Relevant bibliographies by topics / Spiking Neural network simulation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Spiking Neural network simulation'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Spiking Neural network simulation"

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Li, Linyang. "Review of common spiking neural network simulation tools." Applied and Computational Engineering 37, no. 1 (2024): 81–85. http://dx.doi.org/10.54254/2755-2721/37/20230474.

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As a recognized third-generation neural network, the spiking neural network has high concurrency and complexity. Although the degree of research is still far from the previous generation of neural networks, spiking neural networks are excellent in performance and energy consumption. In this paper, the common spiking neural network simulation tools are reviewed. The most frequently used and mentioned tools are NEURON, NEST, and BRAIN. NEURON is more suitable for simulation based on biological applications, pays more attention to biological characteristics, and can support large-scale network si
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Brette, Romain, and Dan F. M. Goodman. "Vectorized Algorithms for Spiking Neural Network Simulation." Neural Computation 23, no. 6 (2011): 1503–35. http://dx.doi.org/10.1162/neco_a_00123.

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High-level languages (Matlab, Python) are popular in neuroscience because they are flexible and accelerate development. However, for simulating spiking neural networks, the cost of interpretation is a bottleneck. We describe a set of algorithms to simulate large spiking neural networks efficiently with high-level languages using vector-based operations. These algorithms constitute the core of Brian, a spiking neural network simulator written in the Python language. Vectorized simulation makes it possible to combine the flexibility of high-level languages with the computational efficiency usual
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Rhodes, Oliver, Luca Peres, Andrew G. D. Rowley, et al. "Real-time cortical simulation on neuromorphic hardware." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2164 (2019): 20190160. http://dx.doi.org/10.1098/rsta.2019.0160.

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Real-time simulation of a large-scale biologically representative spiking neural network is presented, through the use of a heterogeneous parallelization scheme and SpiNNaker neuromorphic hardware. A published cortical microcircuit model is used as a benchmark test case, representing ≈1 mm 2 of early sensory cortex, containing 77 k neurons and 0.3 billion synapses. This is the first hard real-time simulation of this model, with 10 s of biological simulation time executed in 10 s wall-clock time. This surpasses best-published efforts on HPC neural simulators (3 × slowdown) and GPUs running opti
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Grinblat, Guillermo L., Hernán Ahumada, and Ernesto Kofman. "Quantized state simulation of spiking neural networks." SIMULATION 88, no. 3 (2011): 299–313. http://dx.doi.org/10.1177/0037549711399935.

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In this work, we explore the usage of quantized state system (QSS) methods in the simulation of networks of spiking neurons. We compare the simulation results obtained by these discrete-event algorithms with the results of the discrete time methods in use by the neuroscience community. We found that the computational costs of the QSS methods grow almost linearly with the size of the network, while they grows at least quadratically in the discrete time algorithms. We show that this advantage is mainly due to the fact that QSS methods only perform calculations in the components of the system tha
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Cancan, Murat. "On Ev-Degree and Ve-Degree Topological Properties of Tickysim Spiking Neural Network." Computational Intelligence and Neuroscience 2019 (June 2, 2019): 1–7. http://dx.doi.org/10.1155/2019/8429120.

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Topological indices are indispensable tools for analyzing networks to understand the underlying topology of these networks. Spiking neural network architecture (SpiNNaker or TSNN) is a million-core calculating engine which aims at simulating the behavior of aggregates of up to a billion neurons in real time. Tickysim is a timing-based simulator of the interchip interconnection network of the SpiNNaker architecture. Tickysim spiking neural network is considered to be highly symmetrical network classes. Classical degree-based topological properties of Tickysim spiking neural network have been re
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BUSYGIN, Alexander N., Andrey N. BOBYLEV, Alexey A. GUBIN, Alexander D. PISAREV, and Sergey Yu UDOVICHENKO. "NUMERICAL SIMULATION AND EXPERIMENTAL STUDY OF A HARDWARE PULSE NEURAL NETWORK WITH MEMRISTOR SYNAPSES." Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy 7, no. 2 (2021): 223–35. http://dx.doi.org/10.21684/2411-7978-2021-7-2-223-235.

