Neural Spike Research Articles

Multimodal neural recordings with Neuro-FITM uncover diverse patterns of cortical-hippocampal interactions.

Many cognitive processes require communication between the neocortex and the hippocampus. However, coordination between large-scale cortical dynamics and hippocampal activity is not well understood, partially due to the difficulty in simultaneously recording from those regions. In the present study, we developed a flexible, insertable and transparent microelectrode array (Neuro-FITM) that enables investigation of cortical-hippocampal coordinations during hippocampal sharp-wave ripples (SWRs). Flexibility and transparency of Neuro-FITM allow simultaneous recordings of local field potentials and neural spiking from the hippocampus during wide-field calcium imaging. These experiments revealed that diverse cortical activity patterns accompanied SWRs and, in most cases, cortical activation preceded hippocampal SWRs. We demonstrated that, during SWRs, different hippocampal neural population activity was associated with distinct cortical activity patterns. These results suggest that hippocampus and large-scale cortical activity interact in a selective and diverse manner during SWRs underlying various cognitive functions. Our technology can be broadly applied to comprehensive investigations of interactions between the cortex and other subcortical structures.

Estimating Transfer Entropy in Continuous Time Between Neural Spike Trains or Other Event-Based Data.

Transfer entropy (TE) is a widely used measure of directed information flows in a number of domains including neuroscience. Many real-world time series for which we are interested in information flows come in the form of (near) instantaneous events occurring over time. Examples include the spiking of biological neurons, trades on stock markets and posts to social media, amongst myriad other systems involving events in continuous time throughout the natural and social sciences. However, there exist severe limitations to the current approach to TE estimation on such event-based data via discretising the time series into time bins: it is not consistent, has high bias, converges slowly and cannot simultaneously capture relationships that occur with very fine time precision as well as those that occur over long time intervals. Building on recent work which derived a theoretical framework for TE in continuous time, we present an estimation framework for TE on event-based data and develop a k-nearest-neighbours estimator within this framework. This estimator is provably consistent, has favourable bias properties and converges orders of magnitude more quickly than the current state-of-the-art in discrete-time estimation on synthetic examples. We demonstrate failures of the traditionally-used source-time-shift method for null surrogate generation. In order to overcome these failures, we develop a local permutation scheme for generating surrogate time series conforming to the appropriate null hypothesis in order to test for the statistical significance of the TE and, as such, test for the conditional independence between the history of one point process and the updates of another. Our approach is shown to be capable of correctly rejecting or accepting the null hypothesis of conditional independence even in the presence of strong pairwise time-directed correlations. This capacity to accurately test for conditional independence is further demonstrated on models of a spiking neural circuit inspired by the pyloric circuit of the crustacean stomatogastric ganglion, succeeding where previous related estimators have failed.

Prefrontal Inhibition Drives Formation and Dynamic Expression of Probabilistic Pavlovian Fear Conditionin

Under natural conditions, the association between cause and effect is usually not absolute but rather probabilistic, indicating the ambiguous nature of the cues. Responses or memory retrieval triggered by ambiguous cues are altered or biased in a more negative manner in certain psychiatric diseases, such as anxiety and depression. A better understanding of the formation and modulation of this probabilistic association is important for revealing the nature of aversive memory and its alterations in brain diseases. By using probabilistic Pavlovian fear conditioning as a model, we find that conditioning with 50% conditioned and unconditioned stimuli (CS-US) association results in reduced fear responses and neural spiking in dorsomedial prefrontal cortex (dmPFC) due to enhanced inhibition from dmPFC parvalbumin (PV) neurons. This PV-neuronal plasticity is associated with increased synaptic inputs to PV-neurons and requires activation of ventral hippocampus (vHPC). Ventral HPC is required for probabilistic memory formation by detecting CS-US mismatching during conditioning but not for its expression. Stress prior to conditioning impairs the formation of probabilistic memory by abolishing PV-neuronal plasticity, while stress prior to memory retrieval reverts established PV-neuron modulation. Therefore, PV-neurons act as a hub for integration and tailor learned responses to fit brain states at the moment of retrieval.

