Suchergebnisse - "Spiking neural networks"

Implementing Holographic Reduced Representations for Spiking Neural Networks

Autoren: Sumanasena, Vidura de Silva, Daswin Osipov, Evgeny et al.

Quelle: IEEE Access. 13:116606-116620

Schlagwörter: Spiking neural networks (SNN), vector symbolic architecture (VSA), holographic reduced representations, neuromorphic computing, Dependable Communication and Computation Systems, Kommunikations- och beräkningssystem

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-114170
https://doi.org/10.1109/ACCESS.2025.3580582

Highly scalable hardware architecture for real-time execution of spiking neural networks applied to neural cognitive applications

Autoren: Vallejo Mancero, Bernardo Javier

Weitere Verfasser: University/Department: Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica

Thesis Advisors: Madrenas Boadas, Jordi, Zapata Rodríguez, Mireya Patricia

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Spiking Neural Networks (SNN), Neuromorphic Computing, HEENS Architecture, Real-time Processing, FPGA Implementation, Synaptic Plasticity, Spiking neural models, Real-time sensor processing, Redes Neuronales de Impulsos (SNN), Computación Neuromórfica, Procesamiento en Tiempo Real, Implementación en FPGA, Plasticidad Sináptica, Modelos Neuronales de Impulsos, Procesamiento de Sensores en Tiempo Real, Xarxes Neuronals de POlsos (SNN), Computació Neuromòrfica, Arquitectura HEENS, Processament en Temps Real, Implementació en FPGA, Plasticitat Sinàptica, Models Neuronals de Polsos, Processament de Sensors en Temps Real, Àrees temàtiques de la UPC::Enginyeria electrònica, Àrees temàtiques de la UPC::Informàtica, 621.3 - Enginyeria elèctrica. Electrotècnia. Telecomunicacions, 004 - Informàtica

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/694228
https://dx.doi.org/10.5821/dissertation-2117-427834

SpiRec: Soft-Logic Architecture Exploration of Reconfigurable Systems for Spiking Neural Networks: Soft-Logic Architecture Exploration of Reconfigurable Systems for Spiking Neural Networks

Autoren: Lai, Xunqin Corradi, Federico Sahoo, Siva Satyendra

Quelle: 2025 IEEE 36th International Conference on Application-specific Systems, Architectures and Processors (ASAP). :109-116

Schlagwörter: Artificial intelligence, Spiking neural networks, Logic, Field Programmable Gate Arrays, Resource management, Field programmable gate arrays, Logic gates, AI Computing, Spiking Neural Networks, Reconfigurable Architectures, Computer architecture, Delays, Systems architecture, Routing

Analog VLSI Implementation of Subthreshold Spiking Neural Networks and Its Application to Reservoir Computing

Autoren: Satoshi Moriya Masaya Ishikawa Satoshi Ono et al.

Quelle: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)

Schlagwörter: Neurons, Mathematical models, Spiking neural networks, Analog circuit, Transistors, Àrees temàtiques de la UPC::Enginyeria electrònica::Microelectrònica, Circuits, Information processing, Synapses, Spiking neural network, Neuromorphic computing, Power demand, Membrane potentials, Reservoir computing

Dateibeschreibung: application/pdf

Spike-TBR: A noise resilient neuromorphic event representation

Autoren: Magrini, Gabriele Becattini, Federico Cultrera, Luca et al.

Quelle: Pattern Recognition Letters. 196:198-205

Schlagwörter: FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Event cameras, Neuromorphic cameras, Spiking neural networks, Temporal binary representation

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2506.04817

STDP-Trained Spiking Neural Network Reliability Assessment Through Fault Injections

Autoren: Jouni, Zalfa Stratigopoulos, Haralampos-G. Stratigopoulos, Haralampos

Weitere Verfasser: Jouni, Zalfa Stratigopoulos, Haralampos-G. Stratigopoulos, Haralampos

Quelle: 2025 IEEE 31st International Symposium on On-Line Testing and Robust System Design (IOLTS). :1-8

Schlagwörter: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Spiking neural networks, STDP Spike-Timing Dependant Plasticity, Neuromorphic computing, Fault injection analysis, Reliability, [INFO.INFO-IA] Computer Science [cs]/Computer Aided Engineering

Dateibeschreibung: application/pdf

Zugangs-URL: https://hal.science/hal-05083335v1

Unsupervised Particle Tracking with Neuromorphic Computing

Autoren: Coradin, Emanuele Cufino, Fabio Khan, Awais et al.

Quelle: Particles. 8(2)

Schlagwörter: particle detectors, particle tracking, neuromorphic computing, unsupervised learning, spiking neural networks, genetic algorithms, Maskininlärning, Machine Learning

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-113926
https://doi.org/10.3390/particles8020040

Neuromorphic Readout for Hadron Calorimeters

Autoren: Lupi, Enrico Abhishek, NA Aehle, Max et al.

