NSTL国家科技图书文献中心

1. EB-SNN: An Ensemble Binary Spiking Neural Network for Visual Recognition NSTL国家科技图书文献中心

Xinjie Li | Jianxiong Tang ... - 《Pattern Recognition,Part VIII》 - International Conference on Pattern Recognition - 2025, - 313～325 - 共13页

摘要： Neural Network (EB-SNN) for accurate and memory | In recent years, spiking* neural networks (SNNs* | this paper, we propose an Ensemble Binary Spiking | -friendly visual recognition. The EB-SNN* is modeled by* | multiple binary weights for lightweight SNN* modeling*

关键词： EB-SNN | Ensemble binary weights | Knowledge alignment strategy

2. CDNA-SNN: A New Spiking Neural Network for Pattern Classification Using Neuronal Assemblies NSTL国家科技图书文献中心

Vahid Saranirad | Shirin Dora ... - 《IEEE transactions on neural networks and learning systems》 - 2025,36(2) - 2274～2287 - 共14页

摘要：Spiking neural networks (SNNs) mimic their | -regulating evolving spiking* neural (SRESN) ( $p 0.05$ ) on* | artificial neural* networks. It has been proven that* | networks of spiking* neurons have a higher computational* | neural networks. This article introduces a new type of

关键词： Neurons | Training | Firing | Biological neural networks | Backpropagation | Nonhomogeneous media | Membrane potentials

3. Sg-snn: a self-organizing spiking neural network based on temporal information NSTL国家科技图书文献中心

Shouwei,Gao | Ruixin,Zhu ... - 《Cognitive Neurodynamics》 - 2025,19(1) - 共5页

摘要： using a spiking* neural network. The TSO method | Neural Network* (SG-SNN) can generate attention | network self-organization relies predominantly on static | demonstrate that the proposed Self-organizing Glial Spiking | . Furthermore, the SG-SNN* shows improved accuracy on three*

关键词： Spiking neural network | Self-organizing map | Glial neuron model | Neuromorphic data

4. MTSA-SNN: A Multi-modal Time Series Analysis Model Based on Spiking Neural Network NSTL国家科技图书文献中心

Chengzhi Liu | Zihong Luo ... - 《Pattern Recognition,Part XIV》 - International Conference on Pattern Recognition - 2025, - 424～438 - 共15页

摘要： Model Based on Spiking* Neural Network (MTSA-SNN). The* | neural networks struggle with complex, non-stationary | ://github.com/Chenngzz/MTSA-SNN. | Time series analysis and modelling constitute | a crucial research area. Traditional artificial

关键词： Multi-Modal | Time series analysis | Spiking neural network | Joint learning | Pulse encoder | Wavelet transform

5. ECG Signal Classification with a Multi-stage Model Integrating CNN, SNN, and ResNet NSTL国家科技图书文献中心

Xiangyu Shi | Dianjing Cheng ... - 《Advanced Data Mining and Applications,Part I》 - International Conference on Advanced Data Mining and Applications - 2025, - 361～372 - 共12页

摘要：, existing Spiking* Neural Network (SNN) often exhibit* | model that integrates Convolutional Neural Network | (CNN), SNN, and Residual Network* (ResNet) to enhance* | approach for the application of Spiking* Neural Networks* | from the ECG signals, followed by SNN* to capture*

关键词： Convolutional neural network (CNN) | Spiking neural network (SNN) | Residual network (ResNet) | Electrocardiogram (ECG) | Signal processing and classification

6. Spiking Spatiotemporal Neural Architecture Search for EEG-Based Emotion Recognition NSTL国家科技图书文献中心

Li, Wei | Zhu, Zhihao ... - 《IEEE Transactions on Instrumentation and Measurement》 - 2025,74(Pt.1) - 4001014.1～4001014.14 - 共14页

摘要：Spiking neural network (SNN) has the promising | convolution neural* network (SCNN) and spiking long short* | recognition. However, manually designing suitable SNN | propose a novel and effective method, spiking | spatiotemporal neural* architecture search (SSTNAS), for EEG*

关键词： Feature extraction | Electroencephalography | Emotion recognition | Neurons | Convolution | Brain modeling | Computer architecture | Membrane potentials | Genetics | Training...

