemented by a basis of rate coding. In order to create a neural signal which is capable of emulating the synaptic weight based on rate coding, a neural signal is constructed by utilizing telegraphic switching of the magnetic tunnel junction (MTJ), a random magnetization reversal phenomenon. Spike rate of the MTJ neuron is controllable by adjusting the STT current bias, creating signals with rate information that is capable of implementing rate coding scheme as a weight update method for the TiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> memristor synapse. The proposed neuron creates random spike signals containing rate information, which participates in emulating Pavlov associative memory characteristics such as learning and forgetting, as well as rate controlling characteristics. The suggested structure shows a coding scheme which both input and output neurons participate, which can be expanded to larger neuromorphic neural network applications.

Control of Network Bursting in a Model Spiking Network Supplied with Memristor—Implemented Plasticity

We consider an unstructured neuron network model composed of excitatory and inhibitory neurons. The synaptic connections are supplied with spike timing-dependent plasticity (STDP). We take the STDP model implemented using a memristor. In normal conditions, the network forms so-called bursting discharges typical of unstructured living networks in dissociated neuronal cultures. Incorporating a biologically inspired model, we demonstrate how memristive plasticity emulates spike timing-dependent plasticity, which is crucial for regulating synchronous brain activity. We have found that, when the memristor-based STDP for inhibitory connections is activated, the bursting dynamics are suppressed and the network turns to a random spiking mode. The dependence of bursting properties on the degree of the memristor-based STDP plasticity is analyzed. These findings hold implications for advancing invasive neurointerfaces and for the identification and management of epileptiform activity.

Stochastic transition in synchronized spiking nanooscillators

This work reports that synchronization of Mott material-based nanoscale coupled spiking oscillators can be drastically different from that in conventional harmonic oscillators. We investigated the synchronization of spiking nanooscillators mediated by thermal interactions due to the close physical proximity of the devices. Controlling the driving voltage enables in-phase 1:1 and 2:1 integer synchronization modes between neighboring oscillators. Transition between these two integer modes occurs through an unusual stochastic synchronization regime instead of the loss of spiking coherence. In the stochastic synchronization regime, random length spiking sequences belonging to the 1:1 and 2:1 integer modes are intermixed. The occurrence of this stochasticity is an important factor that must be taken into account in the design of large-scale spiking networks for hardware-level implementation of novel computational paradigms such as neuromorphic and stochastic computing.

Sialic acid biosynthesis pathway blockade disturbs neuronal network formation in human iPSC-derived excitatory neurons.

N-acetylneuraminic acid (sialic acid) is present in large quantities in the brain and plays a crucial role in brain development, learning, and memory formation. How sialic acid contributes to brain development is not fully understood. The purpose of this study was to determine the effects of reduced sialylation on network formation in human iPSC-derived neurons (iNeurons). Using targeted mass spectrometry and antibody binding, we observed an increase in free sialic acid and polysialic acid during neuronal development, which was disrupted by treatment of iNeurons with a synthetic inhibitor of sialic acid biosynthesis. Sialic acid inhibition disturbed synapse formation and network formation on microelectrode array (MEA), showing short but frequent (network) bursts and an overall lower firing rate, and higher percentage of random spikes. This study shows that sialic acid is necessary for neuronal network formation during human neuronal development and provides a physiologically relevant model to study the role of sialic acid in patient-derived iNeurons.

Evaluation of predictive capability of Bayesian spatio-temporal models for Covid-19 spread

BackgroundBayesian models have been applied throughout the Covid-19 pandemic especially to model time series of case counts or deaths. Fewer examples exist of spatio-temporal modeling, even though the spatial spread of disease is a crucial factor in public health monitoring. The predictive capabilities of infectious disease models is also important.MethodsIn this study, the ability of Bayesian hierarchical models to recover different parts of the variation in disease counts is the focus. It is clear that different measures provide different views of behavior when models are fitted prospectively. Over a series of time horizons one step predictions have been generated and compared for different models (for case counts and death counts). These Bayesian SIR models were fitted using MCMC at 28 time horizons to mimic prospective prediction. A range of goodness of prediction measures were analyzed across the different time horizons.ResultsA particularly important result is that the peak intensity of case load is often under-estimated, while random spikes in case load can be mimicked using time dependent random effects. It is also clear that during the early wave of the pandemic simpler model forms are favored, but subsequently lagged spatial dependence models for cases are favored, even if the sophisticated models perform better overall.DiscussionThe models fitted mimic the situation where at a given time the history of the process is known but the future must be predicted based on the current evolution which has been observed. Using an overall ‘best’ model for prediction based on retrospective fitting of the complete pandemic waves is an assumption. However it is also clear that this case count model is well favored over other forms. During the first wave a simpler time series model predicts case counts better for counties than a spatially dependent one. The picture is more varied for morality.ConclusionsFrom a predictive point of view it is clear that spatio-temporal models applied to county level Covid-19 data within the US vary in how well they fit over time and also how well they predict future events. At different times, SIR case count models and also mortality models with cumulative counts perform better in terms of prediction. A fundamental result is that predictive capability of models varies over time and using the same model could lead to poor predictive performance.In addition it is clear that models addressing the spatial context for case counts (i.e. with lagged neighborhood terms) and cumulative case counts for mortality data are clearly better at modeling spatio-temporal data which is commonly available for the Covid-19 pandemic in different areas of the globe.

