Adaptive Spike Research Articles

Accurate and Efficient Event-Based Semantic Segmentation Using Adaptive Spiking Encoder-Decoder Network.

Spiking neural networks (SNNs), known for their low-power, event-driven computation, and intrinsic temporal dynamics, are emerging as promising solutions for processing dynamic, asynchronous signals from event-based sensors. Despite their potential, SNNs face challenges in training and architectural design, resulting in limited performance in challenging event-based dense prediction tasks compared with artificial neural networks (ANNs). In this work, we develop an efficient spiking encoder-decoder network (SpikingEDN) for large-scale event-based semantic segmentation (EbSS) tasks. To enhance the learning efficiency from dynamic event streams, we harness the adaptive threshold which improves network accuracy, sparsity, and robustness in streaming inference. Moreover, we develop a dual-path spiking spatially adaptive modulation (SSAM) module, which is specifically tailored to enhance the representation of sparse events and multimodal inputs, thereby considerably improving network performance. Our SpikingEDN attains a mean intersection over union (MIoU) of 72.57% on the DDD17 dataset and 58.32% on the larger DSEC-Semantic dataset, showing competitive results to the state-of-the-art ANNs while requiring substantially fewer computational resources. Our results shed light on the untapped potential of SNNs in event-based vision applications. The source codes are publicly available at https://github.com/EMI-Group/spikingedn.

Adaptive spiking, itinerancy, and quantum effects in artificial neuron circuit hardware with niobium–hafnium oxide-niobium memristor devices inserted

To improve artificial intelligence/autonomous systems and help with treating neurological conditions, there is a requirement for the discovery and design of artificial neuron hardware that mimics the advanced functionality and operation of the neural networks available in biological organisms. We examine experimental artificial neuron circuits that we designed and built in hardware with memristor devices using 4.2 nm of hafnium oxide and niobium metal inserted in the positive and negative feedback of an oscillator. At room temperature, these artificial neurons have adaptive a spiking behavior and hybrid non-chaotic/chaotic modes. When networked, they output with strong itinerancy, and we demonstrate a four-neuron learning network and modulation of signals. The superconducting state at 8.1 K results in Josephson tunneling with signs that the hafnium oxide ionic states are influenced by quantum control effects in accordance with quantum master equation calculations of the expectation values and correlation functions with a calibrated time-dependent Hamiltonian. These results are of importance to continue advancing neuromorphic hardware technologies that integrate memristors and other memory devices for many biological-inspired applications and beyond that can function with adaptive-itinerant spiking and quantum effects in their principles of operation.

Cell type-specific adaptation of the SARS-CoV-2 spike.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) can infect various human tissues and cell types, principally via interaction with its cognate receptor angiotensin-converting enzyme-2 (ACE2). However, how the virus evolves in different cellular environments is poorly understood. Here, we used experimental evolution to study the adaptation of the SARS-CoV-2 spike to four human cell lines expressing different levels of key entry factors. After twenty passages of a spike-expressing recombinant vesicular stomatitis virus (VSV), cell-type-specific phenotypic changes were observed and sequencing allowed the identification of sixteen adaptive spike mutations. We used VSV pseudotyping to measure the entry efficiency, ACE2 affinity, spike processing, TMPRSS2 usage, and entry pathway usage of all the mutants, alone or in combination. The fusogenicity of the mutant spikes was assessed with a cell-cell fusion assay. Finally, mutant recombinant VSVs were used to measure the fitness advantage associated with selected mutations. We found that the effects of these mutations varied across cell types, both in terms of viral entry and replicative fitness. Interestingly, two spike mutations (L48S and A372T) that emerged in cells expressing low ACE2 levels increased receptor affinity, syncytia induction, and entry efficiency under low-ACE2 conditions. Our results demonstrate specific adaptation of the SARS-CoV-2 spike to different cell types and have implications for understanding SARS-CoV-2 tissue tropism and evolution.

