Abstract
Spatio- and spectro-temporal data (SSTD) are the most common types of data collected in many domain areas, including engineering, bioinformatics, neuroinformatics, ecology, environment, medicine, economics, etc. However, there is lack of methods for the efficient analysis of such data and for spatio-temporal pattern recognition (STPR). The brain functions as a spatio-temporal information processing machine and deals extremely well with spatio-temporal data. Its organisation and functions have been the inspiration for the development of new methods for SSTD analysis and STPR. The brain-inspired spiking neural networks (SNN) are considered the third generation of neural networks and are a promising paradigm for the creation of new intelligent ICT for SSTD. This new generation of computational models and systems are potentially capable of modelling complex information processes due to their ability to represent and integrate different information dimensions, such as time, space, frequency, and phase, and to deal with large volumes of data in an adaptive and self-organising manner. The paper reviews methods and systems of SNN for SSTD analysis and STPR, including single neuronal models, evolving spiking neural networks (eSNN) and computational neuro-genetic models (CNGM). Software and hardware implementations and some pilot applications for audio-visual pattern recognition, EEG data analysis, cognitive robotic systems, BCI, neurodegenerative diseases, and others are discussed.
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Acharya, R., Chua, E.C.P., Chua, K.C., Min, L.C., Tamura, T.: Analysis and Automatic Identification of Sleep Stages using Higher Order Spectra. Int. Journal of Neural Systems 20(6), 509–521 (2010)
Arel, I., Rose, D.C., Karnowski, T.P.: Deep Machine Learning: A New Frontier in Artificial Intelligence Research. IEEE Comput. Intelligence Magazine 5(4), 13–18 (2010)
Arel, I., Rose, D., Coop, B.: DeSTIN: A deep learning architecture with application to high-dimensional robust pattern recognition. In: Proc. 2008 AAAI Workshop Biologically Inspired Cognitive Architectures, BICA (2008)
Arel, I., Rose, D., Karnovski, T.: Deep Machine Learning – A New Frontier in Artificial Intelligence Research. IEEE CI Magazine, 13–18 (November 2010)
Barbado, M., Fablet, K., Ronjat, M., De Waard, M.: Gene regulation by voltage-dependent calcium channels. Biochimica et Biophysica Acta 1793, 1096–1104 (2009)
Barker-Collo, S., Feigin, V.L., Parag, V., Lawes, C.M.M., Senior, H.: Auckland Stroke Outcomes Study. Neurology 75(18), 1608–1616 (2010)
Belatreche, A., Maguire, L.P., McGinnity, M.: Advances in Design and Application of Spiking Neural Networks. Soft Comput. 11(3), 239–248 (2006)
Bellas, F., Duro, R.J., Faiña, A., Souto, D.: MDB: Artificial Evolution in a Cognitive Architecture for Real Robots. IEEE Transactions on Autonomous Mental Development 2, 340–354 (2010)
Bengio, Y.: Learning Deep Architectures for AI. Found. Trends. Mach. Learning 2(1), 1–127 (2009)
Benuskova, L., Kasabov, N.: Computational neuro-genetic modelling, 290 pages. Springer, New York (2007)
Berry, M.J., Warland, D.K., Meister, M.: The structure and precision of retinal spiketrains. PNAS 94(10), 5411–5416 (1997)
Bohte, S., Kok, J., LaPoutre, J.: Applications of spiking neural networks. Information Processing Letters 95(6), 519–520 (2005)
Bohte, S.M.: The evidence for neural information processing with precise spike-times: A survey. Natural Computing 3 (2004)
Brader, J., Senn, W., Fusi, S.: Learning real-world stimuli in a neural network with spike-driven synaptic dynamics. Neural Computation 19(11), 2881–2912 (2007)
Brader, J.M., Senn, W., Fusi, S.: Learning Real-World Stimuli in a Neural Net-work with Spike-Driven Synaptic Dynamics. Neural Comput. 19(11), 2881–2912 (2007)
Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J.M., Diesmann, M., Morrison, A., Goodman, P.H., Harris, F.C., Zirpe, M., Natschläger, T., Pecevski, D., Ermentrout, B., Djurfeldt, M., Lansner, A., Rochel, O., Vieville, T., Muller, E., Davison, A.P., Boustani, S.E., Destexhe, A.: Simulation of networks of spiking neurons: a review of tools and strategies. J. Comput. Neurosci. 23, 349–398 (2007)
Buonomano, D., Maass, W.: State-dependent computations: Spatio-temporal processing in cortical networks. Nature Reviews, Neuroscience 10, 113–125 (2009)