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This article presents the results of a numerical simulation and an experimental study of the electrical circuit of a hardware spiking perceptron based on a memristor-diode crossbar. That has required developing and manufacturing a measuring bench, the electrical circuit of which consists of a hardware perceptron circuit and an input peripheral electrical circuit to implement the activation functions of the neurons and ensure the operation of the memory matrix in a spiking mode. The authors have performed a study of the operation of the hardware spiking neural network with memristor synapses in
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GELEN, AYKUT GÖRKEM, and AYTEN ATASOY. "SPAYK: An environment for spiking neural network simulation." Turkish Journal of Electrical Engineering and Computer Sciences 31, no. 2 (2023): 462–80. http://dx.doi.org/10.55730/1300-0632.3995.

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Zhang, Hong, and Yu Zhang. "Memory-Efficient Reversible Spiking Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16759–67. http://dx.doi.org/10.1609/aaai.v38i15.29616.

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Spiking neural networks (SNNs) are potential competitors to artificial neural networks (ANNs) due to their high energy-efficiency on neuromorphic hardware. However, SNNs are unfolded over simulation time steps during the training process. Thus, SNNs require much more memory than ANNs, which impedes the training of deeper SNN models. In this paper, we propose the reversible spiking neural network to reduce the memory cost of intermediate activations and membrane potentials during training. Firstly, we extend the reversible architecture along temporal dimension and propose the reversible spiking
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Ekelmans, Pierre, Nataliya Kraynyukovas, and Tatjana Tchumatchenko. "Targeting operational regimes of interest in recurrent neural networks." PLOS Computational Biology 19, no. 5 (2023): e1011097. http://dx.doi.org/10.1371/journal.pcbi.1011097.

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Neural computations emerge from local recurrent neural circuits or computational units such as cortical columns that comprise hundreds to a few thousand neurons. Continuous progress in connectomics, electrophysiology, and calcium imaging require tractable spiking network models that can consistently incorporate new information about the network structure and reproduce the recorded neural activity features. However, for spiking networks, it is challenging to predict which connectivity configurations and neural properties can generate fundamental operational states and specific experimentally re
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Przewlocka-Rus, Dominika, and Tomasz Kryjak. "The bioinspired traffic sign classifier." Bio-Algorithms and Med-Systems 18, no. 1 (2022): 29–38. http://dx.doi.org/10.1515/bams-2021-0159.

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Abstract Objectives In this paper the research on developing convolutional spiking neural networks for traffic signs classification is presented. Unlike classical ones, spiking networks reflect the behaviour of biological neurons much more closely, by taking into account the time dimension and event-based operation. Spiking networks running on dedicated neuromorphic platforms, such as Intel Loihi, can operate with greater energy efficiency, hence they are an interesting approach for embedded solutions. Methods For convolutional spiking neural networks' design and simulation, Nengo and NengoDL
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Dissertations / Theses on the topic "Spiking Neural network simulation"

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Hunter, Russell I. "Improving associative memory in a network of spiking neurons." Thesis, University of Stirling, 2011. http://hdl.handle.net/1893/6177.

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In this thesis we use computational neural network models to examine the dynamics and functionality of the CA3 region of the mammalian hippocampus. The emphasis of the project is to investigate how the dynamic control structures provided by inhibitory circuitry and cellular modification may effect the CA3 region during the recall of previously stored information. The CA3 region is commonly thought to work as a recurrent auto-associative neural network due to the neurophysiological characteristics found, such as, recurrent collaterals, strong and sparse synapses from external inputs and plastic
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Jin, Xin. "Parallel simulation of neural networks on SpiNNaker universal neuromorphic hardware." Thesis, University of Manchester, 2010. https://www.research.manchester.ac.uk/portal/en/theses/parallel-simulation-of-neural-networks-on-spinnaker-universal-neuromorphic-hardware(d6b8b72a-63c4-44ee-963a-ae349b0e379c).html.

