Hoefler T, Alistarh D, Ben-Nun T, Dryden N, Peste A. Sparsity in deep learning: pruning and growth for efficient inference and training in neural networks. J Mach Learn Res. 2021;22.
Wu H, Judd P, Zhang X, Isaev M, Micikevicius P. Integer quantization for deep learning inference: principles and empirical evaluation. 2020:1–20.
Allen-Zhu Z, Li Y. Towards understanding ensemble, knowledge distillation and self-distillation in deep learning. 2020.
Iandola F, Moskewicz M, Karayev S, Girshick R, Darrell T, Keutzer K, DenseNet. Implement efficient ConvNet descr pyramids. 2014:1–11.
Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size. 2016:1–13.
Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H. MobileNets: efficient convolutional neural networks for mobile vision applications. 2017.
Davies M, Srinivasa N, Lin TH, Chinya G, Cao Y, Choday SH, Dimou G, Joshi P, Imam N, Jain S, et al. Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro. 2018;38:82–99. https://doi.org/10.1109/MM.2018.112130359
Akopyan F, Sawada J, Cassidy A, Alvarez-Icaza R, Arthur J, Merolla P, Imam N, Nakamura Y, Datta P, Nam GJ, et al. TrueNorth: design and tool flow of a 65 MW 1 million neuron programmable neurosynaptic chip. IEEE Trans Comput Des Integr Circuits Syst. 2015;34:1537–57. https://doi.org/10.1109/TCAD.2015.2474396
Painkras E, Plana LA, Garside J, Temple S, Galluppi F, Patterson C, Lester DR, Brown AD, Furber SB, SpiNNaker. A 1-W 18-core system-on-chip for massively-parallel neural network simulation. IEEE J Solid-State Circuits. 2013;48:1943–53. https://doi.org/10.1109/JSSC.2013.2259038
Benjamin BV, Gao P, McQuinn E, Choudhary S, Chandrasekaran AR, Bussat JM, Alvarez-Icaza R, Arthur JV, Merolla PA, Boahen K. Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations. Proc IEEE. 2014;102:699–716. https://doi.org/10.1109/JPROC.2014.2313565
Hodgkin AL, Huxley AF. A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol. 1952;117:500–44. https://doi.org/10.1113/jphysiol.1952.sp004764
Abbott LF. Lapicque’s introduction of the integrate-and-fire model neuron (1907). Brain Res Bull. 1999;50:303–4. https://doi.org/10.1016/S0361-9230(99)00161-6
Izhikevich EM. Which model to use for cortical spiking neurons? IEEE Trans Neural Networks. 2004;15:1063–70. https://doi.org/10.1109/TNN.2004.832719
Eshraghian JK, Ward M, Neftci EO, Wang X, Lenz G, Dwivedi G, Bennamoun M, Jeong DS, Lu WD. Training spiking neural networks using lessons from deep learning. Proc IEEE. 2023;111:1016–54. https://doi.org/10.1109/JPROC.2023.3308088
Zhang M, Gu Z, Zheng N, Ma D, Pan G. Efficient spiking neural networks with logarithmic temporal coding. IEEE Access. 2020;8:98156–67. https://doi.org/10.1109/ACCESS.2020.2994360
Bi G, Poo M. Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci. 1998;18:10464–72. https://doi.org/10.1523/JNEUROSCI.18-24-10464.1998
Song S, Miller KD, Abbott LF. Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nat Neurosci. 2000;3:919–26. https://doi.org/10.1038/78829
Park KS. Humans and electricity: understanding body electricity and applications. 2023.