7. Integrating Multimodal Spatiotemporal Brain Data with Spiking Neural Networks NSTL国家科技图书文献中心

Maryam Doborjeh | Zien Huang ... - 《Brain Informatics,Part II》 - International Conference on Brain Informatics - 2025, - 137～148 - 共12页

摘要：-SNN or fMRI-SNN. The utilization of spiking neural | methodology is built upon a brain-inspired spiking neural | network (SNN) architecture known as NeuCube. In this | (fMRI), are employed to train and test the SNN* models* | classification accuracy by employing an integrated EEG-fMRI SNN

关键词： Spiking neural network (SNN) | Spatio-temporal brain data (STBD) | Electroencephalogram (EEG) | Functional magnetic resonance imaging (fMRI) | NeuCube | Data fusion | Data integration

8. Highly scalable In 0.95 Ga 0.05 As based controllable leaky-integrate-fire neuron for high performance spiking neural network and applications NSTL国家科技图书文献中心

Faizan, Mohd | Loan, Sajad A.... - 《Physica Scripta》 - 2025,100(2) - 共13页

摘要： integration in spiking* neural network hardware* | neural networks (SNN) energy efficiency by causing | creating the spiking* characteristic in this case. It* | . The LIF neuron exhibits a 0.5 MHz high spiking | application of gate voltage, which can increase the spiking

关键词： indium gallium arsenide (InGaAs) | MOSFET | leaky integrate and fire (LIF) model | spiking neural network (SNN) | threshold logic gates

9. Hyperspectral image classification based on faster residual multi-branch spiking neural network NSTL国家科技图书文献中心

Li, Yahui | Liu, Yang ... - 《Computers & geosciences》 - 2025,197(Mar.) - 1.1～1.18 - 共18页

摘要： used in mobile edge computing. Spiking neural network | Deep convolutional neural* network has strong* | (SNN) has the characteristics of event-driven and low | residual multi- branch SNN* (FRM-SNN) based on leaky* | network uses the residual multi-branch module (RMM) as

关键词： Hyperspectral image (HSI) classification | Spiking neural networks (SNN) | Faster residual multi-branch spiking neural network (FRM-SNN) | Approximate derivative | Residual multi-branch module (RMM) | MULTISCALE

10. SpikePR: Position Regression With Deep Spiking Neural Network NSTL国家科技图书文献中心

Huang, Zhao | Zeng, Yifeng ... - 《IEEE sensors journal》 - 2025,25(3) - 4350～4359 - 共10页

摘要： Regression algorithm with a deep spiking neural network | artificial neural* networks (ANNs) in recent years* | (SNN, called SpikePR)-an architecture inspired by | Data-driven human localization technology has | been on the rise with advancements in end-to-end

关键词： Location awareness | Accuracy | Sensors | Spiking neural networks | Position measurement | Estimation | Dead reckoning | Pedestrians | Neurons | Energy consumption...

摘要： Neural Network (EB-SNN) for accurate and memory | In recent years, spiking neural networks (SNNs | this paper, we propose an Ensemble Binary Spiking | -friendly visual recognition. The EB-SNN is modeled by | multiple binary weights for lightweight SNN modeling

关键词： EB-SNN | Ensemble binary weights | Knowledge alignment strategy

摘要：Spiking neural networks (SNNs) mimic their | -regulating evolving spiking neural (SRESN) ( $p 0.05$ ) on | artificial neural networks. It has been proven that | networks of spiking neurons have a higher computational | neural networks. This article introduces a new type of

关键词： Neurons | Training | Firing | Biological neural networks | Backpropagation | Nonhomogeneous media | Membrane potentials

摘要： using a spiking neural network. The TSO method | Neural Network (SG-SNN) can generate attention | network self-organization relies predominantly on static | demonstrate that the proposed Self-organizing Glial Spiking | . Furthermore, the SG-SNN shows improved accuracy on three

关键词： Spiking neural network | Self-organizing map | Glial neuron model | Neuromorphic data

摘要： Model Based on Spiking Neural Network (MTSA-SNN). The | neural networks struggle with complex, non-stationary | ://github.com/Chenngzz/MTSA-SNN. | Time series analysis and modelling constitute | a crucial research area. Traditional artificial

关键词： Multi-Modal | Time series analysis | Spiking neural network | Joint learning | Pulse encoder | Wavelet transform

摘要：, existing Spiking Neural Network (SNN) often exhibit | model that integrates Convolutional Neural Network | (CNN), SNN, and Residual Network (ResNet) to enhance | approach for the application of Spiking Neural Networks | from the ECG signals, followed by SNN to capture

关键词： Convolutional neural network (CNN) | Spiking neural network (SNN) | Residual network (ResNet) | Electrocardiogram (ECG) | Signal processing and classification

摘要：Spiking neural network (SNN) has the promising | convolution neural network (SCNN) and spiking long short | recognition. However, manually designing suitable SNN | propose a novel and effective method, spiking | spatiotemporal neural architecture search (SSTNAS), for EEG

关键词： Feature extraction | Electroencephalography | Emotion recognition | Neurons | Convolution | Brain modeling | Computer architecture | Membrane potentials | Genetics | Training...