Outan: An On-Head System for Driving µLED Arrays Implanted in Freely Moving Mice

In the intact brain, neural activity can be recorded using sensing electrodes and manipulated using light stimulation. Silicon probes with integrated electrodes and µLEDs enable the detection and control of neural activity using a single implanted device. Miniaturized solutions for recordings from small freely moving animals are commercially available, but stimulation is driven by large, stationary current sources. We designed and fabricated a current source chip and integrated it into a headstage PCB that weighs 1.37 g. The proposed system provides 10-bit resolution current control for 32 channels, driving µLEDs with up to 4.6 V and sourcing up to 0.9 mA at a refresh rate of 5 kHz per channel. When calibrated against a µLED probe, the system allows linear control of light output power, up to 10 µW per µLED. To demonstrate the capabilities of the system, synthetic sequences of neural spiking activity were produced by driving multiple µLEDs implanted in the hippocampal CA1 area of a freely moving mouse. The high spatial, temporal, and amplitude resolution of the system provides a rich variety of stimulation patterns. Combined with commercially available sampling headstages, the system provides an easy to use back-end, fully utilizing the bi-directional potential of integrated opto-electronic arrays.

Influence of Choices of Statistical Models on Neural Spike Trend

The Center for Neural Interface Design of the Biodesign Insti- tute at Arizona State University conducted an experiment to investigate how the central nervous system controls hand orientation and movement di- rection during reach-to-grasp movements. ANOVA (Analysis of Variance), a conventional data analysis widely used in neural science, was performed to categorized dierent neural activities. Some preliminary studies on data analysis methods have shown that the principal assumption of ANOVA is violated and some characteristics of data are missing from taking the ra- tio of recorded data. To compensate the deciency of ANOVA, ANCOVA (Analysis of covariance) is introduced in this paper. By considering neural ring counts and temporal intervals respectively, we expect to extract more useful information for determining the correlations among dierent types of neurons with motor behavior. Comparing to ANOVA, ANCOVA can be one step further to identify which direction or orientation is favored during which epoch. We nd that a considerable number of neurons are involved in movement direction, hand orientation, or both combined, and some are signicant in more than one epoch, which indicates there exists a network with unknown pathways connecting neurons in motor cortex throughout the entire movement. For the future studies we suggest to integrate this study into neural networking in order to simulate the whole reach-to-grasp process.

Experimental study of neuromorphic node based on a multiwaveband emitting two-section quantum dot laser

In this work, we present experimental results concerning excitability in a multiband emitting quantum-dot-based photonic neuron. The experimental investigation revealed that the same two-section quantum dot laser can be tuned through a simple bias adjustment to operate either as a leaky integrate and fire or as a resonate and fire neuron. Furthermore, by exploiting the inherent multiband emission of quantum-dot devices revealed by the existence of multiple lasing thresholds, a significant enhancement in the neurocomputational capabilities, such as spiking duration and firing rate, is observed. Spike firing rate increased by an order of magnitude that leads to an enhancement in processing speed and, more importantly, neural spike duration was suppressed to the picosecond scale, which corresponds to a significant temporal resolution enhancement. These new regimes of operation, when combined with thermal insensitivity, silicon cointegration capability, and the fact that these multiband mechanisms are also present in miniaturized quantum-dot devices, render these neuromorphic nodes a proliferating platform for large-scale photonic spiking neural networks.

ANALYZING SECOND ORDER STOCHASTICITY OF NEURAL SPIKING UNDER STIMULI-BUNDLE EXPOSURE.

Conventional analysis of neuroscience data involves computing average neural activity over a group of trials and/or a period of time. This approach may be particularly problematic when assessing the response patterns of neurons to more than one simultaneously presented stimulus. in such cases the brain must represent each individual component of the stimuli bundle, but trial-and-time-pooled averaging methods are fundamentally unequipped to address the means by which multiitem representation occurs. We introduce and investigate a novel statistical analysis framework that relates the firing pattern of a single cell, exposed to a stimuli bundle, to the ensemble of its firing patterns under each constituent stimulus. Existing statistical tools focus on what may be called "first order stochasticity" in trial-to-trial variation in the form of unstructured noise around a fixed firing rate curve associated with a given stimulus. our analysis is based upon the theoretical premise that exposure to a stimuli bundle induces additional stochasticity in the cell's response pattern in the form of a stochastically varying recombination of its single stimulus firing rate curves. We discuss challenges to statistical estimation of such "second order stochasticity" and address them with a novel dynamic admixture point process (DAPP) model. DAPP is a hierarchical point process model that decomposes second order stochasticity into a Gaussian stochastic process and a random vector of interpretable features and facilitates borrowing of information on the latter across repeated trials through latent clustering. We illustrate the utility and accuracy of the DAPP analysis with synthetic data simulation studies. We present real-world evidence of second order stochastic variation with an analysis of monkey inferior colliculus recordings under auditory stimuli.

Neuromorphic Analog Implementation of Neural Engineering Framework-Inspired Spiking Neuron for High-Dimensional Representation.