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Artificial neural networks have shown great potential and have attracted much research interest. One problem faced when simulating such networks is speed. As the number of neurons increases, the time to simulate and train a network increases dramatically. This makes it difficult to simulate and train a large-scale network system without the support of a high-performance computer system. The solution we present is a "real" parallel system - using a parallel machine to simulate neural networks which are intrinsically parallel applications. SpiNNaker is a scalable massively-parallel computing sys
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Mundy, Andrew. "Real time Spaun on SpiNNaker : functional brain simulation on a massively-parallel computer architecture." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/real-time-spaun-on-spinnaker--functional-brain-simulation-on-a-massivelyparallel-computer-architecture(fcf5388c-4893-4b10-a6b4-577ffee2d562).html.

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Model building is a fundamental scientific tool. Increasingly there is interest in building neurally-implemented models of cognitive processes with the intention of modelling brains. However, simulation of such models can be prohibitively expensive in both the time and energy required. For example, Spaun - "the world's first functional brain model", comprising 2.5 million neurons - required 2.5 hours of computation for every second of simulation on a large compute cluster. SpiNNaker is a massively parallel, low power architecture specifically designed for the simulation of large neural models
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Painkras, Eustace. "A chip multiprocessor for a large-scale neural simulator." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/a-chip-multiprocessor-for-a-largescale-neural-simulator(d3637073-2669-4a81-985a-2da9eec46480).html.

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A Chip Multiprocessor for a Large-scale Neural SimulatorEustace PainkrasA thesis submitted to The University of Manchesterfor the degree of Doctor of Philosophy, 17 December 2012The modelling and simulation of large-scale spiking neural networks in biologicalreal-time places very high demands on computational processing capabilities andcommunications infrastructure. These demands are difficult to satisfy even with powerfulgeneral-purpose high-performance computers. Taking advantage of the remarkableprogress in semiconductor technologies it is now possible to design and buildan application-driv
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Harischandra, Nalin. "Computer Simulation of the Neural Control of Locomotion in the Cat and the Salamander." Doctoral thesis, KTH, Beräkningsbiologi, CB, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-47362.

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Locomotion is an integral part of a whole range of animal behaviours. The basic rhythm for locomotion in vertebrates has been shown to arise from local networks residing in the spinal cord and these networks are known as central pattern generators (CPG). However, during the locomotion, these centres are constantly interacting with the sensory feedback signals coming from muscles, joints and peripheral skin receptors in order to adapt the stepping or swimming to varying environmental conditions. Conceptual models of vertebrate locomotion have been constructed using mathematical models of locomo
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Johnson, Melissa. "A Spiking Bidirectional Associative Memory Neural Network." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42222.

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Spiking neural networks (SNNs) are a more biologically realistic model of the brain than traditional analog neural networks and therefore should be better for modelling certain functions of the human brain. This thesis uses the concept of deriving an SNN from an accepted non-spiking neural network via analysis and modifications of the transmission function. We investigate this process to determine if and how the modifications can be made to minimize loss of information during the transition from non-spiking to spiking while retaining positive features and functionality of the non-spiking netwo
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Goel, Piyush. "Spiking neural network based approach to EEG signal analysis." Thesis, University of Portsmouth, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.496600.

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The research described in this thesis presents a new classification technique for continuous electroencephalographic (EEG) recordings, based on a network of spiking neurons. Analysis of the signals is performed on ensemble EEG and the task of the neural network is to identify the P300 component in the signals. The network employs leaky-integrate-and-fire neurons as nodes in a multi-layered structure. The method involves formation of multiple weak classifiers to perform voting and collective results are used for final classification.
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Davies, Sergio. "Learning in spiking neural networks." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/learning-in-spiking-neural-networks(2d2be0d7-9557-481e-b9f1-3889a5ca2447).html.

