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In the realm of marine intelligence, effective ship classification across vast oceanic expanses is pivotal. Despite strides in conventional identification techniques, existing methods exhibit limitations in terms of effectiveness, robustness, and overall performance. This paper introduces a novel Elephant Herd optimization based Spiking Neural Networks (EHO-SNN) for discerning large ships, small vessels, and the absence of ships. Initial satellite images from the Airbus dataset capture ships at sea, subjected to preprocessing via the wavelet transform-based Retinex algorithm (WRA) to eliminate noise and fog artifacts. Deep learning mobile net facilitates feature extraction, while the Elephant Herd algorithm culls irrelevant features, honing in on the most pertinent ones. Finally, the classification through a spiking neural network, distinguishing between large ships, small vessels, and the absence of ships. Detected large and small ships are accurately positioned within a selected scene, while the absence of a ship terminates the process. The Proposed EHO-SNN model attains an impressive classification accuracy of 99.10%. Notably, it surpasses OMRCNN-SHD, Efficient Net, and AN-YOLOv4 by 1.15%, 12.30%, and 7.79%, respectively, thereby advancing overall accuracy in ship classification. [ABSTRACT FROM AUTHOR]

Guided by brain‐like temporal processing and event‐driven manner, neuromorphic computing has emerged as a competitive paradigm to realize artificial intelligence with high energy efficiency. Silicon photonics offers an ideal hardware platform with mutual foundry fabrication process and well‐developed device libraries, however, its huge potential to build integrated neuromorphic systems is significantly hindered due to the lack of scalable on‐chip photonic spiking neurons. Here, the first integrated electrically‐driven spiking neuron based on a silicon microring under the carrier injection working mode is reported, which is capable of emulating fundamental neural dynamics including excitability threshold, temporal integration, refractory period, controllable spike inhibition, and precise time encoding at a speed of 250 MHz. By programming time‐multiplexed spike representations, photonic spiking convolution is experimentally realized for image edge feature detection. Besides, a spiking convolutional neural network is constructed by combining photonic convolutional layers with a software‐implemented fully‐connected layer, which yields a classification accuracy of 94.1% on the benchmark Modified National Institute of Standards and Technology database. Moreover, it is theoretically verified that it's promising to further improve the operation speed to a gigahertz level by developing an electro‐optical co‐simulation model. The proposed microring neuron constitutes the final building block of scalable spike activation, thus representing a great breakthrough to boost the development of on‐chip neuromorphic information processing. [ABSTRACT FROM AUTHOR]

Fatigue driving is one of the leading causes of traffic accidents, and the rapid and accurate detection of driver fatigue is of paramount importance for enhancing road safety. However, the application of deep learning models in fatigue driving detection has long been constrained by high computational costs and power consumption. To address this issue, this study proposes an approach that combines Self-Organizing Map (SOM) and Spiking Neural Networks (SNN) to develop a low-power model capable of accurately recognizing the driver's mental state. Initially, spatial features are extracted from electroencephalogram (EEG) signals using the SOM network. Subsequently, the extracted weight vectors are encoded and fed into the SNN for fatigue driving classification. The research results demonstrate that the proposed method effectively considers the spatiotemporal characteristics of EEG signals, achieving efficient fatigue detection. Simultaneously, this approach successfully reduces the model's power consumption. When compared to traditional artificial neural networks, our method reduces energy consumption by approximately 12.21–42.59%. [ABSTRACT FROM AUTHOR]

To expand the scale of spiking neural networks (SNNs), an interface circuit that supports multiple SNN cores is essential. This circuit should be designed using an asynchronous approach to leverage characteristics of SNNs similar to those of the human brain. However, the absence of a global clock presents timing issues during implementation. Hence, we propose an intermediate latching template to establish asynchronous nonlinear connectivity with multipipeline processing between multiple SNN cores. We design arbitration and distribution blocks in the interface circuit based on the proposed template and fabricate an interface circuit that supports four SNN cores using a full‐custom approach in a 28‐nm CMOS (complementary metal–oxide–semiconductor) FDSOI (fully depleted silicon on insulator) process. The proposed template can enhance throughput in the interface circuit by up to 53% compared with the conventional asynchronous template. The interface circuit transmits spikes while consuming 1.7 and 3.7 pJ of power, supporting 606 and 59 Mevent/s in intrachip and interchip communications, respectively. [ABSTRACT FROM AUTHOR]

This paper presents a membrane computation error‐minimized mixed‐mode spiking neural network (SNN) crossbar array. Our approach involves implementing an embedded dummy switch scheme and a mid‐node pre‐charge scheme to construct a high‐precision current‐mode synapse. We effectively suppressed charge sharing between membrane capacitors and the parasitic capacitance of synapses that results in membrane computation error. A 400 × 20 SNN crossbar prototype chip is fabricated via a 28‐nm FDSOI CMOS process, and 20 MNIST patterns with their sizes reduced to 20 × 20 pixels are successfully recognized under 411 μW of power consumed. Moreover, the peak‐to‐peak deviation of the normalized output spike count measured from the 21 fabricated SNN prototype chips is within 16.5% from the ideal value, including sample‐wise random variations. [ABSTRACT FROM AUTHOR]

