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• A learning-evoked slow-oscillatory architecture paces population activity for offline reactivation across the human medial temporal lobe.
  europepmc 2026 置信度 0.80
• Spiking neural networks for EEG signal analysis: From theory to practice.
  europepmc 2026 置信度 0.80
• Stochasticity in action potential backpropagation: consequences for neuronal computation.
  europepmc 2026 置信度 0.80
• Data-driven ANN-based visual decoding enables unsupervised functional alignment.
  europepmc 2026 置信度 0.80
• Macroscopic dynamics of quadratic integrate-and-fire neurons subject to correlated noise.
  europepmc 2026 置信度 0.80
• A novel image-based neuronal network model framework for understanding visual multistability and neurological disorders.
  europepmc 2026 置信度 0.80
• Synaptic and intrinsic membrane defects disrupt early neural network dynamics in Down syndrome.
  europepmc 2026 置信度 0.80
• From Spontaneous Ignitions to Sensorimotor Cell Assemblies via Dopamine: A Spiking Neurocomputational Model of Infants' Hand Action Acquisition.
  europepmc 2026 置信度 0.80
• High-frequency oscillations and sleep spindles in epilepsy: from mechanisms to modeling.
  europepmc 2026 置信度 0.80
• Spatial richness of neural magnetic fields.
  europepmc 2026 置信度 0.80
• Application of neurodynamics theory in the study of neural circuits in major depressive disorder: a review on neural energy approaches.
  europepmc 2026 置信度 0.80
• Reduced inhibition, bursting, and accelerated oscillations drive early hippocampal hyperactivity in Alzheimer's disease in vivo.
  europepmc 2026 置信度 0.80
• Laminar organization of cellular microcircuits modulating human interictal epileptiform discharges.
  europepmc 2026 置信度 0.80
• Nanogroove-Induced Enhancement of Neural Spike Activity in Stem Cell-Derived Networks.
  europepmc 2026 置信度 0.80
• NeoHebbian synapses to accelerate online training of neuromorphic hardware.
  europepmc 2026 置信度 0.80
• Editorial: Advancements in neural coding: sensory perception and multiplexed encoding strategies.
  europepmc 2026 置信度 0.80
• Coherent-resonant netting: disorder-enhanced selectivity from transient wave-like dynamics on biological connectomes.
  Biological agents face an energy-information bottleneck: inference requires rapid exploration of large hypothesis spaces, yet high-gain spiking is metabolically expensive. We propose Coherent-Resonant Netting (CRN) as a two-regime decision architecture in whic…
  europepmc 2026 置信度 0.80
• A Fire Detection Method Based on a Mind-Linked Continuous-Coupled Neural Network.
  europepmc 2026 置信度 0.80
• SIFT-SNN for Traffic-Flow Infrastructure Safety: A Real-Time Context-Aware Anomaly Detection Framework.
  europepmc 2026 置信度 0.80
• A brain-inspired computational framework for image-based risk assessment.
  europepmc 2026 置信度 0.80
• Reconstructed Brain Like Neural Network R-KFDNN
  Abstract Through the neural system damage and repair process of human brain, we can construct the complex deep learning and training of the repair process such as the damage of brain like high-dimensional flexible neural network system or the local loss of dat…
  crossref zhu rongrong 2021-12-27T16:17:00Z 置信度 0.70
• Review for "Assessment of artificial neural network for thermohydrodynamic lubrication analysis"
  crossref 2020-06-05T17:04:37Z 置信度 0.70
• Deep Learning Models and Neural Network Optimization Strategies: A Review
  crossref Akash Dattatray Raut 2026-05-13T11:23:49Z 置信度 0.70
• A Comprehensive Review of Neural Network Sparsification Approaches
  crossref Zaina Lang 2024-12-04T20:11:53Z 置信度 0.70
• Reconstructed Brain Like Neural Network R-KFDNN