Zhang Z, Xiao M, Ji T, Jiang Y, Lin T, Zhou X, Lin Z. Efficient and generalizable cross-patient epileptic seizure detection through a spiking neural network. Front Neurosci. 2023;17. https://doi.org/10.3389/fnins.2023.1303564
Burelo K, Ramantani G, Indiveri G, Sarnthein JA. Neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG. Sci Rep. 2022;12. https://doi.org/10.1038/s41598-022-05883-8
Li W, Fang C, Zhu Z, Chen C, Song A. Fractal spiking neural network scheme for EEG-based emotion recognition. IEEE J Transl Eng Heal Med. 2024;12:106–18. https://doi.org/10.1109/JTEHM.2023.3320132
Xu FF, Pan D, Zheng H, Ouyang Y, Jia Z, Zeng HEESCN. A novel spiking neural network method for EEG-based emotion recognition. Comput Methods Programs Biomed. 2024;243. https://doi.org/10.1016/j.cmpb.2023.107927
Xu H, Cao K, Chen H, Abudusalamu A, Wu W, Xue Y. Emotional brain network decoded by biological spiking neural network. Front Neurosci. 2023;17. https://doi.org/10.3389/fnins.2023.1200701
Luo Y, Fu Q, Xie J, Qin Y, Wu G, Liu J, Jiang F, Cao Y, Ding X. EEG-based emotion classification using spiking neural networks. IEEE Access. 2020;8:46007–16. https://doi.org/10.1109/ACCESS.2020.2978163
Kasabov NK, NeuCube. A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data. Neural Netw. 2014;52:62–76. https://doi.org/10.1016/j.neunet.2014.01.006
Cai S, Li P, Li HA, Bio-Inspired. Spiking attentional neural network for attentional selection in the listening brain. IEEE Trans Neural Networks Learn Syst. 2023. https://doi.org/10.1109/TNNLS.2023.3303308
Faghihi F, Cai S, Moustafa AAA, Neuroscience-Inspired. Spiking neural network for EEG-based auditory spatial attention detection. Neural Netw. 2022;152:555–65. https://doi.org/10.1016/j.neunet.2022.05.003
Liao X, Wu Y, Wang Z, Wang D, Zhang HA. Convolutional spiking neural network with adaptive coding for motor imagery classification. Neurocomputing. 2023;549. https://doi.org/10.1016/j.neucom.2023.126470
Gong P, Wang P, Zhou Y, Zhang DA, Spiking Neural. Network with adaptive graph convolution and LSTM for EEG-based brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng. 2023;31:1440–50. https://doi.org/10.1109/TNSRE.2023.3246989
Tan C, Šarlija M, Kasabov N, NeuroSense. Short-term emotion recognition and understanding based on spiking neural network modelling of spatio-temporal EEG patterns. Neurocomputing. 2021;434:137–48. https://doi.org/10.1016/j.neucom.2020.12.098
Wu X, Feng Y, Lou S, Zheng H, Hu B, Hong Z, Tan J. Improving NeuCube spiking neural network for EEG-based pattern recognition using transfer learning. Neurocomputing. 2023;529:222–35. https://doi.org/10.1016/j.neucom.2023.01.087
Kennedy J, Eberhart R. Particle swarm optimization. In Proceedings of the Proceedings of ICNN’95 - International Conference on Neural Networks; IEEE; Vol. 4, pp. 1942–1948.
Yang X-S, Deb SC, Search. Recent advances and applications. Neural Comput Appl. 2014;24:169–74. https://doi.org/10.1007/s00521-013-1367-1
Singanamalla SKR, Lin CT. Spiking neural network for augmenting electroencephalographic data for brain computer interfaces. Front Neurosci. 2021;15. https://doi.org/10.3389/fnins.2021.651762
Virgilio G, Sossa CD, Antelis AJH, Falcón JM. Spiking neural networks applied to the classification of motor tasks in EEG signals. Neural Netw. 2020;122:130–43. https://doi.org/10.1016/j.neunet.2019.09.037
Tran LV, Tran HM, Le TM, Huynh TTM, Tran HT, Dao SVT. Application of machine learning in epileptic seizure detection. Diagnostics. 2022;12:2879. https://doi.org/10.3390/diagnostics12112879
Saeedinia SA, Jahed-Motlagh MR, Tafakhori A, Kasabov N. Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals. Sci Rep. 2021;11. https://doi.org/10.1038/s41598-021-90029-5
Koelstra S, Muhl C, Soleymani M, Jong-Seok L, Yazdani A, Ebrahimi T, Pun T, Nijholt A, Patras I. DEAP: a database for emotion analysis; using physiological signals. IEEE Trans Affect Comput. 2012;3:18–31. https://doi.org/10.1109/T-AFFC.2011.15
Zheng W-L, Liu W, Lu Y, Lu B-L, Cichocki A, EmotionMeter. A multimodal framework for recognizing human emotions. IEEE Trans Cybern. 2019;49:1110–22. https://doi.org/10.1109/TCYB.2018.2797176
Wei-Long Zheng; Bao-Liang Lu investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Trans Auton Ment Dev. 2015;7:162–75, https://doi.org/10.1109/TAMD.2015.2431497
Das N, Francart T. and A.B. Auditory attention detection dataset KULeuven.