Situational awareness detection and characterization of mental states have a vital role in medicine and many other fields. An electroencephalogram (EEG) is one of the most effective tools for identifying and analyzing cognitive stress. Yet, the measurement, interpretation, and classification of EEG sensors is a challenging task. This study introduces a novel machine learning-based approach to assist in evaluating situational awareness detection using EEG signals and spiking neural networks (SNNs) based on a unique spike continuous-time neuron (SCTN). The implemented biologically inspired SNN architecture is used for effective EEG feature extraction by applying time–frequency analysis techniques and allows adept detection and analysis of the various frequency components embedded in the different EEG sub-bands. The EEG signal undergoes encoding into spikes and is then fed into an SNN model which is well suited to the serial sequence order of the EEG data. We utilize the SCTN-based resonator for EEG feature extraction in the frequency domain which demonstrates high correlation with the classical FFT features. A new SCTN-based 2D neural network is introduced for efficient EEG feature mapping, aiming to achieve a spatial representation of each EEG sub-band. To validate and evaluate the performance of the proposed approach, a common, publicly available EEG dataset is used. The experimental results show that by using the extracted EEG frequencies features and the SCTN-based SNN classifier, the mental state can be accurately classified with an average accuracy of 96.8% for the common EEG dataset. Our proposed method outperforms existing machine learning-based methods and demonstrates the advantages of using SNNs for situational awareness detection and mental state classifications. [ABSTRACT FROM AUTHOR]

This paper presents an interpretable, spiking neural classifier (IpT-SNC) with time-varying weights. IpT-SNC uses a two-layered spiking neural network (SNN) architecture in which weights of synapses are modeled using amplitude-modulated, time-varying Gaussian functions. Self-regulated particle swarm optimization (SRPSO) is used to update the amplitude, width, and centers of the Gaussian functions and thresholds of neurons in the output layer. IpT-SNC has been developed to improve the interpretability of spiking neural networks. The time-varying weights in IpT-SNC allow us to describe the rationale behind predictions in terms of specific input spikes. The performance of IpT-SNC is evaluated on ten benchmark datasets in the UCI machine learning repository and compared with the performance of other learning algorithms. According to the performance results, IpT-SNC enhances classification performance on testing datasets from a minimum of 0.5% to a maximum of 7.7%. The significance level of IpT-SNC with other learning algorithms is evaluated using statistical tests like the Friedman test and the paired t-test. Furthermore, on the challenging real-world BCI (Brain Computer Interface) competition IV dataset, IpT-SNC outperforms current classifiers by about 8% in terms of classification accuracy. The results indicate that IpT-SNC has better generalization performance than other algorithms. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) are generating wide attention due to their brain-like simulation capabilities and low energy consumption. Converting artificial neural networks (ANNs) to SNNs provides great advantages, combining the high accuracy of ANNs with the robustness and energy efficiency of SNNs. Existing point clouds processing SNNs have two issues to be solved: first, they lack a specialized surrogate gradient function; second, they are not robust enough to process a real-world dataset. In this work, we present a high-accuracy converted SNN for 3D point cloud processing. Specifically, we first revise and redesign the Spiking X-Convolution module based on the X-transformation. To address the problem of non-differentiable activation function arising from the binary signal from spiking neurons, we propose an effective adjustable surrogate gradient function, which can fit various models well by tuning the parameters. Additionally, we introduce a versatile ANN-to-SNN conversion framework enabling modular transformations. Based on this framework and the spiking X-Convolution module, we design the Spiking PointCNN, a highly efficient converted SNN for processing 3D point clouds. We conduct experiments on the public 3D point cloud datasets ModelNet40 and ScanObjectNN, on which our proposed model achieves excellent accuracy. Code will be available on GitHub. [ABSTRACT FROM AUTHOR]

Integrating and implementing spiking neurons and synapse into neuromorphic hardware aligned with spiking neural networks (SNNs) offer significant promise for energy‐efficient operation and decision making. In this work, a stacked artificial synapse and spiking neuron utilizing an indium gallium zinc oxide (IGZO) optosynaptic transistor paired with a vanadium‐based volatile threshold switching memristor are constructed. This compact neuristor encompasses multiple functionalities including the conversion of optical impulses into electrical signals, modifiable post‐synaptic current‐enhanced features, and the implementation of leaky integrate‐and‐fire (LIF) spiking generation behavior, showcasing the capability of information delivery in SNNs. The spiking activity within the proposed configuration can be effectively modulated through the interplay of optical and electrical stimuli. Additionally, the excitatory and inhibitory properties manifested by the spiking behavior underscore the gate‐tunable neuron excitability. Notably, the capacity for accommodating hybrid inputs operation makes achievement of spike‐based associative learning by reviving the Pavlov's dog experiment in the proposed device. Moreover, this research unveils the synaptic weight‐governed spiking activity, demonstrating the sophisticated input–output characteristics of spiking behavior. The stacked memristor and transistor assembly can advance the neuromorphic technologies and lay the foundation for the realization of physical SNNs. [ABSTRACT FROM AUTHOR]