  Abstract Through the neural system damage and repair process of human brain, we can construct the complex deep learning and training of the repair process such as the damage of brain like high-dimensional flexible neural network system or the local loss of dat…
  crossref zhu rongrong 2021-12-13T16:47:14Z 置信度 0.70
• Review for "Assessment of artificial neural network for thermohydrodynamic lubrication analysis"
  crossref 2020-06-05T17:04:37Z 置信度 0.70
• Reconstructed Brain Like Neural Network R-KFDNN
  Abstract Through the neural system damage and repair process of human brain, we can construct the complex deep learning and training of the repair process such as the damage of brain like high-dimensional flexible neural network system or the local loss of dat…
  crossref zhu rongrong 2022-01-11T20:13:17Z 置信度 0.70
• A Review of Algorithms and Hardware Implementations for Spiking Neural Networks
  Deep Learning (DL) has contributed to the success of many applications in recent years. The applications range from simple ones such as recognizing tiny images or simple speech patterns to ones with a high level of complexity such as playing the game of Go. Ho…
  crossref Duy-Anh Nguyen, Xuan-Tu Tran, Francesca Iacopi 2021-05-24T23:35:05Z 置信度 0.70
• A new deep neural network for forecasting: Deep dendritic artificial neural network
  Abstract Deep artificial neural networks have become a good alternative to classical forecasting methods in solving forecasting problems. Popular deep neural networks classically use additive aggregation functions in their cell structures. It is available in t…
  crossref Erol Egrioglu, Eren Bas 2024-06-11T14:02:14Z 置信度 0.70
• Predicting System Surface Parameters Using Artificial Neural Network
  Abstract The prediction of apparent surface torque and the system standpipe pressure holds immense importance in any automated system or digital twin solution. These parameters provide crucial insights that are instrumental in determining various factors in th…
  crossref Mohammad Eltrissi, Omar Yousef 2024-11-04T00:10:57Z 置信度 0.70
• Neuro-Spark: A Submicrosecond Spiking Neural Networks Architecture for In-Sensor Filtering
  crossref Narsinga Rao Miniskar, Aaron R. Young, Kazi Asifuzzaman, Shruti Kulkarni 等 2024-12-02T18:37:03Z 置信度 0.70
• Utilizing Elephant Herd-Inspired Spiking Neural Networks for Enhanced Ship Detection and Classification in Marine Scene Matching
  crossref Sayed Abdhahir Y., Senthil Singh C. 2024-09-01T17:26:01Z 置信度 0.70
• Configuring Safe Spiking Neural Controllers for Cyber-Physical Systems through Formal Verification
  crossref Arkaprava Gupta, Sumana Ghosh, Ansuman Banerjee, Swarup Kumar Mohalik 2024-11-06T18:37:48Z 置信度 0.70
• An AER-based spiking convolution neural network system for image classification with low latency and high energy efficiency
  crossref Yueqi Zhang, Lichen Feng, Hongwei Shan, Liying Yang 等 2023-11-02T19:56:36Z 置信度 0.70
• Slippage Classification in Prosthetic Hands with a Spiking Neural Network
  crossref Jone Follmann, Cosimo Gentile, Francesca Cordella, Loredana Zollo 等 2024-01-03T05:02:57Z 置信度 0.70
• Efficient Training of Feed-Forward Neural Networks
  crossref Martin Moller 2024-12-10T09:11:09Z 置信度 0.70
• Analytical Calculation of Weights Convolutional Neural Network
  crossref P. Sh. Geidarov 2024-07-04T10:02:28Z 置信度 0.70
• Accurate and Optimized Labelling of Fashion Products Through Attention Based Spiking Neural Networks
  crossref N Vithyatharshana, Yedhoti Thrinayani, Yogeshwar Nitin Kulkarni, Rimjhim Padam Singh 等 2024-10-23T17:40:34Z 置信度 0.70
• Towards a general methodology for formal verification on spiking neural P systems
  crossref Mario J. Pérez-Jiménez, Luis Valencia-Cabrera, David Orellana-Martín, Antonio Ramírez-de-Arellano 2024-06-27T16:41:31Z 置信度 0.70