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Artificial neural network simulators are a research field which attracts the interest of researchers from various fields, from biology to computer science. The final objectives are the understanding of the mechanisms underlying the human brain, how to reproduce them in an artificial environment, and how drugs interact with them. Multiple neural models have been proposed, each with their peculiarities, from the very complex and biologically realistic Hodgkin-Huxley neuron model to the very simple 'leaky integrate-and-fire' neuron. However, despite numerous attempts to understand the learning be
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Mekemeza, Ona Keshia. "Photonic spiking neuron network." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2023. http://www.theses.fr/2023UBFCD052.

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Les réseaux neuromorphiques pour le traitement d'informations ont pris une placeimportante aujourd'hui notamment du fait de la montée en complexité des tâches à effectuer : reconnaissance vocale, corrélation d'images dynamiques, prise de décision rapide multidimensionnelle, fusion de données, optimisation comportementale, etc... Il existe plusieurs types de tels réseaux et parmi ceux- ci les réseaux impulsionnels, c'est-à-dire, ceux dont le fonctionnement est calqué sur celui des neurones corticaux. Ce sont ceux qui devraient offrir le meilleur rendement énergétique donc le meilleur passage à
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Han, Bing. "ACCELERATION OF SPIKING NEURAL NETWORK ON GENERAL PURPOSE GRAPHICS PROCESSORS." University of Dayton / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1271368713.

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Books on the topic "Spiking Neural network simulation"

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Skrzypek, Josef, ed. Neural Network Simulation Environments. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2736-7.

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Skrzypek, Josef. Neural Network Simulation Environments. Springer US, 1994.

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Josef, Skrzypek, ed. Neural network simulation environments. Kluwer Academic Publishers, 1994.

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S, Mohan. Artificial neural network modelling. Indian National Committee on Hydrology, 2007.

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Czischek, Stefanie. Neural-Network Simulation of Strongly Correlated Quantum Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52715-0.

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Bui, Tung X. A neural-network based behavioral theory of tank commanders. Naval Postgraduate School, 1992.

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Khataee, A. R. Artificial neural network modeling of water and wastewater treatment processes. Nova Science Publishers, 2010.

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Blizard, M. A neural network simulation running on a TMS 320C40C/IBM PC platform. UMIST, 1993.

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Curram, Stephen. representing intelligent decision making in discrete event simulation: a stochastic neural network approach. typescript, 1997.

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Liu, Jinkun. Radial Basis Function (RBF) Neural Network Control for Mechanical Systems: Design, Analysis and Matlab Simulation. Springer Berlin Heidelberg, 2013.

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Book chapters on the topic "Spiking Neural network simulation"

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Goodman, Dan F. M., and Romain Brette. "Brian Spiking Neural Network Simulator." In Encyclopedia of Computational Neuroscience. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_253.

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Goodman, Dan F. M., and Romain Brette. "Brian Spiking Neural Network Simulator." In Encyclopedia of Computational Neuroscience. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7320-6_253-4.

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Sharp, Thomas, Luis A. Plana, Francesco Galluppi, and Steve Furber. "Event-Driven Simulation of Arbitrary Spiking Neural Networks on SpiNNaker." In Neural Information Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24965-5_48.

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Mouraud, Anthony, and Didier Puzenat. "Simulation of Large Spiking Neural Networks on Distributed Architectures, The “DAMNED” Simulator." In Engineering Applications of Neural Networks. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03969-0_33.

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Ojiugwo, Chukwuka N., Abderazek B. Abdallah, and Christopher Thron. "Simulation of Biological Learning with Spiking Neural Networks." In Studies in Computational Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37830-1_9.

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D’Haene, Michiel, Benjamin Schrauwen, and Dirk Stroobandt. "Accelerating Event Based Simulation for Multi-synapse Spiking Neural Networks." In Artificial Neural Networks – ICANN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840817_79.

