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Chinese Journal of Medical Instrumentation                                         2026年 第50卷 第2期

                                                    综     合     评    述



                   Conference on Neural Information Processing Systems.  [41]   YU X W, ZHANG L, DAI H X, et al. Core-periphery
                   Long Beach, CA, USA: Curran Associates, Inc. , 2017:  principle  guided  redesign  of  self-attention  in  trans-
                   5998-6008.                                        formers[EB/OL].  arXiv  preprint,  2023[2025-11-04].
              [30]   DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al.    https://arxiv.org/abs/2303.15569.
                   An  image  is  worth  16×16  words:  Transformers  for  [42]   RAVICHANDRAN N B, LANSNER A, HERMAN P.
                   imagerecognition  at  scale[C]//International  Conference  Brain-like  combination  of  feedforward  and  recurrent
                   on  Learning  Representations.  Addis  Ababa,  Ethiopia:  network  components  achieves  prototype  extraction  and
                   ICLR Press, 2020.                                 robust pattern recognition[C]//Proceedings of the Japan
              [31]   FAN W Q, DING Y J, NING L B, et al. A survey on  Neuroscience  Society  Annual  Meeting.  Tokyo,  Japan:
                   RAG  meeting  LLMs:  Towards  retrieval-augmented  The Japan Neuroscience Society, 2022: 488-501.
                   large  language  models[C]//Proceedings  of  the  30th  [43]   SOO  W  W  M,  BATTISTA  A,  RADMARD  P,  et  al.
                   ACM  SIGKDD  Conference  on  Knowledge  Discovery  Recurrent  neural  network  dynamical  systems  for
                   and Data Mining. New York, NY, USA: Association for  biological   vision[C]//Proceedings   of   the   38th
                   Computing Machinery, 2024: 6491-6501.             Conference on Neural Information Processing Systems.
              [32]   WOO S, PARK J, LEE J Y, et al. CBAM: Convolutional  Vancouver, BC, Canada: Neural Information Processing
                   block  attention  module[C]//FERRARI  V,  HEBERT  M,  Systems Foundation, 2024: 135966-135982.
                   SMINCHISESCU  C,  et  al.  Computer  Vision  –  ECCV  [44]   PAN W X, ZHAO F F, ZHAO Z Y, et al. Brain-inspired
                   2018. Cham: Springer International Publishing, 2018: 3-  evolutionary   architectures   for   spiking   neural
                   19.                                               networks[J]. IEEE Trans Artif Intell, 2024, 5(11): 5760-
              [33]   SUKHBAATAR S, SZLAM A, WESTON J, et al. End-    5770.
                   to-end  memory  networks[C]//Proceedings  of  the  29th  [45]   LI  Y,  MATEOS  G,  ZHANG  Z  W.  Learning  to  model
                   International  Conference  on  Neural  Information  the relationship between brain structural and functional
                   Processing   Systems.   Montréal,   Canada:   Neural  connectomes[J].  IEEE  Trans  Signal  Inf  Process  Netw,
                   Information  Processing  Systems  Foundation,  2015:  2022, 8: 830-843.
                   2440-2448.                                   [46]   NIE W Z, REN M J, LIU A A, et al. M-GCN: Multi-
              [34]   WU  C  Y,  LI  Y  H,  MANGALAM  K,  et  al.  MeMViT:  branch graph convolution network for 2D image-based
                   Memory-augmented  multiscale  vision  transformer  for  on 3D model retrieval[J]. IEEE Trans Multimed, 2021,
                   efficient long-term video recognition[C]//Proceedings of  23: 1962-1976.
                   the  2022  IEEE/CVF  Conference  on  Computer  Vision  [47]   SABOUR  S,  FROSST  N,  HINTON  G  E.  Dynamic
                   and  Pattern  Recognition  (CVPR).  New  Orleans,  LA,  routing  between  capsules[C]//Proceedings  of  the  30th
                   USA: IEEE, 2022: 13577-13587.                     Advances   in   Neural   Information   Processing
              [35]   VAN DE VEN G M, SIEGELMANN H T, TOLIAS A        Systems(NeurIPS  2017).  Red  Hook,  NY:  Curran
                   S.  Brain-inspired  replay  for  continual  learning  with  Associates, Inc., 2017: 3856-3866.
                   artificial neural networks[J]. Nat Commun, 2020, 11(1):  [48]   LOTTER  W,  KREIMAN  G,  COX  D.  Deep  predictive
                   4069.                                             coding networks for video prediction and unsupervised
              [36]   HE  X,  ZHAO  D  C,  LI  Y,  et  al.  Incorporating  brain-  learning[EB/OL].  arXiv  preprint,  2017[2025-08-03].
                   inspired  mechanisms  for  multimodal  learning  in  https://arxiv.org/abs/1605.08104.
                   artificial intelligence[EB/OL]. arXiv, 2025[2025-08-03].  [49]   HAN  K,  WEN  H,  ZHANG  Y,  et  al.  Deep  predictive
                   https://arxiv.org/abs/2505.10176.                 coding  network  with  local  recurrent  processing  for
              [37]   PARSAPOOR  M.  An  introduction  to  brain  emotional  object recognition[C]//Proceedings of the 31st Advances
                   learning inspired models (BELiMs) with an example of  in Neural Information Processing Systems. Long Beach,
                   BELiMs’ applications[J]. Artif Intell Rev, 2019, 52(1):  CA,  USA:  Neural  Information  Processing  Systems
                   409-439.                                          Foundation, 2018: 9221-9233.
              [38]   HUANG  H,  ZHAO  L,  DAI  H  X,  et  al.  BI-AVAN:  a  [50]   HAFNER D, IRPAN A, DAVIDSON J, et al. Learning
                   brain-inspired  adversarial  visual  attention  network  for  hierarchical  information  flow  with  recurrent  neural
                   characterizing  human  visual  attention  from  neural  modules[C]//  Proceedings  of  the  30th  Advances  in
                   activity[J].  IEEE  Trans  Multimed,  2024,  26:  11191-  Neural  Information  Processing  Systems.  Long  Beach,
                   11203.                                            CA,  USA:  Neural  Information  Processing  Systems
              [39]   SONG Z R, XU Y H, HE Z Z, et al. CP-ViT: Cascade  Foundation, 2017: 6724-6733.
                   vision  transformer  pruning  via  progressive  sparsity  [51]   WANG  H  E,  TRIEBKORN  P,  BREYTON  M,  et  al.
                   Prediction[EB/OL].  arXiv,  2022[2025-08-03]. https://  Virtual brain twins: from basic neuroscience to clinical
                   arxiv.org/abs/2203.04570.                         use[J]. Natl Sci Rev, 2024, 11(5): nwae079.
              [40]   JIANG  X,  ZHANG  T,  ZHANG  S,  et  al.  Fundamental  [52]   HASANI  R,  LECHNER  M,  AMINI  A,  et  al.  Liquid
                   functional  differences  between  gyri  and  sulci:  time-constant  networks[C]//Proceedings  of  the  AAAI
                   implications  for  brain  function,  cognition,  and  Conference  on  Artificial  Intelligence.  Vol  35.
                   behavior[J]. Psychoradiol, 2021, 1(1): 23-41.     Association  for  the  Advancement  of  Artificial


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