• Image Compression using Neural Networks
  Data compression is increasingly becoming an important subject in all areas of computing and communications. While it is true that network speeds have significantly increased, and that the price of disk storage has decreased dramatically, new computer applicat…
  crossref Christopher Cramer, Erol Gelenbe 2024-12-10T09:11:09Z 置信度 0.70
• Designing Spiking Neural Network-Based Reinforcement Learning for 3D Robotic Arm Applications
  This study investigates a novel approach to robotic arm control through integrating spiking neural networks with the twin delayed deep deterministic policy gradient reinforcement learning algorithm. Specifically, it presents the first application of spiking ne…
  crossref Yuntae Park, Jiwoon Lee, Donggyu Sim, Youngho Cho 等 2025-02-03T09:35:07Z 置信度 0.70
• Exploiting Local Optima in Multiversion Neural Computing
  The training of feedforward networks, such as multilayer perceptrons (MLPs) or radial basis function (RBF) nets, is well known to be highly sensitive to initial conditions. A change in the random initialization of the weights, even when all other conditions ar…
  crossref Derek Partridge 2024-12-10T09:11:09Z 置信度 0.70
• English letter recognition based on adaptive optimization spiking neural P systems
  crossref Qin Deng, Zexia Huang, Xiaoliang Chen, Xianyong Li 等 2024-05-06T13:01:46Z 置信度 0.70
• Sleep Stage Classification with Spiking Neural Networks and Transformers using Multi-Channel EEG Data
  crossref Junyan Li, Bin Hu, Zhi-Hong Guan, Bokun Zhang 2024-09-17T18:46:36Z 置信度 0.70
• Variations of Training Process in Vanilla Recurrent Neural Network Framework
  Recurrent neural networks (RNNs) are capable of learning features and long term dependencies from sequential and time series data and show outstanding performance in sequential modeling tasks. However, training process in RNNs is troubled by issues in learning…
  crossref Dokkyun Yi, Inmi Kim, Sunyoung Bu 2024-07-01T09:21:26Z 置信度 0.70
• Logisitics / Multinomial Logistics / Neural Network Probability Contributions
  I develop a statistical approach to decompose the probability of logistics, multinomial logistics, and neural network models into components, called QPC(k). Where P = Σ QPC(k). This approach add another tool in analyzing and visualizing the outcome of logistic…
  crossref Quoc Phan 2024-05-29T13:13:38Z 置信度 0.70
• A Spiking Convolutional Neural Network Algorithm Based on Piecewise-Linear Complementary Leaky Integrate-and-Fire (PCLIF) Neurons and Hybrid Surrogate Gradient Training
  crossref Zong-Zhe Wu, Yi-Wen Chuang, Kea-Tiong Tang 2026-01-14T20:38:47Z 置信度 0.70
• Artificial Neural Network for Digits Classification (July 2024)
  This lab report presents our work on building artificial neural networks (ANNs) from scratch and evaluating their performance on the MNIST and Kuzushiji-MNIST datasets. We experimented with different configurations to assess their impact on model accuracy, inc…
  crossref Girban Adhikari 2025-02-07T12:47:26Z 置信度 0.70
• BERA-CLOUD: resource allocation in cloud computing using a bald eagle optimised spiking neural network
  crossref Nikhil Kumar Marriwala, Sunita Panda, Priya Dasarwar, Pooja Singh 等 2025-09-22T11:30:14Z 置信度 0.70
• Neural and Neuro-Fuzzy Control Systems
  crossref Phil Picton 2024-12-10T09:11:09Z 置信度 0.70
• A Rapid Adapting and Continual Learning Spiking Neural Network Path Planning Algorithm for Mobile Robots
  crossref Harrison Espino, Robert Bain, Jeffrey L. Krichmar 2024-09-10T19:26:40Z 置信度 0.70
• Predicting the temporal-dynamic trajectories of cortical neuronal responses in non-human primates based on deep spiking neural network