Quelle: Particles. 8(2)

Schlagwörter: machine learning, neuromorphic computing, calorimeter, particle detector, spiking neural networks, nanowire, III-V semiconductor nanowires, nanophotonics, Maskininlärning, Machine Learning

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-112583
https://doi.org/10.3390/particles8020052

Input-Triggered Hardware Trojan Attack on Spiking Neural Networks

Autoren: Raptis, Spyridon Kling, Paul Kaskampas, Ioannis et al.

Weitere Verfasser: Raptis, Spyridon Kling, Paul Kaskampas, Ioannis et al.

Quelle: Raptis, S, Kling, P, Kaskampas, I, Alouani, I & Stratigopoulos, H-G 2025, Input-triggered hardware trojan attack on spiking neural networks. in 2025 IEEE International Symposium on Hardware Oriented Security and Trust (HOST): Proceedings. 2025 IEEE International Symposium on Hardware Oriented Security and Trust (HOST): Proceedings, Institute of Electrical and Electronics Engineers Inc., 2025 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), San Jose, United States, 06/05/2025. https://doi.org/10.1109/HOST64725.2025.11050044

Schlagwörter: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, Spiking neural networks, neural network, Computer Science - Artificial Intelligence, [SPI.NANO] Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, Computer Science - Neural and Evolutionary Computing, Hardware security and trust, Artificial Intelligence (cs.AI), hardware trojan, Neuromorphic computing, spiking neural networks, Hardware Trojans, Neural and Evolutionary Computing (cs.NE), [INFO.INFO-CR] Computer Science [cs]/Cryptography and Security [cs.CR]

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2503.21793
https://pure.qub.ac.uk/en/publications/5b73de6a-b6d9-4255-9b51-28cb5be3cf4e
https://hal.science/hal-04999625v1

Spiker+: A Framework for the Generation of Efficient Spiking Neural Networks FPGA Accelerators for Inference at the Edge

Autoren: Carpegna, Alessio Savino, Alessandro Di Carlo, Stefano

Quelle: IEEE Transactions on Emerging Topics in Computing. 13:784-798

Schlagwörter: FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Hardware Architecture (cs.AR), 0202 electrical engineering, electronic engineering, information engineering, Computer Science - Neural and Evolutionary Computing, 02 engineering and technology, Neural and Evolutionary Computing (cs.NE), Spiking Neural Networks, LIF, FPGA, Neuromorphic accelerator, Edge computing, Artificial Intelligence, Frugal AI, Electronic Design Automation, High-level synthesis, Computer Science - Hardware Architecture

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2401.01141

Reinforcement Learning with Spiking Neural Networks of Multi-Quadratic Integrate-and-Fire neurons

Autoren: Debor, Antoine Sacré, Pierre Drion, Guillaume

Quelle: 44th Benelux Meeting on Systems and Control, Netherlands [NL], from 18 to 20 March 2025

Schlagwörter: reinforcement learning, spiking neural networks, mqif, Engineering, computing & technology, Computer science, Ingénierie, informatique & technologie, Sciences informatiques

Zugangs-URL: https://orbi.uliege.be/handle/2268/333216

Slow Electronics with Reservoir Computing

Weitere Verfasser: Inoue, Isao H.

Schlagwörter: Open Access, Neuromorphic Computing, Edge Computing, Reservoir Computing, Slow Electronics, Spiking Neural Networks, Realtime Learning, Low-Frequency Signals, Computer hardware, Machine learning, Mathematical modelling, Neurosciences, Biomedical engineering

Dateibeschreibung: application/pdf

Relation: Computer Science; Computer Science (R0)

Zugangs-URL: https://library.oapen.org/handle/20.500.12657/108718

IzhiRISC-V-a RISC-V-based Processor with Custom ISA Extension for Spiking Neuron Networks Processing with Izhikevich Neurons

Autoren: Szczerek, Wiktor Jan Podobas, Artur

Quelle: Proceedings of the SC’25 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis. :1667-1675

Schlagwörter: RISC-V, ISA, spiking neural networks, neuromorphic computing

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-373252
https://doi.org/10.1145/3731599.3767529

Exploring Neuromorphic Paradigms in Softwarized Networks: A Preliminary Study

Autoren: J Simas, Arthur Esteve Rothenberg, Christian

Quelle: 2025 IEEE 11th International Conference on Network Softwarization (NetSoft). :439-442

Schlagwörter: Spiking neural networks, WP.NW4, Neuromorphic computing, Cognitive network management, Softwarized networks

Delayed Memory Unit: Modeling Temporal Dependency Through Delay Gate

Autoren: Pengfei Sun Jibin Wu Malu Zhang et al.