The stock market index typically mirrors the financial market's performance. Hence, accurate prediction of stock market index trends is essential for investors aiming to mitigate financial risk and enhance future investment returns. Traditional statistical approaches often struggle with the non-linear nature of stock market index data, leading to potential inaccuracies in long-term predictions. To address this issue, we introduce the TCN-LSTM-SNN (TLSNN) model, a hybrid framework that integrates Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) for robust feature extraction, within a highly efficient Spiking Neural Network (SNN) architecture. Additionally, we employ the Subtraction-Average-Based Optimizer (SABO) to refine the Variational Mode Decomposition (VMD) technique, thereby separating the periodic and trend components of stock indices, reducing noise interference, and establishing a decomposition ensemble framework to bolster the model's resilience. The experimental results show that the VMD-TLSNN hybrid model suggested in this study surpasses other individual benchmark models and their hybrid models in prediction accuracy. Additionally, it demonstrates notably lower energy consumption compared to other hybrid models. [ABSTRACT FROM AUTHOR]

The rapid advancement of AI and machine learning has significantly enhanced sound and acoustic recognition technologies, moving beyond traditional models to more sophisticated neural network-based methods. Among these, Spiking Neural Networks (SNNs) are particularly noteworthy. SNNs mimic biological neurons and operate on principles similar to the human brain, using analog computing mechanisms. This capability allows for efficient sound processing with low power consumption and minimal latency, ideal for real-time applications in embedded systems. This paper reviews recent developments in SNNs for sound recognition, underscoring their potential to overcome the limitations of digital computing and suggesting directions for future research. The unique attributes of SNNs could lead to breakthroughs in mimicking human auditory processing more closely. [ABSTRACT FROM AUTHOR]

This paper reviews arrhythmia classification studies using electrocardiogram (ECG) signals. Research on automatically diagnosing arrhythmia in daily life has been actively underway for early detection and treatment of heart disease. Development of automatic arrhythmia classification using ECG signal began based on handcrafted morphological feature extraction and machine learning-based classification methods. As deep neural networks (DNN) show excellent performance in the signal processing field, studies using various types of DNN are also being conducted in ECG classification. However, these DNN-based studies have extremely high computational complexity, making it challenging to perform real-time classification, and are unsuitable for low-power environments such as wearable devices due to high power consumption. Currently, research based on spiking neural network (SNN), which mimics the low-power operation of the human nervous system, is attracting attention as a method that can dramatically reduce complexity and power consumption. The classification accuracy of the SNN-based ECG classification studies is close to that of the DNN-based studies. When combined with neuromorphic hardware, it shows ultra-low-power performance, suggesting the possibility of use in lightweight devices. In this paper, the SNN-based ECG classification studies for low-power environments are mainly reviewed, and prior to this, conventional and DNN-based ECG classification studies are also reviewed. We hope that this review will be helpful to researchers and engineers interested in the field of ECG classification. [ABSTRACT FROM AUTHOR]

This paper presents a membrane computation error-minimized mixed-mode spiking neural network (SNN) crossbar array. Our approach involves imple-menting an embedded dummy switch scheme and a mid-node pre-charge scheme to construct a high-precision current-mode synapse. We effectively suppressed charge sharing between membrane capacitors and the parasitic capacitance of synapses that results in membrane computation error. A 400 X 20 SNN crossbar prototype chip is fabricated via a 28-nm FDSOI CMOS process, and 20 MNIST patterns with their sizes reduced to 20 X 20 pixels are successfully recognized under 411 μW of power consumed. Moreover, the peak-to-peak deviation of the normalized output spike count measured from the 21 fabricated SNN prototype chips is within 16.5% from the ideal value, including sample-wise random variations.

To expand the scale of spiking neural networks (SNNs), an interface circuit that supports multiple SNN cores is essential. This circuit should be designed using an asynchronous approach to leverage characteristics of SNNs similar to those of the human brain. However, the absence of a global clock presents tim-ing issues during implementation. Hence, we propose an intermediate latching template to establish asynchronous nonlinear connectivity with multipipeline processing between multiple SNN cores. We design arbitration and distribu-tion blocks in the interface circuit based on the proposed template and fabri-cate an interface circuit that supports four SNN cores using a full-custom approach in a 28-nm CMOS (complementary metal-oxide-semiconductor) FDSOI (fully depleted silicon on insulator) process. The proposed template can enhance throughput in the interface circuit by up to 53% compared with the conventional asynchronous template. The interface circuit transmits spikes while consuming 1.7 and 3.7 pJ of power, supporting 606 and 59 Mevent/s in intrachip and interchip communications, respectively.