Modeling IP[formula omitted]-induced Ca[formula omitted] signaling based on its interspike interval statistics

Inositol 1,4,5-trisphosphate (IP3)-induced Ca2+ signaling is a second messenger system used by almost all eukaryotic cells. Recent research demonstrated randomness of Ca2+ signaling on all structural levels. We compile eight general properties of Ca2+ spiking common to all cell types investigated and suggest a theory of Ca2+ spiking starting from the random behavior of IP3 receptor channel clusters mediating the release of Ca2+ from the endoplasmic reticulum capturing all general properties and pathway-specific behavior. Spike generation begins after the absolute refractory period of the previous spike. According to its hierarchical spreading from initiating channel openings to cell level, we describe it as a first passage process from none to all clusters open while the cell recovers from the inhibition which terminated the previous spike. Our theory reproduces the exponential stimulation response relation of the average interspike interval Tav and its robustness properties, random spike timing with a linear moment relation between Tav and the interspike interval SD and its robustness properties, sensitive dependency of Tav on diffusion properties, and nonoscillatory local dynamics. We explain large cell variability of Tav observed in experiments by variability of channel cluster coupling by Ca2+-induced Ca2+ release, the number of clusters, and IP3 pathway component expression levels. We predict the relation between puff probability and agonist concentration and [IP3] and agonist concentration. Differences of spike behavior between cell types and stimulating agonists are explained by the different types of negative feedback terminating spikes. In summary, the hierarchical random character of spike generation explains all of the identified general properties.

Artificial Spiking Neuron with Bursting Dynamics for Noise-Resistant Neuromorphic Coding

Emulating complex neuronal functionalities in human brains using emerging biomimetic electronics is critical for next-generation brain-like chips and artificial general intelligence. Burst firing, featured with the repeated firing of discrete groups of spikes, is high-order neuronal dynamics for reliable signal transmission, coding, and processing. Here, we report a memristor (Pt/Co3O4–x/ITO) that can natively produce periodic voltage oscillation groups under current stimulation, capturing the burst-firing features of actual biological neurons. This behavior stems from the dynamic formation/rupture of conductive filaments regulated by the VO concentration at the grain boundaries. The burst frequency can be tuned by adjusting the driving current intensity and used to implement burst frequency coding. The bursting neuron-based artificial neural system achieves a recognition accuracy of 86% for the Fashion-MNIST tasks, under the disturbance of random noise spikes. This work provides a promising platform to realize complex neuronal dynamics and to construct robust brain-inspired neuromorphic systems.

Ictal blinking triggered by isolated spikes as the only manifestation of seizures.

To describe blinking as the only manifestation of seizures from isolated focal and generalized cortical spikes and investigate the relationship between blinks and epileptic discharges. We measured the latency from the onset of spikes to the onset of blinks in two patients using electroencephalogram (EEG) and an electrooculogram (EOG), and calculated the median latency in both cases. We analyzed the latency from spike onset to the onset of additional specific eye movements, seen only in the second case. To determine the frequency of spontaneous blinks (not triggered by spikes), we defined a "control point" at 45 s following a random spike for the first case. We tested for statistically significant associations between latencies of blinks (Case 1) as well as between latencies of blinks and specific eye movements (Case 2). A total of 174 generalized spike-waves followed by a blink were analyzed in the first patient. Approximately 61% of the blinks occurred within 150-450 ms after the onset of the spike. Median latency for blinks following a spike was 294 ms compared to 541 ms for control blinks (p=.02). For the second patient, a total of 160 eye movements following a right occipito-parietal spike were analyzed. The median spike-blink latency in the second case was 497 milliseconds. Median latencies of spike onset to contralateral oblique eye movements with blink and left lateral eye movements were 648 and 655 milliseconds, respectively. Our study shows that isolated cortical spikes can induce epileptic seizures consisting exclusively of blinks. These findings emphasize the importance of careful EEG and EOG analysis to determine blinking as the only ictal phenomenon. We additionally describe a new technique to prove the temporal relationship between cortical discharges and a specific movement when, in addition to the movements triggered by a spike, the same movement is also spontaneously performed by the patient (in this case, blinking).