Quelle: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

Schlagwörter: SPIKING NEURAL-NETWORKS, Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, Technology and Engineering, recurrent neural network (RNN), speech recognition, Computer Science - Neural and Evolutionary Computing, delay line, Machine Learning (cs.LG), Audio and Speech Processing (eess.AS), time series analysis, Delay gate, FOS: Electrical engineering, electronic engineering, information engineering, Neural and Evolutionary Computing (cs.NE), Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Audio and Speech Processing

Dateibeschreibung: application/pdf

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/40030387
http://arxiv.org/abs/2310.14982
https://biblio.ugent.be/publication/01K4QFFFXWDMWGPFWC01VSDAR6/file/01K4QFPYKXE455CJVGGCQ5B0HD
http://hdl.handle.net/1854/LU-01K4QFFFXWDMWGPFWC01VSDAR6
http://doi.org/10.1109/TNNLS.2024.3490833
https://biblio.ugent.be/publication/01K4QFFFXWDMWGPFWC01VSDAR6

Future projections for mammalian whole-brain simulations based on technological trends in related fields

Autoren: Jun Igarashi

Quelle: Neuroscience Research, Vol 215, Iss, Pp 64-76 (2025)

Schlagwörter: Neurons, Spiking neural networks, Whole-brain simulations, Models, Neurological, Brain, Neurosciences. Biological psychiatry. Neuropsychiatry, Connectomics, Supercomputers, Mice, Connectome, Animals, Humans, Computer Simulation, Neural activity measurements, Transcriptomics, RC321-571

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/39571736
https://doaj.org/article/c8c34b84a62345cbb3f041a7d13b31d8

NEXUS: A 28nm 3.3pJ/SOP 16-Core Spiking Neural Network With a Diamond Topology for Real-Time Data Processing

Autoren: Maryam Sadeghi Yasser Rezaeiyan Dario Fernandez Khatiboun et al.

Quelle: Sadeghi, M, Rezaeiyan, Y, Fernández Khatiboun, D, Eissa, S, Corradi, F, Augustine, C & Moradi, F 2025, 'NEXUS : A 28nm 3.3pJ/SOP 16-Core Spiking Neural Network with a Diamond Topology for Real-Time Data Processing', IEEE Transactions on Biomedical Circuits and Systems, vol. 19, no. 3, pp. 523-535. https://doi.org/10.1109/TBCAS.2024.3452635

Schlagwörter: network on chip (NoC), Action Potentials/physiology, Neural Networks, biomedical signal processing, Equipment Design, neuromorphic computing, network on chip, Computer, congestion management, Computer-Assisted, spiking neural networks, Neuromorphic computing, Signal Processing, Humans, SDG 7 - Affordable and Clean Energy, spatio-temporal sparsity, spiking neural network (SNN), Neurons/physiology, SDG 7 – Betaalbare en schone energie

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/39213266

Genetic Motifs as a Blueprint for Mismatch-Tolerant Neuromorphic Computing

Autoren: Boccato, Tommaso Zendrikov, Dmitrii Toschi, Nicola et al.

Quelle: arXiv

Schlagwörter: FOS: Computer and information sciences, Spiking neural networks, Device mismatch, Computer Science - Neural and Evolutionary Computing, Network neuroscience, Neural and Evolutionary Computing (cs.NE), Mixed-signal chips

Dateibeschreibung: application/application/pdf

Zugangs-URL: http://arxiv.org/abs/2410.19403
http://hdl.handle.net/20.500.11850/716329

Deep Learning-Based Event Counting for Apnea-Hypopnea Index Estimation Using Recursive Spiking Neural Networks

Autoren: Lorin Werthen-Brabants Yolanda Castillo-Escario Willemijn Groenendaal et al.

Quelle: IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING

Schlagwörter: Male, Adult, Technology and Engineering, SOUND, PREDICTION, AHI estimation, Polysomnography, deep learning, Signal Processing, Computer-Assisted, Middle Aged, Sleep apnea detection, wearables, Deep Learning, Sleep Apnea Syndromes, spiking neural networks, Humans, Female, Neural Networks, Computer, SLEEP-APNEA, Algorithms, Aged

Dateibeschreibung: application/pdf

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/40030371
http://hdl.handle.net/1854/LU-01JQXFR3GCY7TX1F5BYQ9DHSP8
http://doi.org/10.1109/TBME.2024.3498097
https://biblio.ugent.be/publication/01JQXFR3GCY7TX1F5BYQ9DHSP8
https://biblio.ugent.be/publication/01JQXFR3GCY7TX1F5BYQ9DHSP8/file/01JQXFSYERZTHP0TBPFST5DT8Y

Assessing ECG Interpretation Expertise in Medical Practitioners Through Eye Movement Data and Neuromorphic Models

Autoren: Syed Mohsin Bokhari Muhammad Shafi Fazal Noor et al.

Quelle: IEEE Access, Vol 13, Pp 9430-9449 (2025)

Schlagwörter: ECG, spiking convolutional neural networks (SCNN), recurrent spiking neural networks (RSNN), Electrical engineering. Electronics. Nuclear engineering, Spiking neural networks (SNN), spiking convolutional long short-term memory (SCLSTM), random forest, TK1-9971

Zugangs-URL: https://doaj.org/article/73eef382b27c4767aa2f092140332586