[1] CHIALVO D R. Emergent complex neural dynamics[J]. Nature physics, 2010, 6(10): 744-750.
[2] COOMBES S. Large-scale neural dynamics: simple and complex[J]. NeuroImage, 2010, 52(3): 731-739.
[3] MACGREGOR R. Neural and brain modeling[M]. Elsevier, 2012.
[4] DOLL B B, DUNCAN K D, SIMON D A, et al. Model-based choices involve prospectiveneural activity[J]. Nature neuroscience, 2015, 18(5): 767-772.
[5] CISEK P. Integrated neural processes for defining potential actions and deciding between them:a computational model[J]. Journal of Neuroscience, 2006, 26(38): 9761-9770.
[6] TANG H, TAN K C, YI Z. Neural networks: computational models and applications: Vol. 53[M]. Springer Science & Business Media, 2007.
[7] LEE S H, DAN Y. Neuromodulation of brain states[J]. neuron, 2012, 76(1): 209-222.
[8] WANG W, COLLINGER J L, PEREZ M A, et al. Neural interface technology for rehabilitation: exploiting and promoting neuroplasticity[J]. Physical Medicine and Rehabilitation Clinics,2010, 21(1): 157-178.
[9] MILOSEVIC M, MARQUEZ-CHIN C, MASANI K, et al. Why brain-controlled neuroprosthetics matter: mechanisms underlying electrical stimulation of muscles and nerves in rehabilitation[J]. Biomedical engineering online, 2020, 19: 1-30.
[10] PARK S, KOPPES R A, FRORIEP U P, et al. Optogenetic control of nerve growth[J]. Scientificreports, 2015, 5(1): 9669.
[11] YIZHAR O, FENNO L E, DAVIDSON T J, et al. Optogenetics in neural systems[J]. Neuron,2011, 71(1): 9-34.
[12] CHERNOV M, ROE A W. Infrared neural stimulation: a new stimulation tool for centralnervous system applications[J]. Neurophotonics, 2014, 1(1): 011011-011011.
[13] LIEBERMAN J A, SHEITMAN B B, KINON B J. Neurochemical sensitization in the pathophysiology of schizophrenia: deficits and dysfunction in neuronal regulation and plasticity[J].Neuropsychopharmacology, 1997, 17(4): 205-229.
[14] OKUN M S, FOOTE K D. Parkinson’s disease DBS: what, when, who and why? The time hascome to tailor DBS targets[J]. Expert review of neurotherapeutics, 2010, 10(12): 1847-1857.
[15] LOO C K, MITCHELL P B. A review of the efficacy of transcranial magnetic stimulation(TMS) treatment for depression, and current and future strategies to optimize efficacy[J]. Journal of affective disorders, 2005, 88(3): 255-267.
[16] FISHER R S, VELASCO A L. Electrical brain stimulation for epilepsy[J]. Nature ReviewsNeurology, 2014, 10(5): 261-270.
[17] XUE X, WIMMER R D, HALASSA M M, et al. Spiking recurrent neural networks represent task-relevant neural sequences in rule-dependent computation[J]. Cognitive Computation,2023, 15(4): 1167-1189.
[18] POLLOCK E, JAZAYERI M. Engineering recurrent neural networks from task-relevant manifolds and dynamics[J]. PLoS computational biology, 2020, 16(8): e1008128.
[19] GALLEGO J A, PERICH M G, MILLER L E, et al. Neural Manifolds for the Control ofMovement[J]. Neuron, 2017, 94(5): 978-984.
[20] GU S, PASQUALETTI F, CIESLAK M, et al. Controllability of structural brain networks[J].Nature communications, 2015, 6(1): 8414.
[21] LIANG Z, LUO Z, LIU K, et al. Online Learning Koopman Operator for Closed-Loop ElectricalNeurostimulation in Epilepsy[J]. IEEE Journal of Biomedical and Health Informatics, 2022.