A Highly-Scalable Poisson-Coded Retinal Optogenetic Stimulator with Fully-Analog ED-Based Adaptive Spike Detection and Closed-Loop Calibration

A Highly-Scalable Poisson-Coded Retinal Optogenetic Stimulator with Fully-Analog ED-Based Adaptive Spike Detection and Closed-Loop Calibration

Bioinspired spiking spatiotemporal attention framework for lithium-ion batteries state-of-health estimation

State-of-health (SOH) estimation of batteries is crucial for ensuring the safety of energy storage systems. Prediction models based on external information (current, voltage, etc.) and artificial neural networks (ANN) are effective solutions. However, external information easily interferes, and the ANN-based model has data dependence, high energy consumption, and insufficient cognitive ability. This motivates us to utilize precise battery physical and chemical degradation information and brain-inspired spiking neural networks (SNNs) for accurate SOH estimation. Therefore, this study proposes a bioinspired spiking spatiotemporal attention neural network (SSA-Net) framework for battery health state monitoring by utilizing full-life-cycle electrochemical impedance spectroscopy (EIS). SSA-Net perfectly models brain neurons' information transmission mechanism and neuron dynamics, thereby endowing it with efficient spatiotemporal feature processing capabilities and low power consumption. Based on the designed spiking residual architecture, SSA-Net constructs a deep spiking information encoding framework achieving high gradient transfer efficiency. More importantly, this study proposes a novel SNN-based spiking spatiotemporal attention module, which realizes the enhancement of useful spiking features and discards worthless information through an adaptive spiking feature selection mechanism. Experimental results show that SSA-Net effectively extracts electrochemical features associated with battery degradation, facilitating precise modeling of the nonlinear relationship between EIS data and SOH and achieving competitive performance.

NeuSort: an automatic adaptive spike sorting approach with neuromorphic models

Objective. Spike sorting, a critical step in neural data processing, aims to classify spiking events from single electrode recordings based on different waveforms. This study aims to develop a novel online spike sorter, NeuSort, using neuromorphic models, with the ability to adaptively adjust to changes in neural signals, including waveform deformations and the appearance of new neurons. Approach. NeuSort leverages a neuromorphic model to emulate template-matching processes. This model incorporates plasticity learning mechanisms inspired by biological neural systems, facilitating real-time adjustments to online parameters. Results. Experimental findings demonstrate NeuSort’s ability to track neuron activities amidst waveform deformations and identify new neurons in real-time. NeuSort excels in handling non-stationary neural signals, significantly enhancing its applicability for long-term spike sorting tasks. Moreover, its implementation on neuromorphic chips guarantees ultra-low energy consumption during computation. Significance. NeuSort caters to the demand for real-time spike sorting in brain-machine interfaces through a neuromorphic approach. Its unsupervised, automated spike sorting process makes it a plug-and-play solution for online spike sorting.

Adaptive spike threshold dynamics associated with sparse spiking of hilar mossy cells are captured by a simple model.

Hilar mossy cells (hMCs) in the dentate gyrus (DG) receive inputs from DG granule cells (GCs), CA3 pyramidal cells and inhibitory interneurons, and provide feedback input to GCs. Behavioural and in vivo recording experiments implicate hMCs in pattern separation, navigation and spatial learning. Our experiments link hMC intrinsic excitability to their synaptically evoked in vivo spiking outputs. We performed electrophysiological recordings from DG neurons and found that hMCs displayed an adaptative spike threshold that increased both in proportion to the intensity of injected currents, and in response to spiking itself, returning to baseline over a long time scale, thereby instantaneously limiting their firing rate responses. The hMC activity is additionally limited by a prominent medium after-hyperpolarizing potential (AHP) generated by small conductance K+ channels. We hypothesize that these intrinsic hMC properties are responsible for their low in vivo firing rates. Our findings extend previous studies that compare hMCs, CA3 pyramidal cells and hilar inhibitory cells and provide novel quantitative data that contrast the intrinsic properties of these cell types. We developed a phenomenological exponential integrate-and-fire model that closely reproduces the hMC adaptive threshold nonlinearities with respect to their threshold dependence on input current intensity, evoked spike latency and long-lasting spike-induced increase in spike threshold. Our robust and computationally efficient model is amenable to incorporation into large network models of the DG that will deepen our understanding of the neural bases of pattern separation, spatial navigation and learning. KEY POINTS: Previous studies have shown that hilar mossy cells (hMCs) are implicated in pattern separation and the formation of spatial memory, but how their intrinsic properties relate to their in vivo spiking patterns is still unknown. Here we show that the hMCs display electrophysiological properties that distinguish them from the other hilar cell types including a highly adaptive spike threshold that decays slowly. The spike-dependent increase in threshold combined with an after-hyperpolarizing potential mediated by a slow K+ conductance is hypothesized to be responsible for the low-firing rate of the hMC observed in vivo. The hMC's features are well captured by a modified stochastic exponential integrate-and-fire model that has the unique feature of a threshold intrinsically dependant on both the stimulus intensity and the spiking history. This computational model will allow future work to study how the hMCs can contribute to spatial memory formation and navigation.

