Spiking Neurons Research Articles

Lightweight concrete crack detection based on spiking neural networks

PurposeMost existing methods for concrete crack detection are based on deep learning techniques such as convolutional neural networks. However, these models, due to their large memory footprint, high power consumption and insufficient feature extraction capabilities, face challenges in mobile applications. To address these issues, this paper proposes a lightweight spiking neural network detection model.Design/methodology/approachThis model achieves fast and accurate crack detection. Firstly, the Gabor-Spiking (GS) module preprocesses input images, extracting texture features and edge features of crack images through Gabor filter convolution modules and spiking convolution modules, respectively. Next, the multiscale residual (MR) module is designed, composed of convolutional layers and residual modules of various scales, to process the fused features and perform crack detection.FindingsExperimental results demonstrate that the model’s size can be reduced to 4.6 MB, achieving accuracy improvements to 87.3 and 96.4% on the SDNET and OCD datasets, respectively.Originality/valueThis paper proposes a lightweight spiking neural network detection model based on the GS module for edge texture feature fusion and the MR module for crack detection.

A VOx based optoelectronic memristor for application in visual perception

Abstract While biological vision systems excel at in-memory processing with low power consumption, traditional silicon-based vision chips struggle with high energy demands. This gap motivates the exploration of alternative materials for artificial intelligence applications. This paper presents a VOx-based optoelectronic synaptic memristive device. The proposed artificial synaptic device ITO/VOx /Pt mimics biological functions such as potentiation (P), depression (D), long-term memory (LTM), short-term memory (STM), and paired-pulse facilitation (PPF). The PPF index, standing at 105%, suggests a favorable pattern in short-term memory function. The device served as synapses within a spiking neural network (SNN) showing an achievable pattern classification accuracy of 88.68%, highlighting the potential of the VOx synaptic device for pattern classification tasks. The proposed VOx-based synaptic devices could be a promising platform for efficient pattern recognition and visual perception applications.

Implementation of a novel secured authentication protocol for cyber security applications

Robust verification protocols are crucial for maintaining the security and reliability of sensitive information due to the increasing complexity of cyber-attacks. This paper introduces a novel 5G Secure Handover Protocol aimed at addressing security and effectiveness issues encountered in existing systems. The proposed protocol is robust against various attacks, including de-synchronization, replay, man-in-the-middle (MITM), denial of services (DoS), and jamming, ensures perfect forward key secrecy, safeguarding communication confidentiality. The proposed protocol utilizes a combination of spiking neural network and fuzzy logic (SNN-FL) techniques that must choose the goal cell as carefully as possible before initiating the transfer process. By combining fuzzy logic and spiking neural networks to reduce handover latency and thwart several types of cyberattacks, the proposed 5G Secure Handover Protocol improves security. Extensive simulations show its efficacy and emphasize its potential for safe communication in large-scale cybersecurity applications. The paper presents a novel secure authentication protocol that significantly reduces handover delays and improves efficiency. Simulations show its resilience against common security threats, protecting sensitive information and maintaining secure communication channels. The protocol, with low communication expenses, complex spatial, and latency for changeover verification, is ideal for large-scale cybersecurity applications, contributing to the development of secure digital authentication mechanisms.

Understanding novel neuromodulation pathways in tDCS: brain stem recordings in rats during trigeminal nerve direct current stimulation

tDCS is widely assumed to cause neuromodulation via the electric field in the cortex acting directly on cortical neurons. However, recent evidence suggests that tDCS may indirectly influence brain activity through cranial nerve pathways, notably the trigeminal nerve, but these neuromodulatory pathways remain unexplored. To investigate the first stages in this potential pathway we developed an animal model to study the effect of trigeminal nerve direct current stimulation (TN-DCS) on neuronal activity in the principal sensory nucleus (NVsnpr) and the mesencephalic nucleus of the trigeminal nerve (MeV). We conducted experiments on twenty-four male Sprague Dawley rats (n = 10 NVsnpr, n = 10 MeV during anodic stimulation, and n = 4 MeV during cathodic stimulation). DC stimulation, ranging from 0.5 to 3 mA, targeted the trigeminal nerve’s marginal branch. Concurrently, single-unit electrophysiological recordings were obtained using a 32-channel silicon probe, encompassing three 1-min intervals: pre, during, and post-stimulation. Xylocaine trigeminal nerve blockage served as a control. TN-DCS increased neuronal spiking activity in both NVsnpr and MeV, returning to baseline during the post-stimulation phase. The 3 mA DC stimulation of the blocked trigeminal nerve failed to induce increased spiking activity in the trigeminal nuclei. These findings provide empirical support for trigeminal nuclei modulation via TN-DCS, suggesting the cranial nerve pathways could play a role in mediating the tDCS effects in humans.