[22] CHIAPPALONE M, PASQUALE V, FREGA M. In vitro neuronal networks: From culturingmethods to neuro-technological applications: Vol. 22[M]. Springer, 2019.
[23] FITZHUGH R. Thresholds and plateaus in the Hodgkin-Huxley nerve equations[J]. The Journalof general physiology, 1960, 43(5): 867-896.
[24] CALISSANO P, MATRONE C, AMADORO G. Apoptosis and in vitro Alzheimer’s diseaseneuronal models[J]. Communicative & integrative biology, 2009, 2(2): 163-169.
[25] HENSTRIDGE C M, HYMAN B T, SPIRES-JONES T L. Beyond the neuron–cellular interactions early in Alzheimer disease pathogenesis[J]. Nature Reviews Neuroscience, 2019, 20(2):94-108.
[26] D’SOUZA G X, ROSE S E, KNUPP A, et al. The application of in vitro-derived human neuronsin neurodegenerative disease modeling[J]. Journal of neuroscience research, 2021, 99(1): 124-140.
[27] ZHANG J, YANG H, WU J, et al. Recent progresses in novel in vitro models of primaryneurons: A biomaterial perspective[J]. Frontiers in Bioengineering and Biotechnology, 2022,10: 953031.
[28] WHEELER B C. Building a brain on a chip[C]//2008 30th Annual International Conference ofthe IEEE Engineering in Medicine and Biology Society. IEEE, 2008: 1604-1606.
[29] BROFIGA M, PISANO M, RAITERI R, et al. On the road to the brain-on-a-chip: a review onstrategies, methods, and applications[J]. Journal of Neural Engineering, 2021, 18(4): 041005.
[30] LEE C T, BENDRIEM R M, WU W W, et al. 3D brain Organoids derived from pluripotent stemcells: promising experimental models for brain development and neurodegenerative disorders[J]. Journal of biomedical science, 2017, 24: 1-12.
[31] GLASER J I, WHITEWAY M R, CUNNINGHAM J P, et al. Recurrent Switching DynamicalSystems Models for Multiple Interacting Neural Populations[J]. bioRxiv, 2020.
[32] IZHIKEVICH E M, FITZHUGH R. Fitzhugh-nagumo model[J]. Scholarpedia, 2006, 1(9):1349.
[33] IZHIKEVICH E M. Simple model of spiking neurons[J]. IEEE Transactions on neural networks, 2003, 14(6): 1569-1572.
[34] COOMBES S, BYRNE Á. Next generation neural mass models[M]//Nonlinear dynamics incomputational neuroscience. Springer, 2018: 1-16.
[35] DECO G, JIRSA V K, MCINTOSH A R. Emerging Concepts for the Dynamical Organizationof Resting-State Activity in the Brain[J]. Nature Reviews Neuroscience, 2011, 12(1): 43-56.
[36] WILSON H R, COWAN J D. Excitatory and inhibitory interactions in localized populations ofmodel neurons[J]. Biophysical journal, 1972, 12(1): 1-24.
[37] JANSEN B H, RIT V G. Electroencephalogram and visual evoked potential generation in amathematical model of coupled cortical columns[J]. Biological cybernetics, 1995, 73(4): 357-366.
[38] LARTER R, SPEELMAN B, WORTH R M. A coupled ordinary differential equation latticemodel for the simulation of epileptic seizures[J]. Chaos: An Interdisciplinary Journal of Nonlinear Science, 1999, 9(3): 795-804.
[39] BREAKSPEAR M, TERRY J R, FRISTON K J. Modulation of excitatory synaptic couplingfacilitates synchronization and complex dynamics in a biophysical model of neuronal dynamics[J]. Network: Computation in Neural Systems, 2003, 14(4): 703.
[40] BISHOP C M. Latent variable models[M]//Learning in graphical models. Springer, 1998:371-403.
[41] REISE S P, WALLER N G, COMREY A L. Factor analysis and scale revision.[J]. Psychologicalassessment, 2000, 12(3): 287.