HybridSNN: Combining Bio-Machine Strengths by Boosting Adaptive Spiking Neural Networks.

Spiking neural networks (SNNs), inspired by the neuronal network in the brain, provide biologically relevant and low-power consuming models for information processing. Existing studies either mimic the learning mechanism of brain neural networks as closely as possible, for example, the temporally local learning rule of spike-timing-dependent plasticity (STDP), or apply the gradient descent rule to optimize a multilayer SNN with fixed structure. However, the learning rule used in the former is local and how the real brain might do the global-scale credit assignment is still not clear, which means that those shallow SNNs are robust but deep SNNs are difficult to be trained globally and could not work so well. For the latter, the nondifferentiable problem caused by the discrete spike trains leads to inaccuracy in gradient computing and difficulties in effective deep SNNs. Hence, a hybrid solution is interesting to combine shallow SNNs with an appropriate machine learning (ML) technique not requiring the gradient computing, which is able to provide both energy-saving and high-performance advantages. In this article, we propose a HybridSNN, a deep and strong SNN composed of multiple simple SNNs, in which data-driven greedy optimization is used to build powerful classifiers, avoiding the derivative problem in gradient descent. During the training process, the output features (spikes) of selected weak classifiers are fed back to the pool for the subsequent weak SNN training and selection. This guarantees HybridSNN not only represents the linear combination of simple SNNs, as what regular AdaBoost algorithm generates, but also contains neuron connection information, thus closely resembling the neural networks of a brain. HybridSNN has the benefits of both low power consumption in weak units and overall data-driven optimizing strength. The network structure in HybridSNN is learned from training samples, which is more flexible and effective compared with existing fixed multilayer SNNs. Moreover, the topological tree of HybridSNN resembles the neural system in the brain, where pyramidal neurons receive thousands of synaptic input signals through their dendrites. Experimental results show that the proposed HybridSNN is highly competitive among the state-of-the-art SNNs.

Unsupervised Spiking Neural Network with Dynamic Learning of Inhibitory Neurons.

A spiking neural network (SNN) is a type of artificial neural network that operates based on discrete spikes to process timing information, similar to the manner in which the human brain processes real-world problems. In this paper, we propose a new spiking neural network (SNN) based on conventional, biologically plausible paradigms, such as the leaky integrate-and-fire model, spike timing-dependent plasticity, and the adaptive spiking threshold, by suggesting new biological models; that is, dynamic inhibition weight change, a synaptic wiring method, and Bayesian inference. The proposed network is designed for image recognition tasks, which are frequently used to evaluate the performance of conventional deep neural networks. To manifest the bio-realistic neural architecture, the learning is unsupervised, and the inhibition weight is dynamically changed; this, in turn, affects the synaptic wiring method based on Hebbian learning and the neuronal population. In the inference phase, Bayesian inference successfully classifies the input digits by counting the spikes from the responding neurons. The experimental results demonstrate that the proposed biological model ensures a performance improvement compared with other biologically plausible SNN models.

An unsupervised STDP-based spiking neural network inspired by biologically plausible learning rules and connections

The backpropagation algorithm has promoted the rapid development of deep learning, but it relies on a large amount of labeled data and still has a large gap with how humans learn. The human brain can quickly learn various conceptual knowledge in a self-organized and unsupervised manner, accomplished through coordinating various learning rules and structures in the human brain. Spike-timing-dependent plasticity (STDP) is a general learning rule in the brain, but spiking neural networks (SNNs) trained with STDP alone is inefficient and perform poorly. In this paper, taking inspiration from short-term synaptic plasticity, we design an adaptive synaptic filter and introduce the adaptive spiking threshold as the neuron plasticity to enrich the representation ability of SNNs. We also introduce an adaptive lateral inhibitory connection to adjust the spikes balance dynamically to help the network learn richer features. To speed up and stabilize the training of unsupervised spiking neural networks, we design a samples temporal batch STDP (STB-STDP), which updates weights based on multiple samples and moments. By integrating the above three adaptive mechanisms and STB-STDP, our model greatly accelerates the training of unsupervised spiking neural networks and improves the performance of unsupervised SNNs on complex tasks. Our model achieves the current state-of-the-art performance of unsupervised STDP-based SNNs in the MNIST and FashionMNIST datasets. Further, we tested on the more complex CIFAR10 dataset, and the results fully illustrate the superiority of our algorithm. Our model is also the first work to apply unsupervised STDP-based SNNs to CIFAR10. At the same time, in the small-sample learning scenario, it will far exceed the supervised ANN using the same structure.