Contrastive signal-dependent plasticity: Self-supervised learning in spiking neural circuits.

Brain-inspired machine intelligence research seeks to develop computational models that emulate the information processing and adaptability that distinguishes biological systems of neurons. This has led to the development of spiking neural networks, a class of models that promisingly addresses the biological implausibility and the lack of energy efficiency inherent to modern-day deep neural networks. In this work, we address the challenge of designing neurobiologically motivated schemes for adjusting the synapses of spiking networks and propose contrastive signal-dependent plasticity, a process which generalizes ideas behind self-supervised learning to facilitate local adaptation in architectures of event-based neuronal layers that operate in parallel. Our experimental simulations demonstrate a consistent advantage over other biologically plausible approaches when training recurrent spiking networks, crucially side-stepping the need for extra structure such as feedback synapses.

Solving Max-Cut Problem Using Spiking Boltzmann Machine Based on Neuromorphic Hardware with Phase Change Memory.

Efficiently solving combinatorial optimization problems (COPs) such as Max-Cut is challenging because the resources required increase exponentially with the problem size. This study proposes a hardware-friendly method for solving the Max-Cut problem by implementing a spiking neural network (SNN)-based Boltzmann machine (BM) in neuromorphic hardware systems. To implement the hardware-oriented version of the spiking Boltzmann machine (sBM), the stochastic dynamics of leaky integrate-and-fire (LIF) neurons with random walk noise are analyzed, and an innovative algorithm based on overlapping time windows is proposed. The simulation results demonstrate the effective convergence and high accuracy of the proposed method for large-scale Max-Cut problems. The proposed method is validated through successful hardware implementation on a 6-transistor/2-resistor (6T2R) neuromorphic chip with phase change memory (PCM) synapses. In addition, as an expansion of the algorithm, several annealing techniques and bias split methods are proposed to improve convergence, along with circuit design ideas for efficient evaluation of sampling convergence using cell arrays and spiking systems. Overall, the results of the proposed methods demonstrate the potential of energy-efficient and hardware-implementable approaches using SNNs to solve COPs. To the best of the author's knowledge, this is the first study to solve the Max-Cut problem using an SNN neuromorphic hardware chip.

A hybrid approach based energy management of hybrid energy systems with efficient economic model

ABSTRACT This paper proposes a hybrid AHO-SNN-based energy management (EM) of hybrid energy systems (HES) with an efficient economic model. The proposed hybrid method is combined with the Archerfish Hunting Optimizer (AHO) and Spiking Neural Network (SNN). Commonly, it is named as AHO-SNN technique. The major aim of the AHO-SNN method is the efficient economical modeling of HES and the study of sizing and EM. The comprehensive approach for describing the ideal size EM system (EMS) combination for HES is presented in this paper. The HESs, like fuel cell (FC), electrolyzer (EL), PV, battery energy system (BES), diesel generator (DSL), and hydrogen tank (HT). To determine the ideal size-EMS combination, this new EMS is employed to re-use the analytical as well as economic sizing. The AHO-SNN technique is implemented in the MATLAB software and is compared to various existing techniques. The AHO-SNN method provides an optimum outcome than the existing Particle Swarm optimization (PSO), Heap based optimizer (HBO), and Wild horse optimizer (WHO) methods. The power comparison of the existing technique HBO achieved power is 30 W, WHO is 20 W, WHO is around 10 W. In the proposed technique, the power generated is 40 W, which is way higher than the existing techniques. After implementing the integrated framework, the size of PV is cut in half, from 140 to 60 kW, which reduces the electrolyzer and the size of the hydrogen tank in also cut in half. In addition to a 35% decrease in diesel generator working hours and a 40% reduction in the leveled energy cost, PV degrades at a rate of 0.5%.