Brain-inspired hardware designs realize neural principles in electronics to provide high-performing, energy-efficient frameworks for artificial intelligence. The Neural Engineering Framework (NEF) brings forth a theoretical framework for representing high-dimensional mathematical constructs with spiking neurons to implement functional large-scale neural networks. Here, we present OZ, a programable analog implementation of NEF-inspired spiking neurons. OZ neurons can be dynamically programmed to feature varying high-dimensional response curves with positive and negative encoders for a neuromorphic distributed representation of normalized input data. Our hardware design demonstrates full correspondence with NEF across firing rates, encoding vectors, and intercepts. OZ neurons can be independently configured in real-time to allow efficient spanning of a representation space, thus using fewer neurons and therefore less power for neuromorphic data representation.

Efficient neural spike sorting using data subdivision and unification.

Neural spike sorting is prerequisite to deciphering useful information from electrophysiological data recorded from the brain, in vitro and/or in vivo. Significant advancements in nanotechnology and nanofabrication has enabled neuroscientists and engineers to capture the electrophysiological activities of the brain at very high resolution, data rate and fidelity. However, the evolution in spike sorting algorithms to deal with the aforementioned technological advancement and capability to quantify higher density data sets is somewhat limited. Both supervised and unsupervised clustering algorithms do perform well when the data to quantify is small, however, their efficiency degrades with the increase in the data size in terms of processing time and quality of spike clusters being formed. This makes neural spike sorting an inefficient process to deal with large and dense electrophysiological data recorded from brain. The presented work aims to address this challenge by providing a novel data pre-processing framework, which can enhance the efficiency of the conventional spike sorting algorithms significantly. The proposed framework is validated by applying on ten widely used algorithms and six large feature sets. Feature sets are calculated by employing PCA and Haar wavelet features on three widely adopted large electrophysiological datasets for consistency during the clustering process. A MATLAB software of the proposed mechanism is also developed and provided to assist the researchers, active in this domain.

Comparison of Sneo-Based Neural Spike Detection Algorithms for Implantable Multi-Transistor Array Biosensors

Real-time neural spike detection is an important step in understanding neurological activities and developing brain-silicon interfaces. Recent approaches exploit minimally invasive sensing techniques based on implanted complementary metal-oxide semiconductors (CMOS) multi transistors arrays (MTAs) that limit the damage of the neural tissue and provide high spatial resolution. Unfortunately, MTAs result in low signal-to-noise ratios due to the weak capacitive coupling between the nearby neurons and the sensor and the high noise power coming from the analog front-end. In this paper we investigate the performance achievable by using spike detection algorithms for MTAs, based on some variants of the smoothed non-linear energy operator (SNEO). We show that detection performance benefits from the correlation of the signals detected by the MTA pixels, but degrades when a high firing rate of neurons occurs. We present and compare different approaches and noise estimation techniques for the SNEO, aimed at increasing the detection accuracy at low SNR and making it less dependent on neurons firing rates. The algorithms are tested by using synthetic neural signals obtained with a modified version of NEUROCUBE generator. The proposed approaches outperform the SNEO, showing a more than 20% increase on averaged sensitivity at 0 dB and reduced dependence on the neuronal firing rate.

Blood Pressure Change in Intrafascicular Vagal Activities

Baroreflex plays a significant role in modulating blood pressure for the human body. It is known that activation of the vagal nerve related to baroreflex can lead to reductions of blood pressure. However, how the vagal activities quantitatively relate with blood pressure can hardly be achieved. Here fine carbon nanotube yarn (CNTy) electrodes were adopted for recording intrafascicular vagal activities, synchronized with measurement of arterial blood pressure in a rat. Together with a novel algorithm, the results preliminarily quantified that there were six clusters of neural spikes within recorded vagal activities, and the number of individual vagal spikes correspondingly varied with blood pressure. Especially for Cluster 2, the neural activations decreased with arterial blood pressure increasing. This study can shed lights on the quantified neural mechanism underlying the control of vagal activities on blood pressure, and guide the vagal-nerve neuromodulation for treating hypertension.