Purpose. Causal relationship recognition is a fundamental operation in neural networks aimed at learning behavior, action planning, and inferring external world dynamics. This operation is particularly crucial for reinforcement learning (RL). In the context of spiking neural networks (SNNs), events are represented as spikes emitted by network neurons or input nodes. Detecting causal relationships within these events is essential for effective RL implementation. Methods. This research paper presents a novel approach to realize causal relationship recognition using a simple spiking binary neuron. The proposed method leverages specially designed synaptic plasticity rules, which are both straightforward and efficient. Notably, our approach accounts for the temporal aspects of detected causal links and accommodates the representation of spiking signals as single spikes or tight spike sequences (bursts), as observed in biological brains. Furthermore, this study places a strong emphasis on the hardware-friendliness of the proposed models, ensuring their efficient implementation on modern and future neuroprocessors. Results. Being compared with precise machine learning techniques, such as decision tree algorithms and convolutional neural networks, our neuron demonstrates satisfactory accuracy despite its simplicity. Conclusion. We introduce a multi-neuron structure capable of operating in more complex environments with enhanced accuracy, making it a promising candidate for the advancement of RL applications in SNNs.

The purpose of this study is to develop and investigate a model of astrocytic regulation of burst dynamics of a spiking neural network with synaptic plasticity in inhibitory synapses. Methods. The “integrate and firing” model was used as a neuron model. To describe the dynamics of synaptic connections, a conductance-dependent synapse model with corresponding characteristic relaxation times for excitatory and inhibitory synapses was used. At the same time, inhibitory synaptic plasticity, described by the Vogel model, was used in the dynamics of inhibitory synapses between excitatory and inhibitory neurons. At the same time, the dynamics of excitatory synapses was regulated by the mean-field model of gliotransmitter concentration. Results. A model for the regulation of burst dynamics in a neuron-glial network with inhibitory synaptic plasticity was developed and studied. The main dynamic modes of neuronal activity were obtained in the absence of regulation, in the presence of only synaptic plasticity, and in the presence of also astrocytic regulation of synaptic transmission. A study was conducted of the influence of astrocytic modulation on the frequency of burst activity of the neural network. Conclusion. The study showed the possibility of controlling the burst activity of a spiking neural network by taking into account inhibitory synaptic plasticity for inhibitory synapses between inhibitory and excitatory neurons, as well as astrocytic modulation of excitatory synapses. Astrocytic modulation of synaptic transmission may act as an additional mechanism for maintaining homeostasis in the neural network beyond synaptic transmission, which exists on a faster time scale.

[Display omitted] • An optoelectronic spiking neuron is developed. • The gate-modulated PDVT-10 channel was combined with a volatile threshold switching memristor. • The device achieves optoelectronic performance through resistance-matching mechanism. • The neuron alters its spiking behavior in a manner resembling that of a retina. • The artificial neuron accurately replicates neuronal signal transmission in a biologically manner. The human perception and learning heavily rely on the visual system, where the retina plays a vital role in preprocessing visual information. Developing neuromorphic vision hardware is based on imitating the neurobiological functions of the retina. In this work, an optoelectronic neuron is developed by combining a gate-modulated PDVT-10 channel with a volatile threshold switching memristor, enabling the achievement of optoelectronic performance through a resistance-matching mechanism. The optoelectronic spiking neuron exhibits the ability to alter its spiking behavior in a manner resembling that of a retina. Incorporating electrical and optical modulation, the artificial neuron accurately replicates neuronal signal transmission in a biologically manner. Moreover, it demonstrates inhibition of neuronal firing during darkness and activation upon exposure to light. Finally, the evaluation of a perceptron spiking neural network utilizing these leaky integrate-and-fire neurons is conducted through simulation to assess its capability in classifying image recognition algorithms. This research offers a hopeful direction for the development of easily expandable and hierarchically structured spiking electronics, broadening the range of potential applications in biomimetic vision within the emerging field of neuromorphic hardware. [ABSTRACT FROM AUTHOR]

The vast amount of data generated by large-scale open online course platforms provide a solid foundation for the analysis of learning behavior in the field of education. This study utilizes the historical and final learning behavior data of over 300 000 learners from 17 courses offered on the edX platform by Harvard University and the Massachusetts Institute of Technology during the 2012–2013 academic year. We have developed a spike neural network to predict learning outcomes, and analyzed the correlation between learning behavior and outcomes, aiming to identify key learning behaviors that significantly impact these outcomes. Our goal is to monitor learning progress, provide targeted references for evaluating and improving learning effectiveness, and implement intervention measures promptly. Experimental results demonstrate that the prediction model based on online learning behavior using spiking neural network achieves an impressive accuracy of 99.80%. The learning behaviors that predominantly affect learning effectiveness are found to be students’ academic performance and level of participation.

Background and Objectives: The purpose of this work is to study the influence of the extracellular matrix of the brain on the formation of burst dynamics of a spiking neural network. Materials and Methods: The Izhikevich neuron model was used as a neuron model. To describe the dynamics of the extracellular matrix of the brain, the phenomenological model of Kazantsev, constructed using the formalism of the Hodgkin – Huxley model, was used. A model of the formation of burst dynamics of a spiking neural network under the influence of the extracellular matrix of the brain was developed and studied. Results: The main dynamic modes of neural activity have been obtained in the absence of regulation and in the presence of the extracellular matrix of the brain. Conclusion: It has been explored how the modulation by the extracellular matrix of the brain can influence the frequency of burst activity of the neural network. It has been found that the regulation of neural activity, mediated by the extracellular matrix of the brain, promotes the grouping of spikes into quasi-synchronous population discharges, called population bursts. In this case, an increase in the strength of the influence of the extracellular matrix of the brain on postsynaptic currents through synaptic scaling leads to an increase in the degree of synchrony of neuron populations.