  crossref Jie Zhang, Liwei Huang, Zhengyu Ma, Huihui Zhou 2023-09-05T07:01:47Z 置信度 0.70
• DSC-SNN: A Depthwise Separable Convolutional Spiking Neural Network for Efficient, Privacy-Preserving Action Recognition from Event-Based Data
  crossref Nguyen Tan Khang Le, Ha Dung Nguyen, Thanh Binh Nguyen 2025-08-29T17:39:20Z 置信度 0.70
• Real-time Image Recognition System based on Ultra-Low-Latency Spiking Neural Networks
  crossref Bin Lan, Xi Zhang, Heng Dong, Ming Xu 2025-02-12T18:16:22Z 置信度 0.70
• Novel Neuron-Stability Weighted Dynamic Evolving Spiking Neural Network (NSW-DeSNN) for Classification of fMRI Data
  crossref Maryam Doborjeh, Zohreh Doborjeh, Nikola Kasabov 2025-11-10T15:52:47Z 置信度 0.70
• Exploring spiking neural networks for deep reinforcement learning in robotic tasks
  crossref Luca Zanatta, Francesco Barchi, Simone Manoni, Silvia Tolu 等 2024-12-27T18:24:01Z 置信度 0.70
• EnsembleSTDP: Distributed in-situ Spike Timing Dependent Plasticity Learning in Spiking Neural Networks
  crossref Hanyu Yuga, Khanh N. Dang 2025-01-03T19:17:28Z 置信度 0.70
• Autoregressive neural network
  crossref Sara Rivera, Candace Moore, Joachim Feger 2024-08-06T09:51:47Z 置信度 0.70
• Plan (Pruning Learning Artificial Neural Network)
  crossref Hasan Can Beydili 2024-06-12T03:59:37Z 置信度 0.70
• Advancing neural network calibration: The role of gradient decay in large-margin Softmax optimization
  crossref Siyuan Zhang, Linbo Xie 2024-06-12T16:57:24Z 置信度 0.70
• Emergent perceptual biases from state-space geometry in trained spiking recurrent neural networks
  crossref Luis Serrano-Fernández, Manuel Beirán, Néstor Parga 2024-07-03T22:49:54Z 置信度 0.70
• Gravitational wave isolation with autoencoder neural network cascade
  crossref Mayank Sengupta 2024-02-15T12:02:08Z 置信度 0.70
• PETNet–Coincident Particle Event Detection using Spiking Neural Networks
  crossref Jan Debus, Charlotte Debus, Günther Dissertori, Markus Götz 2024-06-11T17:30:32Z 置信度 0.70
• Compressed Latent Replays for Lightweight Continual Learning on Spiking Neural Networks
  crossref Alberto Dequino, Alessio Carpegna, Davide Nadalini, Alessandro Savino 等 2024-09-25T17:27:50Z 置信度 0.70
• Spatio-Temporal Interactive Learning for Efficient Image Reconstruction of Spiking Cameras
  crossref Bin Fan, Jiaoyang Yin, Yuchao Dai, Chao Xu 等 2025-11-03T11:20:56Z 置信度 0.70
• Revealing Functions of Extra-Large Excitatory Postsynaptic Potentials: Insights from Dynamical Characteristics of Reservoir Computing with Spiking Neural Networks
  crossref Asato Fujimoto, Sou Nobukawa, Yusuke Sakemi, Yoshiho Ikeuchi 等 2024-09-16T13:02:55Z 置信度 0.70
• SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks
  Investigations in the field of spiking neural networks (SNNs) encompass diverse, yet overlapping, scientific disciplines. Examples range from purely neuroscientific investigations, researches on computational aspects of neuroscience, or applicative-oriented st…
  crossref Emanuele Gemo, Sabina Spiga, Stefano Brivio 2024-01-07T23:51:25Z 置信度 0.70
• K-order echo-type spiking neural P systems for time series forecasting
  crossref Juan He, Hong Peng, Jun Wang, Qian Yang 等 2024-09-16T06:31:29Z 置信度 0.70
• Optimizing construction cost management with BIM technology and Neural Network Models
  crossref 2024-05-23T10:07:56Z 置信度 0.70
• Investigation of Low-Energy Spiking Neural Networks Based on Temporal Coding for Scene Classification
  crossref Paolo Paolo_lunghi, Stefano Silvestrini, Dominik Dold, Gabriele Meoni 等 2025-01-31T21:07:16Z 置信度 0.70
• Author Correction: Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks
  crossref Hritom Das, Catherine Schuman, Nishith N. Chakraborty, Garrett S. Rose 2024-05-23T12:01:56Z 置信度 0.70
• Exploiting Label Skewness for Spiking Neural Networks in Federated Learning