[42] BLEI D M, NG A Y, JORDAN M I. Latent dirichlet allocation[J]. Journal of machine Learningresearch, 2003, 3(Jan): 993-1022.
[43] EDDY S R. Hidden markov models[J]. Current opinion in structural biology, 1996, 6(3):361-365.
[44] REYNOLDS D A, et al. Gaussian mixture models.[J]. Encyclopedia of biometrics, 2009, 741(659-663).
[45] DONG Y, LIU Y, QIN S J. Efficient Dynamic Latent Variable Analysis for High-DimensionalTime Series Data[J]. IEEE Transactions on Industrial Informatics, 2020, 16(6): 4068-4076.
[46] PU Y, GAN Z, HENAO R, et al. Variational autoencoder for deep learning of images, labelsand captions[J]. Advances in neural information processing systems, 2016, 29.
[47] GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial networks[J]. Communications of the ACM, 2020, 63(11): 139-144.
[48] HURWITZ C, KUDRYASHOVA N, ONKEN A, et al. Building population models for largescale neural recordings: Opportunities and pitfalls[J]. Current opinion in neurobiology, 2021,70: 64-73.
[49] SANI O G, ABBASPOURAZAD H, WONG Y T, et al. Modeling behaviorally relevant neuraldynamics enabled by preferential subspace identification[J]. Nature Neuroscience, 2021, 24(1):140-149.
[50] SOLEIMANI G, NITSCHE M A, BERGMANN T O, et al. Closing the loop between brain andelectrical stimulation: towards precision neuromodulation treatments[J]. Translational psychiatry, 2023, 13(1): 279.
[51] SANTHANAM G, YU B M, GILJA V, et al. Factor-analysis methods for higher-performanceneural prostheses[J]. Journal of neurophysiology, 2009, 102(2): 1315-1330.
[52] CHEN Z, BARBIERI R, BROWN E N. State space modeling of neural spike train and behavioral data[M]//Statistical signal processing for neuroscience and neurotechnology. Elsevier,2010: 175-218.
[53] CHEN G, KING J A, BURGESS N, et al. How vision and movement combine in the hippocampal place code[J]. Proceedings of the National Academy of Sciences, 2013, 110(1): 378-383.
[54] CHEN Z. Advanced state space methods for neural and clinical data[M]. Cambridge UniversityPress, 2015.
[55] YU B M, CUNNINGHAM J P, SANTHANAM G, et al. Gaussian-process factor analysisfor low-dimensional single-trial analysis of neural population activity[J]. Advances in neuralinformation processing systems, 2008, 21.
[56] CUNNINGHAM J P, BYRON M Y. Dimensionality reduction for large-scale neural recordings[J]. Nature neuroscience, 2014, 17(11): 1500-1509.
[57] LAWHERN V, WU W, HATSOPOULOS N, et al. Population decoding of motor cortical activityusing a generalized linear model with hidden states[J]. Journal of neuroscience methods, 2010,189(2): 267-280.
[58] CHEN Z, GOMPERTS S N, YAMAMOTO J, et al. Neural representation of spatial topologyin the rodent hippocampus[J]. Neural computation, 2014, 26(1): 1-39.
[59] PENNY W, GHAHRAMANI Z, FRISTON K. Bilinear dynamical systems[J]. PhilosophicalTransactions of the Royal Society B: Biological Sciences, 2005, 360(1457): 983-993.
[60] VOGELSTEIN J T, WATSON B O, PACKER A M, et al. Spike inference from calcium imagingusing sequential Monte Carlo methods[J]. Biophysical journal, 2009, 97(2): 636-655.
[61] WU W, CHEN Z, GAO S, et al. A hierarchical Bayesian approach for learning sparse spatiotemporal decompositions of multichannel EEG[J]. NeuroImage, 2011, 56(4): 1929-1945.
[62] LATIMER K W, YATES J L, MEISTER M L, et al. Single-trial spike trains in parietal cortexreveal discrete steps during decision-making[J]. Science, 2015, 349(6244): 184-187.
