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撰写与修订；通信作者：蒋希、尧德中，负责选题 generation of neural network models[J]. Neural Netw,
与总体构思指导、框架及关键内容审核、稿件审阅 1997, 10(9): 1659-1671.
[16] ZHENG T Y, HAN L Y, ZHANG T L. Research
修改与最终定稿。
advances and new paradigms for biology-inspired
spiking neural networks[EB/OL]. arXiv, 2024[2025-08-
参考文献 12]. https://arxiv.org/abs/2408.13996.
[1] AZEVEDO F A C, CARVALHO L R B, GRINBERG L [17] LAGANI G, FALCHI F, GENNARO C, et al. Spiking
T, et al. Equal numbers of neuronal and nonneuronal neural networks and bio-inspired supervised deep
cells make the human brain an isometrically scaled-up learning: a survey[EB/OL]. arXiv, 2023[2025-08-
primate brain[J]. J Comp Neurol, 2009, 513(5): 532-541. 12]. https://arxiv.org/abs/2307.16235.
[2] WILLIAMS R W, HERRUP K. The control of neuron [18] FANG W, YU Z F, CHEN Y Q, et al. Deep residual
number[J]. Annu Rev Neurosci, 1988, 11: 423-453. learning in spiking neural networks[C]//Proceedings of
[3] VILLEGAS P, MORETTI P, MUÑOZ M A. Frustrated the 35th International Conference on Neural Information
hierarchical synchronization and emergent complexity in Processing Systems. Red Hook, NY: Curran Associates,
the human connectome network[J]. Sci Rep, 2014,4: Inc. , 2021: 21056-21069.
5990. [19] LIU F Q, ZHENG H, MA S C, et al. Advancing brain-
[4] POPP J L, THIELE J A, FASKOWITZ J, et al. inspired computing with hybrid neural networks[J]. Natl
Structural-functional brain network coupling during Sci Rev, 2024, 11(5): nwae066.
cognitive demand reveals intelligence-relevant [20] ZHAO R, YANG Z Y, ZHENG H, et al. A framework
communication strategies[J]. Commun Biol, 2025, 8(1): for the general design and computation of hybrid neural
855. networks[J]. Nat Commun, 2022, 13(1): 3427.
[5] EKUNDAYO O S, EZUGWU A E. Deep learning: [21] DARLOW L, REGAN C, RISI S, et al. Continuous
historical overview from inception to actualization, thought machines[EB/OL]. arXiv, preprint, 2025[2025-
models, applications and future trends[J]. Appl Soft 08-03]. https://arxiv.org/abs/2505.05522.
Comput, 2025, 181: 113378. [22] LECUN Y, BENGIO Y, HINTON G. Deep learning[J].
[6] SANCHEZ-LENGELING B, REIF E, PEARCE A, et al. Nature, 2015, 521(7553): 436-444.
A gentle introduction to graph neural networks[J]. [23] KASABOV N. NeuCube: a spiking neural network
Distill, 2021: e33. architecture for mapping, learning and understanding of
[7] ÓLAFSDÓTTIR H F, BUSH D, BARRY C. The role of spatio-temporal brain data[J]. Neural Netw, 2014, 52:
hippocampal replay in memory and planning[J]. Curr 62-76.
Biol, 2018, 28(1): R37-R50. [24] SINDAGI V A, PATEL V M. Generating high-quality
[8] HASSABIS D, KUMARAN D, SUMMERFIELD C, et crowd density maps using contextual pyramid
al. Neuroscience-inspired artificial intelligence[J]. CNNs[C]//2017 IEEE International Conference on
Neuron, 2017, 95(2): 245-258. Computer Vision (ICCV). Piscataway, NJ: IEEE Press,
[9] ZHANG X Z, ZHANG X L, HUANG Q, et al. A review 2017: 1879-1888.
of epilepsy detection and prediction methods based on [25] KUBILIUS J, SCHRIMPF M, KAR K, et al. Brain-like
EEG signal processing and deep learning[J]. Front object recognition with high-performing shallow
Neurosci, 2024, 18: 1468967. recurrent ANNs[C]//Proceedings of the 32nd Advances
[10] ABUALROB M A, ITBAISHA A, MESRAOUA B. in Neural Information Processing Systems. Vancouver,
Unlocking new frontiers in epilepsy through AI: from Canada: Neural Information Processing Systems
seizure prediction to personalized medicine[J]. Epilepsy Foundation, 2019: 12785-12796.
Behav, 2025, 166: 110327. [26] JIANG Y, HE Z B, PENG Z H, et al. Hierarchical graph
[11] PEROVNIK M, RUS T, SCHINDLBECK K A, et al. learning with small-world brain connectomes for
Functional brain networks in the evaluation of patients cognitive prediction[C]//Medical Image Computing and
with neurodegenerative disorders[J]. Nat Rev Neurol, Computer Assisted Intervention - MICCAI 2024.
2023, 19(2): 73-90. Springer Nature Switzerland, 2024: 306-316.
[12] MCCULLOCH W S, PITTS W. A logical calculus of [27] BARABÁSI A L, ALBERT R. Emergence of scaling in
the ideas immanent in nervous activity.1943[J]. Bull random networks[J]. Science, 1999, 286(5439): 509-
Math Biol, 1990, 52(1-2): 99-115. 512.
[13] ROSENBLATT F. The perceptron: a probabilistic model [28] BUCKNER R L, SEPULCRE J, TALUKDAR T, et al.
for information storage and organization in the brain[J]. Cortical hubs revealed by intrinsic functional
Psychol Rev, 1958, 65(6): 386-408. connectivity: mapping, assessment of stability, and
[14] SARKER I H. Deep learning: A comprehensive relation to alzheimer’s disease[J]. J Neurosci, 2009,
overview on techniques, taxonomy, applications and 29(6): 1860-1873.
research directions[J]. SN Comput Sci, 2021, 2(6): 420. [29] VASWANI A, SHAZEER N, PARMAR N, et al.
[15] MAASS W. Networks of spiking neurons: the third Attention is all you need[C]//Proceedings of the 31st

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