  The energy efficiency of deep spiking neural networks (SNNs) aligns with the constraints of resource-limited edge devices, positioning SNNs as a promising foundation for intelligent applications leveraging the extensive data collected by these devices. To safe…
  crossref Di Yu, Xin Du, Linshan Jiang, Huijing Zhang 等 2024-07-26T10:28:11Z 置信度 0.70
• High-performance deep spiking neural networks with 0.3 spikes per neuron
  Abstract Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks than artificial neural networks. This is puzzling given that theoretical …
  crossref Ana Stanojevic, Stanisław Woźniak, Guillaume Bellec, Giovanni Cherubini 等 2024-08-09T00:02:30Z 置信度 0.70
• Protein structure prediction with recurrent neural network and convolutional neural network: a case study
  crossref Ritu Karwasra, Kushagra Khanna, Kapil Suchal, Ajay Sharma 等 2024-04-26T02:23:14Z 置信度 0.70
• Classification of Pests in Agricultural Farms Using Convolutional Neural Network Compared to Artificial Neural Network
  crossref Bobbilla Ramya Sri, T. Suresh Balakrishnan 2024-11-04T23:06:46Z 置信度 0.70
• A Bio-inspired Low-power Hybrid Analog/Digital Spiking Neural Networks for Pervasive Smart Cameras
  crossref Yung-Ting Hsieh, Dario Pompili 2024-04-23T18:10:48Z 置信度 0.70
• NeuDen: A Framework for the Integration of Neuromorphic Evolving Spiking Neural Networks with Dynamic Evolving Neuro-Fuzzy Systems for Predictive and Explainable Modelling of Streaming Data
  Abstract This paper introduces a novel framework, called here 'NeuDen' for the integration of neuromorphic evolving spiking neural networks (eSNN), that learn efficiently multiple time series in their temporal association and interaction, with dynamic evolving…
  crossref Iman AbouHassan, Nikola Kasabov 2024-03-19T09:56:25Z 置信度 0.70
• Implementation Convolutional Neural Network (CNN) for Bima Script Handwriting Recognition
  crossref Ahmad Angga Handoko, Mochamad Alfan Rosid 2024-05-20T09:12:47Z 置信度 0.70
• A Compact 140nW/input Winner-Take-All Circuit for Spiking Neural Networks
  crossref Gaurav R, Abhishek A. Kadam, Ajay K. Singh, Laxmeesha Somappa 等 2024-07-02T17:22:52Z 置信度 0.70
• SpikeSynth: Energy-Efficient Adaptive Analog Printed Spiking Neural Networks
  crossref Priyanjana Pal, Alexander Studt, Tara Gheshlaghi, Michael Hefenbrock 等 2025-11-20T18:39:34Z 置信度 0.70
• AI-driven plant disease detection with tailored convolutional neural network
  crossref Sk Mahmudul Hassan, Keshab Nath, Michal Jasinski, Arnab Kumar Maji 2025-08-01T06:47:26Z 置信度 0.70
• Spiking Neural Networks for RF Signal Classification and Unified Timing Estimation on Loihi 2
  crossref Howard Yanxon, Lauren Reinerman-Jones 2025-12-18T18:32:00Z 置信度 0.70
• Integration of Contrastive Predictive Coding and Spiking Neural Networks
  crossref Emirhan Bilgiç, Neslihan Serap Şengör, Namık Berk Yalabık, Yavuz Selim İşler 等 2025-08-15T18:11:33Z 置信度 0.70
• A Review On Fusion Of Spiking Neural Networks And Transformers
  crossref Oumaima Marsi, Sébastien Ambellouis, José Mennesson, Cyril Meurie 等 2025-03-22T00:29:13Z 置信度 0.70
• A dual-stage pruning method for energy-efficient spiking neural networks
  crossref Yaning Li, Han Yang, Xiurui Xie 2025-11-04T17:38:40Z 置信度 0.70
• Non-uniform Memory Partitioning For Low-Power Spiking Neural Networks
  crossref Simon Richter, Darío Fernández Khatiboun, Maryam Sadeghi, Milad Zamani 等 2025-09-25T17:52:35Z 置信度 0.70
• Brain-Machine Interface Decoding Using Optimized Recurrent Spiking Neural Networks on MCU
  crossref Eraraya Morenzo Muten, Nur Ahmadi, Timothy G. Constandinou, Trio Adiono 2026-01-27T04:49:15Z 置信度 0.70
• Topological and geometrical signatures of computation in rate, spiking, and oscillatory neural reservoirs