Tangermann M, Müller K-R, Aertsen A, Birbaumer N, Braun C, Brunner C, Leeb R, Mehring C, Miller KJ, Müller-Putz GR, et al. Review of the BCI competition IV. Front Neurosci. 2012;6. https://doi.org/10.3389/fnins.2012.00055
Blankertz B, Muller K-R, Curio G, Vaughan TM, Schalk G, Wolpaw JR, Schlogl A, Neuper C, Pfurtscheller G, Hinterberger T, et al. The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials. IEEE Trans Biomed Eng. 2004;51:1044–51. https://doi.org/10.1109/TBME.2004.826692
Soleymani M, Lichtenauer J, Pun T, Pantic MA. Multimodal database for affect recognition and implicit tagging. IEEE Trans Affect Comput. 2012;3:42–55. https://doi.org/10.1109/T-AFFC.2011.25
Schrauwen B, Van Campenhout IBSA. a Fast and Accurate Spike Train Encoding Scheme. In proceedings of the proceedings of the international joint conference on neural networks, 2003.; IEEE; Vol. 4, pp. 2825–2830.
Chu H, Yan Y, Gan L, Jia H, Qian L, Huan Y, Zheng L, Zou Z. A neuromorphic processing system with spike-driven SNN processor for wearable ECG classification. IEEE Trans Biomed Circuits Syst. 2022;16:511–23. https://doi.org/10.1109/TBCAS.2022.3189364
Xing Y, Zhang L, Hou Z, Li X, Shi Y, Yuan Y, Zhang F, Liang S, Li Z, Yan L. Accurate ECG classification based on spiking neural network and attentional mechanism for real-time implementation on personal portable devices. Electron. 2022;11. https://doi.org/10.3390/electronics11121889
Feng Y, Geng S, Chu J, Fu Z, Hong S. Building and training a deep spiking neural network for ECG classification. Biomed Signal Process Control. 2022;77. https://doi.org/10.1016/j.bspc.2022.103749
Jiang J, Tian F, Liang J, Shen Z, Liu Y, Zheng J, Wu H, Zhang Z, Fang C, Zhao Y, et al. MSPAN: a memristive spike-based computing engine with adaptive neuron for edge arrhythmia detection. Front Neurosci. 2021;15. https://doi.org/10.3389/fnins.2021.761127
Yan Z, Zhou J, Wong WF. Energy efficient ECG classification with spiking neural network. Biomed Signal Process Control. 2021;63. https://doi.org/10.1016/j.bspc.2020.102170
Shekhawat D, Chaudhary D, Kumar A, Kalwar A, Mishra N, Sharma D. Binarized spiking neural network optimized with momentum search algorithm for fetal arrhythmia detection and classification from ECG signals. Biomed Signal Process Control. 2024;89. https://doi.org/10.1016/j.bspc.2023.105713
Yin B, Corradi F, Bohté SM. Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks. Nat Mach Intell. 2021;3:905–13. https://doi.org/10.1038/s42256-021-00397-w
Moody GB, Mark RG. The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag. 2001;20:45–50. https://doi.org/10.1109/51.932724
Clifford G, Liu C, Moody B, Lehman L, Silva I, Li Q, Johnson A. Mark, R. AF classification from a short single lead ECG recording: the physionet computing in cardiology challenge 2017.; September 14 2017.
Laguna P, Mark RG, Goldberg A, Moody GB. A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. In Proceedings of the Computers in Cardiology 1997; IEEE; pp. 673–676.
Xu M, Chen X, Sun A, Zhang X, Chen XA. Novel event-driven spiking convolutional neural network for electromyography pattern recognition. IEEE Trans Biomed Eng. 2023;70:2604–15. https://doi.org/10.1109/TBME.2023.3258606
Vitale A, Donati E, Germann R, Magno M. Neuromorphic edge computing for biomedical applications: gesture classification using EMG signals. IEEE Sens J. 2022;22:19490–9. https://doi.org/10.1109/JSEN.2022.3194678
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