Oxide semiconductor-based ferroelectric thin-film transistors for advanced neuromorphic computing

Neuromorphic computing that mimics the biological brain has been demonstrated as a next-generation computing method due to its low power consumption and parallel data processing characteristics. To realize neuromorphic computing, diverse neural networks such as deep neural networks (DNNs) and spiking neural networks (SNNs) have been introduced. DNNs require artificial synapses that have analog conductance modulation characteristics, whereas SNNs require artificial synapses that have conductance modulation characteristics controlled by temporal relationships between signals, so the development of a multifunctional artificial synapse is required. In this work, we report a ferroelectric thin-film transistor (FeTFT) that uses zirconium-doped hafnia (HfZrOx) and indium zinc tin oxide (IZTO) for neuromorphic applications. With reliable conductance modulation characteristics, we suggest that the FeTFT with HfZrOx and IZTO can be used as an artificial synapse for both DNNs and SNNs. The linear and symmetric conductance modulation characteristics in FeTFTs result in high recognition accuracy (93.1%) of hand-written images, which is close to the accuracy (94.1%) of an ideal neural network. Also, we show that the FeTFTs can emulate diverse forms of spike-time-dependent plasticity, which is an important learning rule for SNNs. These results suggest that FeTFT is a promising candidate to realize neuromorphic computing hardware.

An Efficient and Flexible Spike Train Model Via Empirical Bayes

Accurate statistical models of neural spike responses can characterize the information carried by neural populations. But the limited samples of spike counts during recording usually result in model overfitting. Besides, current models assume spike counts to be Poisson-distributed, which ignores the fact that many neurons demonstrate over-dispersed spiking behaviour. Although the Negative Binomial Generalized Linear Model (NB-GLM) provides a powerful tool for modeling over-dispersed spike counts, the maximum likelihood-based standard NB-GLM leads to highly variable and inaccurate parameter estimates. Thus, we propose a hierarchical parametric empirical Bayes method to estimate the neural spike responses among neuronal population. Our method integrates both Generalized Linear Models (GLMs) and empirical Bayes theory, which aims to (1) improve the accuracy and reliability of parameter estimation, compared to the maximum likelihood-based method for NB-GLM and Poisson-GLM; (2) effectively capture the over-dispersion nature of spike counts from both simulated data and experimental data; and (3) provide insight into both neural interactions and spiking behaviours of the neuronal populations. We apply our approach to study both simulated data and experimental neural data. The estimation of simulation data indicates that the new framework can accurately predict mean spike counts simulated from different models and recover the connectivity weights among neural populations. The estimation based on retinal neurons demonstrate the proposed method outperforms both NB-GLM and Poisson-GLM in terms of the predictive log-likelihood of held-out data. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>

A Performance Comparison of Artificial Neural Networks and Spiking Neural Networks

The continual development of artificial neural networks (ANNs) on von-Neuman based hardware poses a threat to potential advances in artificial intelligence (AI). At current rates, power usage of such models will rival that of large cities or even supersede the amount of power produced globally. Therefore, it is imperative to make a transition to a more power efficient computing architecture to make way for future advances in AI. The development of neuromorphic hardware has paved a path for a new set of neural networks called spiking neural networks (SNNs) to act as a viable alternative for machine learning tasks on such brain-like hardware. The primary motive of the research is to contribute towards a search for viable applications of SNNs in their current infancy. The project wishes to address whether SNNs can outperform ANNs image classification of handwritten digits on the MNIST dataset. Despite training on non-optimal von Neumann hardware, statistically significant evidence suggests that the SNN outperformed the ANN in terms of the mean accuracy of the task. As a result of the findings, the paper suggests that further research should focus on translating older ANN models to SNN counterparts that outperform on their tasks and finding applications of SNNs on von Neumann hardware that outperform ANNs. Both prior focuses will narrow the gap between ANN and SNN performance, and in doing so makes a transition to more power efficient and uninhibited AI advancement a reality.

Neural Spike Sorting Using Binarized Neural Networks

This article presents the design and efficient hardware implementation of binarized neural networks (BNNs) for brain-implantable neural spike sorting. In contrast to the conventional artificial neural networks (ANNs), in which the weights and activation functions of neurons are represented using real values, the BNNs utilize binarized weights and activation functions to dramatically reduce the memory requirement and computational complexity of the ANNs. The designed BNN is trained using several realistic neural datasets to verify its accuracy for neural spike sorting. The application-specific integrated circuit (ASIC) implementation of the designed BNN in a standard 0.18- [Formula: see text] CMOS process occupies 0.33 mm 2 of silicon area. Power consumption estimation of the ASIC layout shows that the BNN dissipates [Formula: see text] of power from a 1.8 V supply while operating at 24 kHz. The designed BNN-based spike sorting system is also implemented on a field-programmable gate array and is shown to reduce the required on-chip memory by 89% compared to those of the alternative state-of-the-art spike sorting systems. To the best of our knowledge, this is the first work employing BNNs for real-time in vivo neural spike sorting.

Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data.