Recent advancements in neuromorphic computing have led to the development of hardware architectures inspired by Spiking Neural Networks (SNNs) to emulate the efficiency and parallel processing capabilities of the human brain. This work focuses on testing the HEENS architecture, specifically designed for high parallel processing and biological realism in SNN emulation, implemented on a ZYNQ family FPGA. The study applies this architecture to the classification of digits using the well-known MNIST database. The image resolutions were adjusted to match HEENS' processing capacity. Results were compared with existing work, demonstrating HEENS' performance comparable to other solutions. This study highlights the importance of balancing accuracy and efficiency in the execution of applications. HEENS offers a flexible solution for SNN emulation, allowing for the implementation of programmable neural and synapticmodels. It encourages the exploration of novel algorithms and network architectures, providing an alternative for real-time processing with efficient energy consumption. [ABSTRACT FROM AUTHOR]

The development of biologically realistic models of brain microcircuits and regions constitutes currently a very relevant topic in computational neuroscience. One of the main challenges of such models is the passage between different scales, going from the microscale (cellular) to the meso (microcircuit) and macroscale (region or whole-brain level), while keeping at the same time a constraint on the demand of computational resources. In this paper we introduce a multiscale modeling framework for the hippocampal CA1, a region of the brain that plays a key role in functions such as learning, memory consolidation and navigation. Ourmodeling framework goes fromthe single cell level to the macroscale and makes use of a novel mean-field model of CA1, introduced in this paper, to bridge the gap between the micro and macro scales. We test and validate the model by analyzing the response of the system to the main brain rhythms observed in the hippocampus and comparing our results with the ones of the corresponding spiking network model of CA1. Then, we analyze the implementation of synaptic plasticity within our framework, a key aspect to study the role of hippocampus in learning and memory consolidation, and we demonstrate the capability of our framework to incorporate the variations at synaptic level. Finally, we present an example of the implementation of our model to study a stimulus propagation at the macro-scale level, and we show that the results of our framework can capture the dynamics obtained in the corresponding spiking network model of the whole CA1 area. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) have garnered significant attention due to their notable energy efficiency. However, conventional SNNs rely on spike firing frequency to encode information, necessitating a fixed sampling time and leaving room for further optimization. This study presents a novel approach to reduce sampling time and conserve energy by extracting early prediction results from the intermediate layer of the network and integrating them with the final layer's predictions in a Bayesian fashion. Experimental evaluations conducted on image classification tasks using MNIST, CIFAR-10, and CIFAR-100 datasets demonstrate the efficacy of our proposed method when applied to VGGNets and ResNets models. Results indicate a substantial energy reduction of 38.8% in VGGNets and 48.0% in ResNets, illustrating the potential for achieving significant efficiency gains in spiking neural networks. These findings contribute to the ongoing research in enhancing the performance of SNNs, facilitating their deployment in resource-constrained environments. Our code is available on GitHub: https://github.com/hanebarla/BayesianSpikeFusion. [ABSTRACT FROM AUTHOR]

The development of neuromorphic optoelectronic systems opens up the possibility of the next generation of artificial vision. In this work, the novel broadband (from 365 to 940 nm) and multilevel storage optoelectronic synaptic thin‐film transistor (TFT) arrays are reported using the photosensitive conjugated polymer (poly[(9,9‐dioctylfluorenyl‐2,7‐diyl)‐co‐(bithiophene)], F8T2) sorted semiconducting single‐walled carbon nanotubes (sc‐SWCNTs) as channel materials. The broadband synaptic responses are inherited to absorption from both photosensitive F8T2 and sorted sc‐SWCNTs, and the excellent optoelectronic synaptic behaviors with 200 linearly increasing conductance states and long retention time > 103 s are attributed to the superior charge trapping at the AlOx dielectric layer grown by atomic layer deposition. Furthermore, the synaptic TFTs can achieve IOn/IOff ratios up to 106 and optoelectronic synaptic plasticity with the low power consumption (59 aJ per single pulse), which can simulate not only basic biological synaptic functions but also optical write and electrical erase, multilevel storage, and image recognition. Further, a novel Spiking Neural Network algorithm based on hardware characteristics is designed for the recognition task of Caltech 101 dataset and multiple features of the images are successfully extracted with higher accuracy (97.92%) of the recognition task from the multi‐frequency curves of the optoelectronic synaptic devices. [ABSTRACT FROM AUTHOR]