A Novel Spiking Graph Attention Network for Intelligent Fault Diagnosis of Planetary Gearboxes

As an essential component in the power transmission system of rotating machinery, planetary gearboxes are often subjected to extreme operating conditions, including heavy loads and low speeds, which can cause key components to fail over time. Early planetary gearbox fault features are weak, coupled with the impact of strong noise in the industrial field, which brings great challenges to the accurate identification and diagnosis of planetary gearbox faults. To address these issues, we propose a novel spiking graph attention network (Spiking-GAT) for intelligent fault diagnosis of planetary gearboxes, which realizes synchronous extraction of temporal and spatial features of the original signal, to accurately identify the fault category and severity of planetary gearbox. Firstly, a graph data construction method based on chaos theory is proposed. A multi-phase coupled chaotic oscillator array based on Duffing (MP-COAD) is established to reconstruct the original signal, and the K-nearest neighbor classifier (KNN) is used to construct the reconstructed signal as graph data. Secondly, a novel Spiking GAT intelligent fault diagnosis framework has been developed, which provides an adaptive spiking coding approach for graph data and deep mining and extraction of spatial-temporal features to accurately identify the health status of planetary gearboxes. Finally, the experimental and industrial fault simulation test rigs of the planetary gearbox are designed, and three cases are studied to verify the accuracy and stability of Spiking-GAT. The results show that the classification accuracy of Spiking-GAT reaches 100% on the datasets of the two test rigs, and has excellent noise robustness.

Integrated Sensor Electronic Front-Ends with Self-X Capabilities

The ongoing vivid advance in integration technologies is giving leverage both to computing systems as well as to sensors and sensor systems. Both conventional computing systems as well as innovative computing systems, e.g., following bio-inspiration from nervous systems or neural networks, require efficient interfacing to an increasing diversity of sensors under the constraints of metrology. The realization of sufficiently accurate, robust, and flexible analog front-ends (AFE) is decisive for the overall application system and quality and requires substantial design expertise both for cells in System-on-Chip (SoC) or chips in System-in-Package (SiP) realizations. Adding robustness and flexibility to sensory systems, e.g., for Industry 4.0., by self-X or self-* features, e.g., self-monitoring, -trimming, or -healing (AFEX) approaches the capabilities met in living beings and is pursued in our research. This paper summarizes on two chips, denoted as Universal-Sensor-Interface-with-self-X-properties (USIX) based on amplitude representation and reports on recently identified challenges and corresponding advanced solutions, e.g., on circuit assessment as well as observer robustness for classic amplitude-based AFE, and transition activities to spike domain representation spiking-analog-front-ends with self-X properties (SAFEX) based on adaptive spiking electronics as the next evolutionary step in AFE development. Key cells for AFEX and SAFEX have been designed in XFAB xh035 CMOS technology and have been subject to extrinsic optimization and/or adaptation. The submitted chip features 62,921 transistors, a total area of 10.89 mm2 (74% analog, 26% digital), and 66 bytes of the configuration memory. The prepared demonstrator will allow intrinsic optimization and/or adaptation for the developed technology agnostic concepts and chip instances. In future work, confirmed cells will be moved to complete versatile and robust AFEs, which can serve both for conventional as well as innovative computing systems, e.g., spiking neurocomputers, as well as to leading-edge technologies to serve in SOCs.

Neuromorphic adaptive spiking CPG towards bio-inspired locomotion

In recent years, locomotion mechanisms exhibited by vertebrate animals have been the inspiration for the improvement in the performance of robotic systems. These mechanisms include the adaptability of their locomotion to any change registered in the environment through their biological sensors. In this regard, we aim to replicate such kind of adaptability through a sCPG. This sCPG generates different locomotion (rhythmic) patterns which are driven by an external stimulus, that is, the output of a FSR sensor to provide feedback. The sCPG consists of a network of five populations of LIF neurons designed with a specific topology in such a way that the rhythmic patterns can be generated and driven by the aforementioned external stimulus. Therefore, eventually, the locomotion of an end robotic platform could be adapted to the terrain by using any sensor as input. The sCPG with adaptation has been numerically validated at software and hardware level, using the Brian 2 simulator and the SpiNNaker neuromorphic platform for the latest. In particular, our experiments clearly show an adaptation in the oscillation frequencies between the spikes produced in the populations of the sCPG while the input stimulus varies. To validate the robustness and adaptability of the sCPG, we have performed several tests by variating the output of the sensor. These experiments were carried out in Brian 2 and SpiNNaker; both implementations showed a similar behavior with a Pearson correlation coefficient of 0.905.