  The computational efficiency of a neural substrate is shaped by the geometry and topology of its state-space manifold. We propose and test a “computational matching” principle: efficiency is maximized when the intrinsic geometry of a reservoir’s representation…
  crossref Oleg V. Maslennikov 2025-12-22T14:08:51Z 置信度 0.70
• Brain‐Inspired Efficient Pruning: Exploiting Criticality in Spiking Neural Networks
  ABSTRACT Spiking neural networks (SNNs) have gained significant attention due to their energy‐efficient and multiplication‐free characteristics. Despite these advantages, deploying large‐scale SNNs on edge hardware is challenging due to limited resource availa…
  crossref Shuo Chen, Zeshi Liu, Haihang You 2025-11-03T02:44:50Z 置信度 0.70
• Spiking neural networks for sound localization: A new perspective on illuminating auditory spatial perception
  Humans estimate sound source direction using information from their auditory neural system. Traditional methods use auditory cues [e.g., interaural time differences (ITDs) and interaural level differences (ILDs), etc] to perform sound localization. These cues …
  crossref Qin Liu, Laurent S. Simon, Hervé Lissek 2025-07-18T16:44:32Z 置信度 0.70
• An area and power efficient VLSI architecture for epileptic seizure detection using Transpose Form Retimed Delayed LMS filter and spiking neural networks
  crossref Venugopal Ponnuraj, Sasikala Thangavelu, Bagirathan Kaliyamurthi 2025-05-05T23:32:01Z 置信度 0.70
• Neural Optimization Machine for Design With Neural Network Based Objectives and Constraints
  crossref Jie Chen, Yongming Liu 2025-02-12T10:03:43Z 置信度 0.70
• Artificial Neural Network Based Controlled Technique to Optimize Fuel and Battery Systemartificial Neural Network Based Controlled Technique to Optimize Fuel and Battery Systemartificial Neural Network Based Controlled Technique to Optimize Fuel and Battery System
  crossref Vikramaditya Dave, megha sen 2025-07-11T23:37:54Z 置信度 0.70
• Dygenn dynamic Gaussian weighted evolving Spiking Neural Network model for enhanced neurological disease detection
  Abstract Spiking Neural Network (SNN) is a promising field for modelling neuronal activity in the brain, with applications in healthcare, agriculture, finance, manufacturing and others. However, traditional SNNs often suffer from static synaptic weights, limit…
  crossref Priya Das, Sarita Nanda, Prabodh Kumar Sahoo, Aswini Kumar Samantaray 等 2026-08-04T21:39:57Z 置信度 0.70
• Artificial Neural Network
  crossref Venkata A. P. Chavali, Amit A. Deshmukh, Aarti G. Ambekar 2025-09-26T06:50:56Z 置信度 0.70
• Analysis and Prediction of Flood Disasters Based on BP Neural Network and Convolutional Neural Network
  crossref Yanbing Lin 2025-03-28T03:18:58Z 置信度 0.70
• Batchnorm-Free Binarized Deep Spiking Neural Network for a Lightweight Machine Learning Model
  The development of deep neural networks, although demonstrating astounding capabilities, leads to more complex models, high energy consumption, and expensive hardware costs. While network quantization is a widely used method to address this problem, the typica…
  crossref Hasna Nur Karimah, Chankyu Lee, Yeongkyo Seo 2025-04-16T04:05:31Z 置信度 0.70
• The Impact of Internal Noise on Deep and Spiking Neural Networks
  crossref Nadezhda Semenova, Daniil Maksimov, Ivan Kolesnikov 2025-10-20T17:48:23Z 置信度 0.70
• RCIM: A Reconfigurable Compute-in-Memory Architecture for Artificial, Spiking and Hybrid Neural Networks
  crossref Ruihao He, Cong Wang, Xipeng Lin, Shaoxuan Li 等 2026-01-20T20:37:10Z 置信度 0.70
• Spiking Multi Scale Graph Convolutional Neural Networks for Fault Diagnosis of Rotating Machinery
  crossref Alaeldden Abduelhadi, Jie Cao, Haopeng Liang 2026-05-15T02:41:37Z 置信度 0.70