Photonic neuromorphic computing is a competitive paradigm to overcome the bottleneck of von Neumann architectures. Incoherent and coherent synaptic networks are two popular schemes realizing photonic weighting functions. Previous works have proved the distributed feedback (DFB) laser with an intracavity saturable absorber (DFB‐SA) can behavior like a spiking neuron. However, the compatibility with the incoherent synaptic architecture has not yet been demonstrated. Here the neuron‐like dynamics of a DFB‐SA laser subject to single‐wavelength and multiple‐wavelengths incoherent optical injections are experimentally demonstrated. The results show that, for the DFB‐SA laser subject to single‐wavelength incoherent injection, the neuron‐like dynamics including threshold, temporal integration, and refractory period are achieved. Besides, the range of injection wavelength that leads to a successful neuron‐like response is identified. For the DFB‐SA laser with four‐wavelength incoherent optical injection, the neuron‐like dynamics can also be achieved. In addition, the effect of wavelength interval is also considered. The logic XOR operation and Iris recognition tasks are successfully implemented. Furthermore, the feasibility of a cascaded system for the DFB‐SA lasers with four‐wavelengths incoherent optical injection is demonstrated. This work provides a feasible scheme for the system integration of photonic spiking neurons and incoherent synaptic networks. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (CNNs) are commonly used as computational models for the primate ventral stream, while deep spiking neural networks (SNNs) incorporated with both the temporal and spatial spiking information still lack investigation. We compared performances of SNN and CNN in prediction of visual responses to the naturalistic stimuli in area V4, inferior temporal (IT), and orbitofrontal cortex (OFC). The accuracies based on SNN were significantly higher than that of CNN in prediction of temporal-dynamic trajectory and averaged firing rate of visual response in V4 and IT. The temporal dynamics were captured by SNN for neurons with diverse temporal profiles and category selectivities, and most sensitively captured around the time of peak responses for each brain region. Consistently, SNN activities showed significantly stronger correlations with IT, V4 and OFC responses. In SNN, correlations with neural activities were stronger for later time-step features than early time-step features. The temporal-dynamic prediction was also significantly improved by considering preceding neural activities during the prediction. Thus, our study demonstrated SNN as a powerful temporal-dynamic model for cortical responses to complex naturalistic stimuli. [ABSTRACT FROM AUTHOR]

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In this study, the resistive switching phenomenon and synaptic mimicry characteristics of an indium tin oxide (ITO)/indium zinc oxide (IZO)/Al2O3/TaN device are characterized. The insertion of a thin Al2O3 layer via atomic layer deposition improves the resistive switching characteristics such as cycle‐to‐cycle and device‐to‐device uniformity and reduces the power consumption of the proposed device with respect to a single‐layer ITO/IZO/TaN device. The proposed device exhibits the coexistence of volatile and nonvolatile characteristics under optical and electrical measurement conditions. Nonvolatile memory characteristics with stable retention results are used for synaptic applications by emulating potentiation, depression, and spike‐timing‐dependent plasticity. Furthermore, the device shows volatile characteristics under ultraviolet‐light illumination, emulating paired‐pulse facilitation and excitatory post‐synaptic current responses. Finally, optical‐enhanced reservoir computing is implemented based on the nonlinear and volatile nature of the IZO‐based resistive random‐access memory device. [ABSTRACT FROM AUTHOR]

Human vision system remains alert for dangers and adopts high‐speed and low‐power coding methods to convert the image information to spike signals. To meet the demand for danger alert in machine vision, it is important to design intelligent sensors to integrate the functions of image perception and high‐efficiency coding for high‐priority analysis. Inspired by the human visual system, a MoS2 phototransistor is introduced on SiNx substrate enabling simultaneous image perception and time‐to‐first‐spike (TTFS) coding. The device demonstrates exceptional performance in encoding 3‐bit grayscale images, achieving a low mean squared error of 0.008 and a high structural similarity index of 0.9784. Spiking neural networks (SNN) with TTFS coding achieve high recognition accuracy (98.86%) while reducing spike count by 75%. The device array also perceives motion direction and object states by converting data temporally. This work establishes a hardware foundation to promote the performance of SNNs in efficiently identifying crucial information. [ABSTRACT FROM AUTHOR]

An event camera generates an event stream based on changes in brightness, retaining only the characteristics of moving objects, and addresses the high power consumption associated with using high-frame-rate cameras for high-speed eye-tracking tasks. However, the asynchronous incremental nature of event camera output has not been fully utilized, and there are also issues related to missing event datasets. Combining the temporal information encoding and state-preserving properties of a spiking neural network (SNN) with an event camera, a near-range eye-tracking algorithm is proposed as well as a novel event-based dataset for validation and evaluation. According to experimental results, the proposed solution outperforms artificial neural network (ANN) algorithms, while computational time remains only 12.5% of that of traditional SNN algorithms. Furthermore, the proposed algorithm allows for self-adjustment of time resolution, with a maximum achievable resolution of 0.081 ms, enhancing tracking stability while maintaining accuracy. [ABSTRACT FROM AUTHOR]