The Hawkes process and its extensions effectively model self-excitatory phenomena including earthquakes, viral pandemics, financial transactions, neural spike trains and the spread of memes through social networks. The usefulness of these stochastic process models within a host of economic sectors and scientific disciplines is undercut by the processes' computational burden: complexity of likelihood evaluations grows quadratically in the number of observations for both the temporal and spatiotemporal Hawkes processes. We show that, with care, one may parallelize these calculations using both central and graphics processing unit implementations to achieve over 100-fold speedups over single-core processing. Using a simple adaptive Metropolis-Hastings scheme, we apply our high-performance computing framework to a Bayesian analysis of big gunshot data generated in Washington D.C. between the years of 2006 and 2019, thereby extending a past analysis of the same data from under 10,000 to over 85,000 observations. To encourage widespread use, we provide hpHawkes, an open-source R package, and discuss high-level implementation and program design for leveraging aspects of computational hardware that become necessary in a big data setting.

Dirichlet depths for point process

Statistical depths have been well studied for multivariate and functional data over the past few decades, but remain under-explored for point processes. A first attempt on the notion of point process depth was conducted recently where the depth was defined as a weighted product of two terms: (1) the probability of the number of events in each process and (2) the depth of the event times conditioned on the number of events by using a Mahalanobis depth. We point out that multivariate depths such as the Mahalanobis depth cannot be directly used because they often neglect the important ordering property in the point process events. To deal with this problem, we propose a model-based approach for point process systematically. In particular, we develop a Dirichlet-distribution-based framework on the conditional depth term, where the new methods are referred to as Dirichlet depths. We examine mathematical properties of the new depths and conduct asymptotic analysis. In addition, we illustrate the new methods using various simulated and real experiment data. It is found that the proposed framework provides a reasonable center-outward rank and the new methods have accurate decoding in one neural spike train dataset.

Synchronous beta oscillation of epileptiform activities detected by microelectrode arrays in the awake and anesthetized mice

Isoflurane is a reversible anesthetic which is widely used in the surgery and animal experiments. It was still not clear whether isoflurane would affect the pattern of epileptiform signals even without seizure. In this work, an implantable and nanocomposites coated microelectrode array (MEA) was fabricated to study the neural spike firing and local field potential (LFP) in the hippocampus of awake and isoflurane anesthetized epileptic mice. The MEA recorded the neural activities during anesthesia, wakefulness and seizures at same location in the hippocampus. The results demonstrated that LFP power and neural spike firing rate were higher under anesthesia than under wakefulness. A peak appeared in the beta frequency band of epileptic LFP power distribution. There were remarkable differences in neural activities during seizures, with peak spike firing rate, amplitude and LFP power values being the largest. The epileptic animal without seizure exhibit normal behavior, but shows higher power than normal ones. The neural spike and LFP exhibited synchronous change with a similar power spectral distribution showing an epileptic peak at 11.6 Hz and higher power in the beta frequency band of 10–25 Hz. The MEA technology used in the mice brain provides an effective platform to reveal the real mechanism underlying epilepsy.

Power System Disturbance Classification With Online Event-Driven Neuromorphic Computing

Accurate online classification of disturbance events in a transmission network is an important part of wide-area monitoring. Although many conventional machine learning techniques are very successful in classifying events, they rely on extracting information from PMU data at control centers and processing them through CPU/GPUs, which are highly inefficient in terms of energy consumption. To solve this challenge without compromising accuracy, this article presents a novel methodology based on event-driven neuromorphic computing architecture for classification of power system disturbances. A Spiking Neural Network (SNN)-based computing framework is proposed, which exploits sparsity in disturbances and promotes local event-driven operation for unsupervised learning and inference from incoming data. Spatio-temporal information of PMU signals is first extracted and encoded into spike trains and classification is achieved with SNN-based supervised and unsupervised learning framework. In addition, benefits of deep spiking networks for complex multi-class event identification problem are presented by leveraging increasing dynamic neural sparse spiking events with network depth. Moreover, a QR decomposition-based selection technique is proposed to identify signals participating in the low rank subspace of multiple disturbance events. Performance of the proposed method is validated on data collected from a 16-machine, 5-area New England-New York system.

Neural Spike Sorting And Classification

Spike sorting is the process of separating the extracellular recording of the brain signal into one unit activity. There are a number of proposed algorithms for this purpose, but there is still no acceptable solution. In this paper a spike sorting method has been proposed based on the Euclidean distance of the most effective features of spikes represented by principle components (PCs) of the detected and aligned spikes. The assessments of the method, based on signal-to-noise ratio (SNR) representing background noise, showed that the method performed spike sorting to a high level of accuracy.