Inter-domain communication in SARS-CoV-2 spike proteins controls protease-triggered cell entry.

SARS-CoV-2 continues to evolve into variants of concern (VOC), with greatest variability in the multidomain, entry-facilitating spike proteins. To recognize the significance of adaptive spike protein changes, we compare variant SARS-CoV-2 virus particles in several assays reflecting authentic virus-cell entry. Virus particles with adaptive changes in spike amino-terminal domains (NTDs) are hypersensitive to proteolytic activation of membrane fusion, an essential step in virus-cell entry. Proteolysis is within fusion domains (FDs), at sites over 10 nm from the VOC-specific NTD changes, indicating allosteric inter-domain control of fusion activation. In addition, NTD-specific antibodies block FD cleavage, membrane fusion, and virus-cell entry, suggesting restriction of inter-domain communication as a neutralization mechanism. Finally, using structure-guided mutagenesis, we identify an inter-monomer β sheet structure that facilitates NTD-to-FD transmissions and subsequent fusion activation. This NTD-to-FD axis that sensitizes viruses to infection and to NTD-specific antibody neutralization provides new context for understanding selective forces driving SARS-CoV-2 evolution.

Mathematical simulation of memristive for classification in machine learning

Over the last few years, neuromorphic computation has been a widely researched topic. One of the neuromorphic computation elements is the memristor. The memristor is a high density, analogue memory storage, and compliance with Ohm's law for minor potential changes. Memristive behaviour imitates synaptic behaviour. It is a nanotechnology that can reduce power consumption, improve synaptic modeling, and reduce data transmission processes. The purpose of this paper is to investigate a customized mathematical model for machine learning algorithms. This model uses a computing paradigm that differs from standard Von-Neumann architectures, and it has the potential to reduce power consumption and increasing performance while doing specialized jobs when compared to regular computers. Classification is one of the most interesting fields in machine learning to classify features patterns by using a specific algorithm. In this study, a classifier based memristive is used with an adaptive spike encoder for input data. We run this algorithm based on Anti-Hebbian and Hebbian learning rules. These investigations employed two of datasets, including breast cancer Wisconsin and Gaussian mixture model datasets. The results indicate that the performance of our algorithm that has been used based on memristive is reasonably close to the optimal solution.

Co-circulation of two SARS-CoV-2 variant strains within imported pet hamsters in Hong Kong

ABSTRACT During the investigation of a pet shop outbreak of severe acute respiratory coronavirus 2 (SARS-CoV-2) with probable hamster-to-human transmission, the environmental and hamster samples in epidemiologically linked pet shops were found positive for SARS-CoV-2 Delta variant AY.127 strains which are phylogenetically closely related to patients and reported European strains. This interspecies’ spill-over has triggered transmission in 58 patients epidemiologically linked to three pet shops. Incidentally, three dwarf hamsters imported from the Netherlands and centralized in a warehouse distributing animals to pet shops were positive for SARS-CoV-2 spike variant phylogenetically related to European B.1.258 strains from March 2020. This B.1.258 strain almost disappeared in July 2021. While no hamster-to-human transmission of B.1.258-like strain was found in this outbreak, molecular docking showed that its spike receptor-binding domain (RBD) has a similar binding energy to human ACE2 compared to that of Delta variant AY.127. Therefore, the potential of this B.1.258-related spike variant for interspecies jumping cannot be ignored. The co-circulation of B.1.258-related spike variants with Delta AY.127, which originated in Europe and was not previously found in Hong Kong, suggested that hamsters in our wholesale warehouse and retail pet shops more likely have acquired these viruses in the Netherlands or stopovers during delivery by aviation than locally. The risk of human-to-hamster reverse zoonosis by multiple SARS-CoV-2 variants leading to further adaptive spike mutations with subsequent transmission back to humans cannot be underestimated as an outbreak source of COVID-19. Testing imported pet animals susceptible to SARS-CoV-2 is warranted to prevent future outbreaks.

The N501Y spike substitution enhances SARS-CoV-2 infection and transmission.