Neuromorphic hardware has become hotspot in the field of brain-like computing due to its advantages. However, the presence of external noise imposes challenges with respect to maintaining normal function of neuromorphic hardware. Biological brains have self-adaptability to external noise, meaning that a brain-like hardware with bio-plausibility can be expected to improve robustness. The purpose of this paper is to implement a highly fitted brain-like hardware with anti-interference ability (AIA) while preserving bio-plausibility. We propose a method of implementing a small-world spiking neural network (SWSNN) with bio-plausibility based on a field-programmable gate array (FPGA), in which the nodes are Izhikevich neuron modules, the edges are synaptic plasticity modules, and the topology is a small-world network. Then, the AIAs of the FPGA-based SNNs with different external noises are evaluated by two anti-interference indices. Further, taking a speech recognition task as the case study, the AIAs of these FPGA-based SNNs are verified in application. Finally, the AIA mechanism of the FPGA-based SNNs is discussed. Our results demonstrate that: (i) In the FPGA-based SWSNN, the FPGA-based Izhikevich neuron modules and the synaptic plasticity modules highly fit to the corresponding simulation results, and the topology conforms to the small-world property of human functional brain networks. (ii) Based on two anti-interference indices, the FPGA-based SWSNN outperforms the FPGA-based SNNs with other topologies, which is further verified by the speech recognition accuracy. (iii) Our discussions hint that the synaptic plasticity is intrinsic factor of the AIA, and the topology is a factor affecting the AIA. [ABSTRACT FROM AUTHOR]

Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical signal processing and is strongly associated with other vital signs such as blood pressure, heart rate, and heart rate variability. Various physiological signals, such as photoplethysmogram (PPG) signals, are used to extract respiratory information. RR is also estimated by detecting peak patterns and cycles in the signals through signal processing and deep-learning approaches. In this study, we propose an end-to-end RR estimation approach based on a third-generation artificial neural network model—spiking neural network. The proposed model employs PPG segments as inputs, and directly converts them into sequential spike events. This design aims to reduce information loss during the conversion of the input data into spike events. In addition, we use feedback-based integrate-and-fire neurons as the activation functions, which effectively transmit temporal information. The network is evaluated using the BIDMC respiratory dataset with three different window sizes (16, 32, and 64 s). The proposed model achieves mean absolute errors of 1.37 ± 0.04, 1.23 ± 0.03, and 1.15 ± 0.07 for the 16, 32, and 64 s window sizes, respectively. Furthermore, it demonstrates superior energy efficiency compared with other deep learning models. This study demonstrates the potential of the spiking neural networks for RR monitoring, offering a novel approach for RR estimation from the PPG signal. [ABSTRACT FROM AUTHOR]

The spiking convolutional neural network (SCNN) is a kind of spiking neural network (SNN) with high accuracy for visual tasks and power efficiency on neuromorphic hardware, which is attractive for edge applications. However, it is challenging to implement SCNNs on resource-constrained edge devices because of the large number of convolutional operations and membrane potential (Vm) storage needed. Previous works have focused on timestep reduction, network pruning, and network quantization to realize SCNN implementation on edge devices. However, they overlooked similarities between spiking feature maps (SFmaps), which contain significant redundancy and cause unnecessary computation and storage. This work proposes a dual-threshold spiking convolutional neural network (DT-SCNN) to decrease the number of operations and memory access by utilizing similarities between SFmaps. The DT-SCNN employs dual firing thresholds to derive two similar SFmaps from one Vm map, reducing the number of convolutional operations and decreasing the volume of Vms and convolutional weights by half. We propose a variant spatio-temporal back propagation (STBP) training method with a two-stage strategy to train DT-SCNNs to decrease the inference timestep to 1. The experimental results show that the dual-thresholds mechanism achieves a 50% reduction in operations and data storage for the convolutional layers compared to conventional SCNNs while achieving not more than a 0.4% accuracy loss on the CIFAR10, MNIST, and FashionMNIST datasets. Due to the lightweight network and single timestep inference, the DT-SCNN has the least number of operations compared to previous works, paving the way for low-latency and power-efficient edge applications. [ABSTRACT FROM AUTHOR]

Monitoring heart conditions through electrocardiography (ECG) has been the cornerstone of identifying cardiac irregularities. Cardiologists often rely on a detailed analysis of ECG recordings to pinpoint deviations that are indicative of heart anomalies. This traditional method, while effective, demands significant expertise and is susceptible to inaccuracies due to its manual nature. In the realm of computational analysis, Artificial Neural Networks (ANNs) have gained prominence across various domains, which can be attributed to their superior analytical capabilities. Conversely, Spiking Neural Networks (SNNs), which mimic the neural activity of the brain more closely through impulse-based processing, have not seen widespread adoption. The challenge lies primarily in the complexity of their training methodologies. Despite this, SNNs offer a promising avenue for energy-efficient computational models capable of displaying a high-level performance. This paper introduces an innovative approach employing SNNs augmented with an attention mechanism to enhance feature recognition in ECG signals. By leveraging the inherent efficiency of SNNs, coupled with the precision of attention modules, this model aims to refine the analysis of cardiac signals. The novel aspect of our methodology involves adapting the learned parameters from ANNs to SNNs using leaky integrate-and-fire (LIF) neurons. This transfer learning strategy not only capitalizes on the strengths of both neural network models but also addresses the training challenges associated with SNNs. The proposed method is evaluated through extensive experiments on two publicly available benchmark ECG datasets. The results show that our model achieves an overall accuracy of 93.8% on the MIT-BIH Arrhythmia dataset and 85.8% on the 2017 PhysioNet Challenge dataset. This advancement underscores the potential of SNNs in the field of medical diagnostics, offering a path towards more accurate, efficient, and less resource-intensive analyses of heart diseases. [ABSTRACT FROM AUTHOR]