Modelling and estimating volatilities in exchange rate return and the response of exchange rates to oil shock

Developing countries have persistently witnessed volatile exchange. Such volatility triggered instability in their exchange rates which induced colossal fluctuations in currency rates leading to uncertainty for both the consumers and firms. All these have instigated changes in official exchange rates that are harmful to underlie trade patterns in these countries. This study estimated fluctuations in daily exchange rate returns of ten African countries using generalized autoregressive conditional heteroskedasticity (GARCH) models, having ascertained the significance of autoregressive conditional heteroskedasticity (ARCH) effects. Structural vector autoregression (SVAR) estimator was utilized. Results showed Kenya shilling is the most relatively stable currency, whereas the Malawian kwacha is the most volatile among the currencies. There had been a series of random spikes in the exchange rate of Ghanaian cedi. Ghana and Kenya exchange rates are best projected using EGARCH, whereas SGARCH may be more efficient in estimating the volatility of Morocco and Zambia exchange rates. Leverage effects indicated a considerable magnitude of the adverse impact of bad news in the foreign exchange (FX) markets of Ghana and Zambia. Volatility shocks are expected to last in the future in those countries.

Toward White Light Random Lasing Emission Based on Strained Nano Polygermanium Doped with Tin via Metal-Induced Crystallization (MIC)

Solubility of Sn in Ge network gives it a preference for photonic applications, because of the direct transition in GeSn alloy. Here, we employed the metal-induced crystallization (MIC) process of amorphous Ge and Si via Sn as a novel mechanism to incorporate Sn inside Ge and Si networks. (Al/Si/Sn/Ge/Sn) and (Al/Ge/Sn/Ge/Sn) multilayers are deposited by thermal vacuum evaporation on different substrates. The devices are annealed under low vacuum at 500 °C to incorporate the oxygen for band-gap tuning. The structure of Ge-doped nanocrystals is investigated. The direct transition and band-gap values have been estimated using diffuse reflectance spectroscopy and photoluminescence (PL) measurements. PL indicated that the junctions have emissions from visible to NIR regions that make them promising as optically pumped white-light sources as well as waveguide applications, and the impact of the base substrate on enhancing the emission has been investigated via PL measurements. Electroluminescence measurements show that the prepared heterostructures on fluorine-doped tin oxide (FTO) substrate have sharp random lasing spikes over the range of PL samples spectra as the sample can lase randomly by light scattering through the Ge-doped nanocrystalline materials. The charge carrier lifetime measurements show high lifetime for the prepared sample. These give them the chance to be a candidate for white-light random laser diode applications.

Origin of the efficiency of spike timing-based neural computation for processing temporal information

Although the advantage of spike timing-based over rate-based network computation has been recognized, the underlying mechanism remains unclear. Using Tempotron and Perceptron as elementary neural models, we examined the intrinsic difference between spike timing-based and rate-based computations. For more direct comparison, we modified Tempotron computation into rate-based computation with the retention of some temporal information. Previous studies have shown that spike timing-based computation are computationally more powerful than rate-based computation in terms of the number of computational units required and the capability in classifying random patterns. Our study showed that spike timing-based and rate-based Tempotron computations provided similar capability in classifying random spike patterns, as well as in text sentiment classification and spam text detection. However, spike timing-based computation is superior in performing a task involving discriminating forward vs. reverse sequence of events, i.e., information mainly temporal in nature. Further studies revealed that this superiority required the asymmetry in the profile of the postsynaptic potential (PSP), and that temporal sequence information was converted to biased spatial distribution of synaptic weight modifications during learning. Thus, the intrinsic PSP asymmetry is a mechanistic basis for the high efficiency of spike timing-based computation for processing temporal information.

A Simulation Study of Tolerance of Breathing Amplitude Variations in Radiotherapy of Lung Cancer Using 4DCT and Time-Resolved 4DMRI.

