Search Results

Brain-inspired spiking neuron networks (SNNs) have attracted widespread research interest due to their low power features, high biological plausibility, and strong spatiotemporal information processing capability. Although adopting a surrogate ...

The structure of the human brain has evolved to achieve extraordinary computing power through continuous refinement by natural selection. At present, the topology of brain-like model lacks biological plausibility. In this paper, a new brain-like ...

• We propose a fMRI-SNN constrained by functional brain network from human fMRI data.
• The fMRI-SNN is superior to other SNNs in terms of speech recognition performance.
• Recognition mechanism is discussed from neuronal firing and ...

This article investigates the application of spiking neural networks (SNNs) to the problem of topic modeling (TM): the identification of significant groups of words that represent human-understandable topics in large sets of documents. Our ...

Generative models based on neural networks present a substantial challenge within deep learning. As it stands, such models are primarily limited to the domain of artificial neural networks. Spiking neural networks, as the third generation of ...

• Identify two principal challenges in generative models based on SNNs.
• Propose our model specifically to address the two challenges.
• Introduce a new attention-based scoring decoding technique.
• Superior performance on complex ...

Reduced precision number formats have become increasingly popular in various fields of computational science, as they offer the potential to enhance energy efficiency, reduce silicon area, and improve processing speed. However, this is often at ...

Sleep apnea (SLA) is a commonly encountered sleep disorder characterized by repetitive cessation of respiration while sleeping. In the past few years, researchers have focused on developing less complex and more cost-effective ...

• A novel approach for sleep apnea detection using a spiking neural network (SNN) and single-lead ECG signal.
• The SNN model with temporal encoding and a tempotron learning model is used to detect sleep apnea.
• Utilizing heart rate ...

In the domain of robotics, rapid and accurate fault diagnosis is crucial for maintaining the reliability and efficiency of systems. Traditional convolutional neural networks (CNNs), though adept at fault detection, often falter in handling the ...

Spiking Neural Networks (SNNs) are increasingly deployed in applications on resource constraint embedding systems due to their low power. Unfortunately, SNNs are vulnerable to adversarial examples which threaten the application security. Existing ...

Spiking neural networks (SNNs), as the brain-inspired neural networks, encode information in spatio-temporal dynamics. They have the potential to serve as low-power alternatives to artificial neural networks (ANNs) due to their sparse and event-...

Interpretation of the prediction is vital for mission critical tasks. Accurate interpretation relies upon the generalization accuracy of the model. In this paper, we propose a modified gradient descent learning algorithm to improve the ...

• Modified gradient descent-based learning algorithm for a spiking neural classifier.
• Transformation of spiking neural classifier to an interpretable classifier.
• Improve generalization ability for reliable interpretation.
• Post-...

Spike-based neuromorphic hardware has demonstrated substantial potential in low energy consumption and efficient inference. However, the direct training of deep spiking neural networks is challenging, and conversion-based methods still require ...

Research on neuromorphic computing with spiking neural networks and in-memory computing to achieve low-power consumption and high-speed operation has received great attention. In this study, we proposed a new synaptic device called a fusion ...

• A fusion synapse that consists of a memristor and a capacitor has been proposed.
• Our proposed synapse represents the weight using the time constant.
• A spiking neural network was designed with our proposed synapses.
• HSPICE ...

In this study, the "resting-potential-adjustable soft-reset (RSR)" integrate-and-fire neuron model is proposed for higher reliability of hardware-based spiking neural networks (SNNs). Recently, researchers have attempted to implement neuronal ...

Display Omitted

The attention mechanism has been proven to be an effective way to improve the performance of spiking neural networks (SNNs). However, from the perspective of tensor decomposition to examine the existing attention modules, we find that the rank of ...

Lip reading is a visual alternative to enhance the intelligence of traditional speech recognition, which can benefit from retina-like event cameras that focus on dynamic movements. Spiking Neural Networks (SNNs) are inherently well-suited to ...

The detection of bacterial and viral microbes is pivotal for both human and animal well-being in the public health services and for veterinary care. Even in a laboratory, the isolation of microorganisms requires time-consuming procedures and ...

• Introduces hybrid Neural Networks with Spiking layers for classifying microbes.
• Compares model performance and energy use against traditional Deep Neural Networks.
• Enables microbial classification using less energy, supporting ...

U-Net, known for its simple yet efficient architecture, is widely utilized for image processing tasks and is particularly suitable for deployment on neuromorphic chips. This paper introduces the novel concept of Spiking-UNet for image processing, ...

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 ...

The present study aims to develop a sustainable framework employing brain-inspired neural networks for solving boundary value problems in Engineering Mechanics. Spiking neural networks, known as the third generation of artificial neural networks, ...

While speeding up cryptography tasks can be accomplished by using a multi-core architecture to parallelize computation, one of the major challenges is optimizing power consumption. In principle, depending on the computation workload, individual ...