The results of first-step research activity are presented for realizing an envisioned "event radio" capability that mimics neuromorphic event-based camera processing. The energy efficiency of neuromorphic processing is orders of magnitude higher than traditional von Neumann-based processing and is realized through synergistic design of brain-inspired software and hardware computing elements. Relative to event-based cameras, the development of event-based hardware devices supporting Radio Frequency (RF) applications is severely lagging and considerable interest remains in obtaining neuromorphic efficiency through event-based RF signal processing. In the Operational Technology (OT) protection arena, this includes efficient software computing capability to provide reliable device classification. A Random Forest (RndF) classifier is considered here as a reliable precursor to obtaining Spiking Neural Network (SNN) benefits. Both 1D and 2D eventized RF fingerprints are generated for bursts from NDev = 8 WirelessHART devices. Average correct classification (%C) results show that 2D fingerprinting is best overall using detected events in burst Gabor transform responses. This includes %C ≥ 90% under multiple access interference conditions using an average of NEPB ≥ 400 detected events per burst. This is sufficiently promising to motivate next-step activity aimed at (1) reducing fingerprint dimensionality and minimizing the required computational resources, and (2) transitioning to a neuromorphic-friendly SNN classifier—two significant steps toward developing the necessary computing elements to achieve the full benefits of neuromorphic processing in the envisioned RF event radio. [ABSTRACT FROM AUTHOR]

In this study, a triple‐gated transistor with a p+‐i‐n+ silicon nanosheet (NS) is proposed as a single synaptic device, and bidirectional synaptic functions are realized using reconfigurable memory characteristics. The triple‐gated NS transistor features steep switching and bistable characteristics with a subthreshold swing below 5 mV dec−1 and an ON/OFF current ratio of ≈5 × 106 for both the n‐ and p‐channel modes. This transistor exhibits electrically symmetric reconfigurable memory characteristics with an ON current ratio of 1.02 for the n‐ and p‐channel modes. Moreover, the bidirectional synaptic weight updates of binarized spike‐timing‐dependent plasticity learning are successfully performed in a single transistor. This study demonstrates the potential of a triple‐gated NS transistor for achieving compact synaptic arrays in large‐scale silicon‐based neuromorphic computing systems. [ABSTRACT FROM AUTHOR]

Abstract In spiking neural networks (SNNs), artificial sensor neurons are crucial for converting real‐world analog information into encoded spikes. However, existing SNNs face challenges due to the inefficient implementation of input sensor neurons. Here, this study proposes an SNN‐compatible spike mode sensor, designed to directly convert analog current signals into real‐time encoded spikes, feeding the SNN concurrently. The input sensor neuron is realized using a stable neuron circuit employing a threshold switching (TS) memristor and secondary order RC block. This design enables time delay‐free spike firing, operates at low voltage, and offers a wide signal sensing range. Furthermore, this study presents an expression delineating the relationship between the pulse emission properties of the circuit and the parameters of its components, laying the basis for circuit components design and development. Analytical analysis confirms the sensor's efficacy in implementing rate‐based and time‐to‐first spike encoding schemes. Integrating the sensor into SNNs as the input layer for image training and recognition tasks yields an impressive accuracy of 87.58% on the MNIST dataset, showcasing its applicability as a crucial interface between the physical world and the SNN framework.

Mobile, low-cost, and energy-aware operation of Artificial Intelligence (AI) computations in smart circuits and autonomous robots will play an important role in the next industrial leap in intelligent automation and assistive devices. Neuromorphic hardware with spiking neural network (SNN) architecture utilizes insights from biological phenomena to offer encouraging solutions. Previous studies have proposed reinforcement learning (RL) models for SNN responses in the rat hippocampus to an environment where rewards depend on the context. The scale of these models matches the scope and capacity of small embedded systems in the framework of Internet-of-Bodies (IoB), autonomous sensor nodes, and other edge applications. Addressing energy-efficient artificial learning problems in such systems enables smart micro-systems with edge intelligence. A novel bio-inspired RL system architecture is presented in this work, leading to significant energy consumption benefits without foregoing real-time autonomous processing and accuracy requirements of the context-dependent task. The hardware architecture successfully models features analogous to synaptic tagging, changes in the exploration schemes, synapse saturation, and spatially localized task-based activation observed in the brain. The design has been synthesized, simulated, and tested on Intel MAX10 Field-Programmable Gate Array (FPGA). The problem-based bio-inspired approach to SNN edge architectural design results in 25X reduction in average power compared to the state-of-the-art for a test with real-time context learning and 30 trials. Furthermore, 940x lower energy consumption is achieved due to improvement in the execution time.