Commentary: Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch

Commentary: Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch

A Neuromorphic Radar Sensor for Low-Power IoT Systems

As radar sensors become an integral component of Internet of Things (IoT) systems, the challenge of high power consumption poses a significant barrier, especially for battery-operated devices. This article introduces NeuroRadar, a groundbreaking solution that leverages a radar front-end capable of generating spike sequences, which can be efficiently processed by energy-saving Spiking Neural Networks (SNNs). We explore the innovative design and implementation of NeuroRadar, showcasing its effectiveness in applications like gesture recognition and human tracking. By achieving dramatically lower power consumption compared to traditional radar systems, NeuroRadar represents a new paradigm in energy-efficient IoT sensing.

Phase Change Memory Drift Compensation in Spiking Neural Networks Using a Non-Linear Current Scaling Strategy

The non-ideality aspects of phase change memory (PCM) such as drift and resistance variability can pose significant obstacles in neuromorphic hardware implementations. A unique drift and variability compensation strategy is demonstrated and implemented in an FD-SOI SNN hardware unit composed of embedded phase change memories (ePCMs), current attenuators, and spiking neurons. The effect of drift and variability compensation on inference accuracy is tested on the MNIST dataset to show that our drift and variability mitigation strategy is effective in sustaining its accuracy over time. The variability is reduced by up to 5% while the drift coefficient is reduced by up to 57.8%. The drift is compensated and the SNN classification accuracy is sustained for up to 2 years with intrinsic control-free hardware that tracks the ePCM current over time and consumes less than 30 µW. The results are based on ePCM chip experimental data and pos-layout simulation of a test chip comprising the proposed circuit solution.

All-optical logical operations based on frequency-encoded photonic spiking neuron

All-optical logical operations based on frequency-encoded photonic spiking neuron

Spiking Neural Networks for Object Detection Based on Integrating Neuronal Variants and Self-Attention Mechanisms

Thanks to their event-driven asynchronous computing capabilities and low power consumption advantages, spiking neural networks (SNNs) show significant potential for computer vision tasks, especially in object detection. However, effective training methods and optimization mechanisms for SNNs remain underexplored. This study proposes two high accuracy SNNs for object detection, AMS_YOLO and AMSpiking_VGG, integrating neuronal variants and attention mechanisms. To enhance these proposed networks, we explore the impact of incorporating different neuronal variants.The results show that the optimization in the SNN’s structure with neuronal variants outperforms that in the attention mechanism for object detection. Compared to the state-of-the-art in the current SNNs, AMS_YOLO improved by 6.7% in accuracy on the static dataset COCO2017, and AMS_Spiking has improved by 11.4% on the dynamic dataset GEN1.

Brain-Inspired Architecture for Spiking Neural Networks.

Spiking neural networks (SNNs), using action potentials (spikes) to represent and transmit information, are more biologically plausible than traditional artificial neural networks. However, most of the existing SNNs require a separate preprocessing step to convert the real-valued input into spikes that are then input to the network for processing. The dissected spike-coding process may result in information loss, leading to degenerated performance. However, the biological neuron system does not perform a separate preprocessing step. Moreover, the nervous system may not have a single pathway with which to respond and process external stimuli but allows multiple circuits to perceive the same stimulus. Inspired by these advantageous aspects of the biological neural system, we propose a self-adaptive encoding spike neural network with parallel architecture. The proposed network integrates the input-encoding process into the spiking neural network architecture via convolutional operations such that the network can accept the real-valued input and automatically transform it into spikes for further processing. Meanwhile, the proposed network contains two identical parallel branches, inspired by the biological nervous system that processes information in both serial and parallel. The experimental results on multiple image classification tasks reveal that the proposed network can obtain competitive performance, suggesting the effectiveness of the proposed architecture.