摘要：-SNN or fMRI-SNN. The utilization of spiking neural | methodology is built upon a brain-inspired spiking neural | network (SNN) architecture known as NeuCube. In this | (fMRI), are employed to train and test the SNN models | classification accuracy by employing an integrated EEG-fMRI SNN

关键词： Spiking neural network (SNN) | Spatio-temporal brain data (STBD) | Electroencephalogram (EEG) | Functional magnetic resonance imaging (fMRI) | NeuCube | Data fusion | Data integration

摘要： integration in spiking neural network hardware | neural networks (SNN) energy efficiency by causing | creating the spiking characteristic in this case. It | . The LIF neuron exhibits a 0.5 MHz high spiking | application of gate voltage, which can increase the spiking

关键词： indium gallium arsenide (InGaAs) | MOSFET | leaky integrate and fire (LIF) model | spiking neural network (SNN) | threshold logic gates

摘要： used in mobile edge computing. Spiking neural network | Deep convolutional neural network has strong | (SNN) has the characteristics of event-driven and low | residual multi- branch SNN (FRM-SNN) based on leaky | network uses the residual multi-branch module (RMM) as

关键词： Hyperspectral image (HSI) classification | Spiking neural networks (SNN) | Faster residual multi-branch spiking neural network (FRM-SNN) | Approximate derivative | Residual multi-branch module (RMM) | MULTISCALE

摘要： Regression algorithm with a deep spiking neural network | artificial neural networks (ANNs) in recent years | (SNN, called SpikePR)-an architecture inspired by | Data-driven human localization technology has | been on the rise with advancements in end-to-end

关键词： Location awareness | Accuracy | Sensors | Spiking neural networks | Position measurement | Estimation | Dead reckoning | Pedestrians | Neurons | Energy consumption...

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摘要： Neural Network (EB-SNN) for accurate and memory | In recent years, spiking* neural networks (SNNs* | this paper, we propose an Ensemble Binary Spiking | -friendly visual recognition. The EB-SNN* is modeled by* | multiple binary weights for lightweight SNN* modeling*

摘要：Spiking neural networks (SNNs) mimic their | -regulating evolving spiking* neural (SRESN) ( $p 0.05$ ) on* | artificial neural* networks. It has been proven that* | networks of spiking* neurons have a higher computational* | neural networks. This article introduces a new type of

摘要： using a spiking* neural network. The TSO method | Neural Network* (SG-SNN) can generate attention | network self-organization relies predominantly on static | demonstrate that the proposed Self-organizing Glial Spiking | . Furthermore, the SG-SNN* shows improved accuracy on three*

摘要： Model Based on Spiking* Neural Network (MTSA-SNN). The* | neural networks struggle with complex, non-stationary | ://github.com/Chenngzz/MTSA-SNN. | Time series analysis and modelling constitute | a crucial research area. Traditional artificial

摘要：, existing Spiking* Neural Network (SNN) often exhibit* | model that integrates Convolutional Neural Network | (CNN), SNN, and Residual Network* (ResNet) to enhance* | approach for the application of Spiking* Neural Networks* | from the ECG signals, followed by SNN* to capture*

摘要：Spiking neural network (SNN) has the promising | convolution neural* network (SCNN) and spiking long short* | recognition. However, manually designing suitable SNN | propose a novel and effective method, spiking | spatiotemporal neural* architecture search (SSTNAS), for EEG*

摘要：-SNN or fMRI-SNN. The utilization of spiking neural | methodology is built upon a brain-inspired spiking neural | network (SNN) architecture known as NeuCube. In this | (fMRI), are employed to train and test the SNN* models* | classification accuracy by employing an integrated EEG-fMRI SNN

摘要： integration in spiking* neural network hardware* | neural networks (SNN) energy efficiency by causing | creating the spiking* characteristic in this case. It* | . The LIF neuron exhibits a 0.5 MHz high spiking | application of gate voltage, which can increase the spiking

摘要： used in mobile edge computing. Spiking neural network | Deep convolutional neural* network has strong* | (SNN) has the characteristics of event-driven and low | residual multi- branch SNN* (FRM-SNN) based on leaky* | network uses the residual multi-branch module (RMM) as

摘要： Regression algorithm with a deep spiking neural network | artificial neural* networks (ANNs) in recent years* | (SNN, called SpikePR)-an architecture inspired by | Data-driven human localization technology has | been on the rise with advancements in end-to-end