[63] WHITEWAY M R, BUTTS D A. Revealing unobserved factors underlying cortical activitywith a rectified latent variable model applied to neural population recordings[J]. Journal ofneurophysiology, 2017.
[64] CHING S, BROWN E N. Modeling the dynamical effects of anesthesia on brain circuits[J].Current opinion in neurobiology, 2014, 25: 116-122.
[65] MCCARTHY M M, CHING S, WHITTINGTON M A, et al. Dynamical changes in neurologicaldiseases and anesthesia[J]. Current opinion in neurobiology, 2012, 22(4): 693-703.
[66] CHING S, PURDON P L, VIJAYAN S, et al. A neurophysiological–metabolic model for burstsuppression[J]. Proceedings of the National Academy of Sciences, 2012, 109(8): 3095-3100.
[67] LIU S, CHING S. Homeostatic dynamics, hysteresis and synchronization in a low-dimensionalmodel of burst suppression[J]. Journal of mathematical biology, 2017, 74: 1011-1035.
[68] KIM M G, KAMIMURA H A, LEE S A, et al. Image-guided focused ultrasound modulateselectrically evoked motor neuronal activity in the mouse peripheral nervous system in vivo[J].Journal of neural engineering, 2020, 17(2): 026026.
[69] ZHONG G, YANG Z, JIANG T. Precise modulation strategies for transcranial magnetic stimulation: advances and future directions[J]. Neuroscience Bulletin, 2021: 1-17.
[70] XU W, WANG J, LI X N, et al. Neuronal and synaptic adaptations underlying the benefits ofdeep brain stimulation for Parkinson’s disease[J]. Translational Neurodegeneration, 2023, 12(1): 55.
[71] ADAIR D, TRUONG D, ESMAEILPOUR Z, et al. Electrical stimulation of cranial nerves incognition and disease[J]. Brain stimulation, 2020, 13(3): 717-750.
[72] MARTÍNEZ S, GARCÍA-VIOLINI D, BELLUSCIO M, et al. Dynamical models in neuroscience from a closed-loop control perspective[J]. IEEE Reviews in Biomedical Engineering,2022, 16: 706-721.
[73] BAKER C, ZHU V, ROSENBAUM R. Nonlinear stimulus representations in neural circuitswith approximate excitatory-inhibitory balance[J]. PLoS computational biology, 2020, 16(9):e1008192.
[74] HENNEQUIN G, VOGELS T P, GERSTNER W. Optimal control of transient dynamics inbalanced networks supports generation of complex movements[J]. Neuron, 2014, 82(6): 1394-1406.
[75] PETRUCCI M N, NEUVILLE R S, AFZAL M F, et al. Neural closed-loop deep brain stimulation for freezing of gait[J]. Brain Stimulation: Basic, Translational, and Clinical Research inNeuromodulation, 2020, 13(5): 1320-1322.
[76] WU Y C, LIAO Y S, YEH W H, et al. Directions of deep brain stimulation for epilepsy andParkinson’s disease[J]. Frontiers in Neuroscience, 2021, 15: 680938.
[77] YEUNG E, KUNDU S, HODAS N. Learning deep neural network representations for Koopman operators of nonlinear dynamical systems[C]//2019 American Control Conference (ACC).IEEE, 2019: 4832-4839.
[78] CHANG S, WEI X, SU F, et al. Model Predictive Control for Seizure Suppression Based onNonlinear Auto-Regressive Moving-Average Volterra Model[J]. IEEE Transactions on NeuralSystems and Rehabilitation Engineering, 2020, 28(10): 2173-2183.
[79] ASHOURVAN A, PEQUITO S D G M, KHAMBHATI A N, et al. Model-based design forseizure control by stimulation[J]. Journal of Neural Engineering, 2020, 17(2).
[80] SCANGOS K W, KHAMBHATI A N, DALY P M, et al. Closed-loop neuromodulation in anindividual with treatment-resistant depression[J]. Nature Medicine, 2021: 1-5.