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Zhang, Fangzhou, Mingyue Cui, Jiakang Zhang, Yehua Ling, Han Liu, and Kai Huang. "Accelerated Optimization for Simulation of Brain Spiking Neural Network on GPGPUs." In Algorithms and Architectures for Parallel Processing. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0811-6_10.

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Glackin, Brendan, Jim Harkin, Thomas M. McGinnity, and Liam P. Maguire. "A Hardware Accelerated Simulation Environment for Spiking Neural Networks." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00641-8_38.

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Wu, QingXiang, T. Martin McGinnity, Liam Maguire, Rongtai Cai, and Meigui Chen. "Simulation of Visual Attention Using Hierarchical Spiking Neural Networks." In Bio-Inspired Computing and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24553-4_5.

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Jahnke, A., T. Schönauer, U. Roth, K. Mohraz, and H. Klar. "Simulation of spiking neural networks on different hardware platforms." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0020312.

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Conference papers on the topic "Spiking Neural network simulation"

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Garcia A., Omar A., Diego Chavez Arana, Eduardo S. Espinoza, Ignacio Rubio Scola, Luis Rodolfo Garcia Carrillo, and Andrew T. Sornborger. "Spiking Neural Network-Based Control of an Unmanned Aerial System Implemented on a Customized Neural Flight Simulation Environment." In 2024 American Control Conference (ACC). IEEE, 2024. http://dx.doi.org/10.23919/acc60939.2024.10644419.

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Kausar, Rizwana, Fakhreddine Zayer, Vidya Sudevan, Jaime Viegas, and Jorge Dias. "Bio-Inspired Home Localization Using Event-Based Vision and Spiking Neural Networks in Simulated Environment." In 2025 International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR). IEEE, 2025. https://doi.org/10.1109/simpar62925.2025.10979075.

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Bodden, Lennard, Duc Bach Ha, Franziska Schwaiger, Lars Kreuzberg, and Sven Behnke. "Spiking CenterNet: A Distillation-boosted Spiking Neural Network for Object Detection." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650418.

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Xin, Yuan, Zengkun Xie, Yan Yang, Qiang Fu, and Dongqing Wang. "Memristor-Based Spiking Neural Network Implementation Scheme." In 2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2024. http://dx.doi.org/10.1109/itnec60942.2024.10733006.

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Jiang, Xiaoyang, Qiang Zhang, Jingkai Sun, Jiahang Cao, Jingtong Ma, and Renjing Xu. "Fully Spiking Neural Network for Legged Robots." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890793.

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Li, Yang, and Yi Zeng. "Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikes." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/345.

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Spiking neural network (SNN), as a brain-inspired energy-efficient neural network, has attracted the interest of researchers. While the training of spiking neural networks is still an open problem. One effective way is to map the weight of trained ANN to SNN to achieve high reasoning ability. However, the converted spiking neural network often suffers from performance degradation and a considerable time delay. To speed up the inference process and obtain higher accuracy, we theoretically analyze the errors in the conversion process from three perspectives: the differences between IF and ReLU,
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Nguyen, Quang Anh Pham, Philipp Andelfinger, Wentong Cai, and Alois Knoll. "Transitioning Spiking Neural Network Simulators to Heterogeneous Hardware." In SIGSIM-PADS '19: SIGSIM Principles of Advanced Discrete Simulation. ACM, 2019. http://dx.doi.org/10.1145/3316480.3322893.

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Cheung, Kit, Simon R. Schultz, and Philip H. W. Leong. "A parallel spiking neural network simulator." In 2009 International Conference on Field-Programmable Technology (FPT). IEEE, 2009. http://dx.doi.org/10.1109/fpt.2009.5377667.

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Khun, Jiri, Martin Novotny, and Miroslav Skrbek. "High-Performance Spiking Neural Network Simulator." In 2019 8th Mediterranean Conference on Embedded Computing (MECO). IEEE, 2019. http://dx.doi.org/10.1109/meco.2019.8760291.