Spike-based rotation detection using a self-powered triboelectric sensor with mimicry of a vestibular system

The biological vestibular system in semicircular canals detects head rotation with high energy efficiency through its unique spike-based processing. We propose a self-powered vestibular sensory neuron inspired by the biological vestibular system and utilizing a triboelectric nanogenerator (TENG) as a rotation sensor and a neuron transistor (neuristor) as a spiking neuron. This artificial vestibular neuron simultaneously detects the angular velocity and encodes it into spikes. When the angular velocity that is applied to the TENG increases, the spiking frequency increases, and this is a desired property of the vestibular neuron. Therefore, the vestibular neuron can serve as an input neuron in a spiking neural network (SNN). We also demonstrate a fully hardware-based neuromorphic system that is attractive for its improved energy efficiency to recognize rotation axes. Compared to a vestibular system based on conventional von-Neumann computing, that based on the proposed neuromorphic computing can effectively reduce power consumption by adopting spike transmission and employing the self-powered sensor. The proposed system is promising for applications related to rotation that demands high energy-efficiency.

Between-area communication through the lens of within-area neuronal dynamics.

A core problem in systems and circuits neuroscience is deciphering the origin of shared dynamics in neuronal activity: Do they emerge through local network interactions, or are they inherited from external sources? We explore this question with large-scale networks of spatially ordered spiking neuron models where a downstream network receives input from an upstream sender network. We show that linear measures of the communication between the sender and receiver networks can discriminate between emergent or inherited population dynamics. A match in the dimensionality of the sender and receiver population activities promotes faithful communication. In contrast, a nonlinear mapping between the sender to receiver activity, for example, through downstream emergent population-wide fluctuations, can impair linear communication. Our work exposes the benefits and limitations of linear measures when analyzing between-area communication in circuits with rich population-wide neuronal dynamics.

Compact Physical Implementation of Spiking Neural Network Using Ambipolar WSe2 n-Type/p-Type Ferroelectric Field-Effect Transistor.

Spiking neural networks (SNNs) are attracting increasing interests for their ability to emulate biological processes, offering energy-efficient computation and event-driven processing. Currently, no devices are known to combine both neuronal and synaptic functions. This study presents an experimental demonstration of an ambipolar WSe2 n-type/p-type ferroelectric field-effect transistor (n/p-FeFET) integrated with ferroelectric Hf0.5Zr0.5O2 (HZO) to achieve both volatile and nonvolatile properties in a single device. The nonvolatile n-FeFET, driven by the stable ferroelectric properties of HZO, exhibits highly linear synaptic behavior. In contrast, the volatile p-FeFET, influenced by electron self-compensation in the ambipolar WSe2, enables self-resetting leaky-integrate-and-fire neurons. Integrating neuronal and synaptic functions in the same device allows for compact neuromorphic computing applications. Additionally, simulations of SNNs using experimentally calibrated synaptic and neuronal models achieved a 93.8% accuracy in MNIST digit recognition. This innovative approach advances the development of SNNs with high biomimetic fidelity and reduced hardware costs.

TDC-Less Direct Time-of-Flight Imaging Using Spiking Neural Networks

TDC-Less Direct Time-of-Flight Imaging Using Spiking Neural Networks

Asynchronous interface circuit for nonlinear connectivity in multicore spiking neural networks

AbstractTo 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.

Mixed‐mode SNN crossbar array with embedded dummy switch and mid‐node pre‐charge scheme

AbstractThis 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.

Spiking Neural Network Pressure Sensor.

Von Neumann architecture requires information to be encoded as numerical values. For that reason, artificial neural networks running on computers require the data coming from sensors to be discretized. Other network architectures that more closely mimic biological neural networks (e.g., spiking neural networks) can be simulated on von Neumann architecture, but more important, they can also be executed on dedicated electrical circuits having orders of magnitude less power consumption. Unfortunately, input signal conditioning and encoding are usually not supported by such circuits, so a separate module consisting of an analog-to-digital converter, encoder, and transmitter is required. The aim of this article is to propose a sensor architecture, the output signal of which can be directly connected to the input of a spiking neural network. We demonstrate that the output signal is a valid spike source for the Izhikevich model neurons, ensuring the proper operation of a number of neurocomputational features. The advantages are clear: much lower power consumption, smaller area, and a less complex electronic circuit. The main disadvantage is that sensor characteristics somehow limit the parameters of applicable spiking neurons. The proposed architecture is illustrated by a case study involving a capacitive pressure sensor circuit, which is compatible with most of the neurocomputational properties of the Izhikevich neuron model. The sensor itself is characterized by very low power consumption: it draws only 3.49 μA at 3.3 V.