[81] XI Y G, LI D W, LIN S. Model Predictive Control —Status and Challenges[J]. Acta AutomaticaSinica, 2013, 39(3): 222-236.
[82] KUMAR G, KOTHARE M V, THAKOR N V, et al. Designing closed-loop brain-machineinterfaces using model predictive control[J]. Technologies, 2016, 4(2): 18.
[83] LUSCH B, KUTZ J N, BRUNTON S L. Deep learning for universal linear embeddings ofnonlinear dynamics[J]. Nature communications, 2018, 9(1): 1-10.
[84] CHO K, VAN MERRIËNBOER B, GULCEHRE C, et al. Learning phrase representations usingRNN encoder-decoder for statistical machine translation[A]. 2014.66参考文献
[85] WAGENAAR D A, MADHAVAN R, PINE J, et al. Controlling bursting in cortical cultures withclosed-loop multi-electrode stimulation[J]. Journal of Neuroscience, 2005, 25(3): 680-688.
[86] RUARO M E, BONIFAZI P, TORRE V. Toward the neurocomputer: image processing andpattern recognition with neuronal cultures[J]. IEEE Transactions on Biomedical Engineering,2005, 52(3): 371-383.
[87] PASQUALE V, MARTINOIA S, CHIAPPALONE M. Stimulation triggers endogenous activitypatterns in cultured cortical networks[J]. Scientific reports, 2017, 7(1): 1-16.
[88] GLADKOV A, KOLPAKOV V, PIGAREVA Y, et al. Functional connectivity of neural network in dissociated hippocampal culture grown on microelectrode array[J]. Современныетехнологии в медицине, 2017, 9(2 (eng)): 61-66.
[89] SCARSI F, TESSADORI J, CHIAPPALONE M, et al. Investigating the impact of electricalstimulation temporal distribution on cortical network responses[J]. BMC neuroscience, 2017,18(1): 1-13.
[90] BAKKUM D J, CHAO Z C, POTTER S M. Long-term activity-dependent plasticity of action potential propagation delay and amplitude in cortical networks[J]. PLOS one, 2008, 3(5):e2088.
[91] YANG Y, QIAO S, SANI O G, et al. Modelling and prediction of the dynamic responses of largescale brain networks during direct electrical stimulation[J]. Nature biomedical engineering,2021, 5(4): 324-345.
[92] Products | www.multichannelsystems.com — multichannelsystems.com[EB/OL]. https://www.multichannelsystems.com/products.
[93] LIANG Z, LUO Z, LIU K, et al. Online Learning Koopman Operator for Closed-Loop ElectricalNeurostimulation in Epilepsy[J]. IEEE Journal of Biomedical and Health Informatics, 2022.
[94] WÜLFING J M, KUMAR S S, BOEDECKER J, et al. Adaptive long-term control of biologicalneural networks with deep reinforcement learning[J]. Neurocomputing, 2019, 342: 66-74.
[95] SHENOY K V, KAO J C. Measurement, manipulation and modeling of brain-wide neuralpopulation dynamics[J]. Nature Communications, 2021, 12(1): 1-5.
[96] HE K, ZHANG X, REN S, et al. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification[C]//Proceedings of the IEEE international conference oncomputer vision. 2015: 1026-1034.
[97] LO M C, WIDGE A S. Closed-loop neuromodulation systems: next-generation treatments forpsychiatric illness[J]. International review of psychiatry, 2017, 29(2): 191-204.
[98] NENADIC Z, BURDICK J. Spike detection using the continuous wavelet transform[J/OL].IEEE Transactions on Biomedical Engineering, 2005, 52(1): 74-87. DOI: 10.1109/TBME.2004.839800.
[99] AZAMI H, SANEI S. Three novel spike detection approaches for noisy neuronal data[C/OL]//2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE). 2012:44-49. DOI: 10.1109/ICCKE.2012.6395350.
[100] BUTTERWORTH S. On the theory of filter amplifiers[J]. wireless engineer, 1929.
修改评论