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Sakellariou, Vasilis, and Vassilis Paliouras. "An FPGA Accelerator for Spiking Neural Network Simulation and Training." In 2021 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2021. http://dx.doi.org/10.1109/iscas51556.2021.9401317.

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Reports on the topic "Spiking Neural network simulation"

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Wickham, K. L. Neural Network Simulation Package from Ohio State University. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/6502468.

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Pasupuleti, Murali Krishna. Neuromorphic Nanotech: 2D Materials for Energy-Efficient Edge Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rr325.

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Abstract The demand for energy-efficient, real-time computing is driving the evolution of neuromorphic computing and edge AI systems. Traditional silicon-based processors struggle with power inefficiencies, memory bottlenecks, and scalability limitations, making them unsuitable for next-generation low-power AI applications. This research report explores how 2D materials, such as graphene, transition metal dichalcogenides (TMDs), black phosphorus, and MXenes, are enabling the development of neuromorphic architectures that mimic biological neural networks for high-speed, ultra-low-power computat
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Aimone, James, Christopher Bennett, Suma Cardwell, Ryan Dellana, and Tianyao Xiao. Mosaic The Best of Both Worlds: Analog devices with Digital Spiking Communication to build a Hybrid Neural Network Accelerator. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1673175.

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Cordes, G. A., S. R. Bryan, R. H. Powell, and D. R. Chick. Neural network setpoint control of an advanced test reactor experiment loop simulation. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/6529674.

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Ntuen, Celestine A., and Robert Li. A Neural Network Model for Human Workload Simulation in Complex Human-Machine System. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada387288.

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Markova, Oksana, Serhiy Semerikov та Maiia Popel. СoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. Sun SITE Central Europe, 2018. http://dx.doi.org/10.31812/0564/2250.

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The role of neural network modeling in the learning сontent of special course “Foundations of Mathematic Informatics” was discussed. The course was developed for the students of technical universities – future IT-specialists and directed to breaking the gap between theoretic computer science and it’s applied applications: software, system and computing engineering. CoCalc was justified as a learning tool of mathematical informatics in general and neural network modeling in particular. The elements of technique of using CoCalc at studying topic “Neural network and pattern recognition” of the sp
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Semerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev, and Arnold Kiv. Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3178.

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The authors of the given article continue the series presented by the 2018 paper “Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot”. This time, they consider mathematical informatics as the basis of higher engineering education fundamentalization. Mathematical informatics deals with smart simulation, information security, long-term data storage and big data management, artificial intelligence systems, etc. The authors suggest studying basic principles of mathematical informatics by applying cloud-oriented means of various levels including those traditio
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Semerikov, Serhiy O., Illia O. Teplytskyi, Yuliia V. Yechkalo, and Arnold E. Kiv. Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. [б. в.], 2018. http://dx.doi.org/10.31812/123456789/2648.

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The article substantiates the necessity to develop training methods of computer simulation of neural networks in the spreadsheet environment. The systematic review of their application to simulating artificial neural networks is performed. The authors distinguish basic approaches to solving the problem of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools of neural network simulation, application of third-party add-ins to spreadsheets, development of macros using the embedded languages of spreadsheets; use of standard spreadsheet ad
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Farhi, Edward, and Hartmut Neven. Classification with Quantum Neural Networks on Near Term Processors. Web of Open Science, 2020. http://dx.doi.org/10.37686/qrl.v1i2.80.

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We introduce a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning. The quantum circuit consists of a sequence of parameter dependent unitary transformations which acts on an input quantum state. For binary classification a single Pauli operator is measured on a designated readout qubit. The measured output is the quantum neural network’s predictor of the binary label of the input state. We show through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets. W
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Middlebrooks, Sam E., John P. Jones, and Patrick H. Henry. The Compass Paradigm for the Systematic Evaluation of U.S. Army Command and Control Systems Using Neural Network and Discrete Event Computer Simulation. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada450646.

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