The B.1.1.7 variant (also known as Alpha) of SARS-CoV-2, the cause of the COVID-19 pandemic, emerged in the UK in the summer of 2020. The prevalence of this variant increased rapidly owing to an increase in infection and/or transmission efficiency1. The Alpha variant contains 19 nonsynonymous mutations across its viral genome, including 8 substitutions or deletions in the spike protein that interacts with cellular receptors to mediate infection and tropism. Here, using a reverse genetics approach, we show that of the 8 individual spike protein substitutions, only N501Y resulted in consistent fitness gains for replication in the upper airway in a hamster model as well as in primary human airway epithelial cells. The N501Y substitution recapitulated the enhanced viral transmission phenotype of the eight mutations in the Alpha spike protein, suggesting that it is a major determinant of the increased transmission of the Alpha variant. Mechanistically, the N501Y substitution increased the affinity of the viral spike protein for cellular receptors. As suggested by its convergent evolution in Brazil, South Africa and elsewhere2,3, our results indicate that N501Y substitution is an adaptive spike mutation of major concern.

Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks

Inspired by detailed modelling of biological neurons, spiking neural networks (SNNs) are investigated as biologically plausible and high-performance models of neural computation. The sparse and binary communication between spiking neurons potentially enables powerful and energy-efficient neural networks. The performance of SNNs, however, has remained lacking compared with artificial neural networks. Here we demonstrate how an activity-regularizing surrogate gradient combined with recurrent networks of tunable and adaptive spiking neurons yields the state of the art for SNNs on challenging benchmarks in the time domain, such as speech and gesture recognition. This also exceeds the performance of standard classical recurrent neural networks and approaches that of the best modern artificial neural networks. As these SNNs exhibit sparse spiking, we show that they are theoretically one to three orders of magnitude more computationally efficient compared to recurrent neural networks with similar performance. Together, this positions SNNs as an attractive solution for AI hardware implementations. The use of sparse signals in spiking neural networks, modelled on biological neurons, offers in principle a highly efficient approach for artificial neural networks when implemented on neuromorphic hardware, but new training approaches are needed to improve performance. Using a new type of activity-regularizing surrogate gradient for backpropagation combined with recurrent networks of tunable and adaptive spiking neurons, state-of-the-art performance for spiking neural networks is demonstrated on benchmarks in the time domain.

Adaptive Spiking Sensor System Based on CMOS Memristors Emulating Long and Short-Term Plasticity of Biological Synapses for Industry 4.0 Applications

Abstract A conventional analog to digital converter (ADC) faces many issues with leading-edge technologies due to noise, manufacturing deviations, signal swings, etc. Thus, we pursue to design an adaptive spiking neural ADC (SN-ADC) with promising features, e.g., robust to noise, low-power, technology scaling issues, and low-voltage operation. Therefore, our approach promises to be technology agnostic, i.e., effectively translatable to aggressive new technologies. It supports machine learning and self-x (self-calibration, self-healing) that needs for industry 4.0 and the internet of things (IoTs). In this work, we design an adaptive spike-to-rank coding (ASRC), which is the main part of the spiking neural ADC. The ASRC is based on CMOS memristors emulating short-term plasticity (STP) and long-term plasticity (LTP) biological synapses. The proposed ASRC compensates deviations by adapting the weights of the synapses. Also, ASRC is designed using XFAB 0.35 μm CMOS technology and Cadence design tools. In addition, ASRC is simulated to test its performance in the temperature range (−40°C to 85°C).

An automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions

Developing high-density electrodes for recording large ensembles of neurons provides a unique opportunity for understanding the mechanism of the neuronal circuits. Nevertheless, the change of brain tissue around chronically implanted neural electrodes usually causes spike wave-shape distortion and raises the crucial issue of spike sorting with an unstable structure. The automatic spike sorting algorithms have been developed to extract spikes from these big extracellular data. However, due to the spike wave-shape instability, there have been a lack of robust spike detection procedures and clustering to overcome the spike loss problem. Here, we develop an automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions to address these distortions and instabilities. The adaptive detection procedure applies to the detected spikes, consists of multi-point alignment and statistical filtering for removing mistakenly detected spikes. The detected spikes are clustered based on the mixture of skew-t distributions to deal with non-symmetrical clusters and spike loss problems. The proposed algorithm improves the performance of the spike sorting in both terms of precision and recall, over a broad range of signal-to-noise ratios. Furthermore, the proposed algorithm has been validated on different datasets and demonstrates a general solution to precise spike sorting, in vitro and in vivo.