As patient breathing irregularities can introduce a large uncertainty in targeting the internal tumor volume (ITV) of lung cancer patients, and thereby affect treatment quality, this study evaluates dose tolerance of tumor motion amplitude variations in ITV-based volumetric modulated arc therapy (VMAT). A motion-incorporated planning technique was employed to simulate treatment delivery of 10 lung cancer patients' clinical VMAT plans using original and three scaling-up (by 0.5, 1.0, and 2.0 cm) motion waveforms from single-breath four-dimensional computed tomography (4DCT) and multi-breath time-resolved 4D magnetic resonance imaging (TR-4DMRI). The planning tumor volume (PTV = ITV + 5 mm margin) dose coverage (PTV D95%) was evaluated. The repeated waveforms were used to move the isocenter in sync with the clinical leaf motion and gantry rotation. The continuous VMAT arcs were broken down into many static beam fields at the control points (2°-interval) and the composite plan represented the motion-incorporated VMAT plan. Eight motion-incorporated plans per patient were simulated and the plan with the native 4DCT waveform was used as a control. The first (D95% ≤ 95%) and second (D95% ≤ 90%) plan breaching points due to motion amplitude increase were identified and analyzed. The PTV D95% in the motion-incorporated plans was 99.4 ± 1.0% using 4DCT, closely agreeing with the corresponding ITV-based VMAT plan (PTV D95% = 100%). Tumor motion irregularities were observed in TR-4DMRI and triggered D95% ≤ 95% in one case. For small tumors, 4 mm extra motion triggered D95% ≤ 95%, and 6-8 mm triggered D95% ≤ 90%. For large tumors, 14 mm and 21 mm extra motions triggered the first and second breaching points, respectively. This study has demonstrated that PTV D95% breaching points may occur for small tumors during treatment delivery. Clinically, it is important to monitor and avoid systematic motion increase, including baseline drift, and large random motion spikes through threshold-based beam gating.

Forecasting the Risk Factor of Frontier Markets: A Novel Stacking Ensemble of Neural Network Approach

Forecasting the risk factor of the financial frontier markets has always been a very challenging task. Unlike an emerging market, a frontier market has a missing parameter named “volatility”, which indicates the market’s risk and as a result of the absence of this missing parameter and the lack of proper prediction, it has almost become difficult for direct customers to invest money in frontier markets. However, the noises, seasonality, random spikes and trends of the time-series datasets make it even more complicated to predict stock prices with high accuracy. In this work, we have developed a novel stacking ensemble of the neural network model that performs best on multiple data patterns. We have compared our model’s performance with the performance results obtained by using some traditional machine learning ensemble models such as Random Forest, AdaBoost, Gradient Boosting Machine and Stacking Ensemble, along with some traditional deep learning models such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term (BiLSTM). We have calculated the missing parameter named “volatility” using stock price (Close price) for 20 different companies of the frontier market and then made predictions using the aforementioned machine learning ensemble models, deep learning models and our proposed stacking ensemble of the neural network model. The statistical evaluation metrics RMSE and MAE have been used to evaluate the performance of the models. It has been found that our proposed stacking ensemble neural network model outperforms all other traditional machine learning and deep learning models which have been used for comparison in this paper. The lowest RMSE and MAE values we have received using our proposed model are 0.3626 and 0.3682 percent, respectively, and the highest RMSE and MAE values are 2.5696 and 2.444 percent, respectively. The traditional ensemble learning models give the highest RMSE and MAE error rate of 20.4852 and 20.4260 percent, while the deep learning models give 15.2332 and 15.1668 percent, respectively, which clearly states that our proposed model provides a very low error value compared with the traditional models.

Controlling the spontaneous firing behavior of a neuron with astrocyte.

Mounting evidence in recent years suggests that astrocytes, a sub-type of glial cells, not only serve metabolic and structural support for neurons and synapses but also play critical roles in the regulation of proper functioning of the nervous system. In this work, we investigate the effect of astrocytes on the spontaneous firing activity of a neuron through a combined model that includes a neuron-astrocyte pair. First, we show that an astrocyte may provide a kind of multistability in neuron dynamics by inducing different firing modes such as random and bursty spiking. Then, we identify the underlying mechanism of this behavior and search for the astrocytic factors that may have regulatory roles in different firing regimes. More specifically, we explore how an astrocyte can participate in the occurrence and control of spontaneous irregular spiking activity of a neuron in random spiking mode. Additionally, we systematically investigate the bursty firing regime dynamics of the neuron under the variation of biophysical facts related to the intracellular environment of the astrocyte. It is found that an astrocyte coupled to a neuron can provide a control mechanism for both spontaneous firing irregularity and burst firing statistics, i.e., burst regularity and size.

Bipolar Random Spike and Bipolar Random Number Generation by Two Magnetic Tunnel Junctions

In several recent studies, the probabilistic nature of magnetic tunnel junction (MTJ) switching has been utilized rather than suppressed for proposals and demonstrations of specific or novel scenarios or applications, such as true random number generation, neural spike generation, stochastic computing (SC), and probabilistic spin logic. Among all schemes of operations for generating tunable random signals, dual-biasing is very simple but also robust against device variations. When an MTJ is connected to a voltage source with polarity encouraging parallel (P)-to-antiparallel (AP) switching by spin-transfer torque (STT), and under a static bias field that favors P-state, the MTJ can switch back and forth between two states randomly, due to the state-dependent modulation of current by the tunneling magnetoresistance (TMR) of the MTJ. In this work, we demonstrate bipolar random signal generation by connecting two MTJs in series with a voltage source.