De Zeeuw, C.I., Hoebeek, F.E., Bosman, L.W.J., Schonewille, M.: Spati-otemporal firing patterns in the cerebellum. Nature Reviews Neurosc. 12, 327–344 (2011)
Shortall, C.R., Moore, A., Smith, E., Hall, M.J., Woiwod, I.P., Harrington, R.: Long-term changes in the abundance of flying insects. Insect Conservation and Diversity 2(4), 251–260 (2009)
Cowburn, P.J., Cleland, J.G.F., Coats, A.J.S., Komajda, M.: Risk stratifica-tion in chronic heart failure. Eur. Heart J. 19, 696–710 (1996)
Craig, D.A., Nguyen, H.T.: Adaptive EEG Thought Pattern Classifier for Advanced Wheelchair Control, Engin. In: Medicine and Biology Society, EMBS 2007, pp. 2544–2547 (2007)
Defoin-Platel, M., Schliebs, S., Kasabov, N.: Quantum-inspired Evolutionary Algorithm: A multi-model EDA. IEEE Trans. Evolutionary Computation 13(6), 1218–1232 (2009)
Delbruck, T.: jAER open source project (2007), http://jaer.wiki.sourceforge.net
Douglas, R., Mahowald, M.: Silicon Neurons. In: Arbib, M. (ed.) The Handbook of Brain Theory and Neural Networks, pp. 282–289. MIT Press (1995)
Dhoble, K., Nuntalid, N., Indivery, G., Kasabov, N.: Online Spatio-Temporal Pattern Recognition with Evolving Spiking Neural Networks utilising Address Event Representation, Rank Order, and Temporal Spike Learning. In: Proc. IJCNN 2012, Brisbane. IEEE (June 2012)
Ferreira, A., Almeida, C., Georgieva, P., Tomé, A., Silva, F.: Advances in EEG-based Biometry. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010, Part II. LNCS, vol. 6112, pp. 287–295. Springer, Heidelberg (2010)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Annals of Eugenics 7, 179–188 (1936)
Florian, R.V.: The chronotron: a neuron that learns to fire temporally-precise spike patterns (2010)
Furber, S., Temple, S.: Neural systems engineering, Interface. J.of the Royal Society 4, 193–206 (2007)
Fusi, S., Annunziato, M., Badoni, D., Salamon, A., Amit, D.: Spike-driven synaptic plasticity: theory, simulation, VLSI implementation. Neural Computation 12(10), 2227–2258 (2000)
Gene and Disease (2005), NCBI, http://www.ncbi.nlm.nih.gov
Gerstner, W.: Time structure of the activity of neural network models. Phys. Rev 51, 738–758 (1995)
Gerstner, W.: What’s different with spiking neurons? In: Mastebroek, H., Vos, H. (eds.) Plausible Neural Networks for Biological Modelling, pp. 23–48. Kluwer Academic Publs. (2001)
Gerstner, W., Kreiter, A.K., Markram, H., Herz, A.V.M.: Neural codes: firing rates and beyond. Proc. Natl. Acad. Sci. USA 94(24), 12740–12741 (1997)
Ghosh-Dastidar, S., Adeli, H.: A New Supervised Learning Algorithm for Multiple Spiking Neural Networks with Application in Epilepsy and Seizure Detection. Neural Networks 22(10), 1419–1431 (2009)
Ghosh-Dastidar, S., Adeli, H.: Improved Spiking Neural Networks for EEG Classification and Epilepsy and Seizure Detection. Integrated Computer-Aided Engineering 14(3), 187–212 (2007)
Goodman, E., Ventura, D.: Spatiotemporal pattern recognition via liquid state ma-chines. In: International Joint Conference on Neural Networks, IJCNN 2006, Vancouver, BC, pp. 3848–3853 (2006)
Gutig, R., Sompolinsky, H.: The tempotron: a neuron that learns spike timing-based decisions. Nat. Neurosci. 9(3), 420–428 (2006)
Hebb, D.: The Organization of Behavior. John Wiley and Sons, New York (1949)
Henley, J.M., Barker, E.A., Glebov, O.O.: Routes, destinations and delays: recent ad-vances in AMPA receptor trafficking. Trends in Neurosc. 34(5), 258–268 (2011)
Hodgkin, A.L., Huxley, A.F.: A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology 117, 500–544 (1952)
Hopfield, J.: Pattern recognition computation using action potential timing for stimulus representation. Nature 376, 33–36 (1995)
Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. PNAS USA 79, 2554–2558 (1982)
Hugo, G.E., Ines, S.: Time and category information in pattern-based codes. Frontiers in Computational Neuroscience 4(0) (2010)
Huguenard, J.R.: Reliability of axonal propagation: The spike doesn’t stop here. PNAS 97(17), 9349–9350 (2000)
Iglesias, J., Villa, A.E.P.: Emergence of Preferred Firing Sequences in Large Spiking Neural Networks During Simulated Neuronal Development. Int. Journal of Neural Systems 18(4), 267–277 (2008)
Indiveri, G., Linares-Barranco, B., Hamilton, T., Van Schaik, A., Etienne-Cummings, R., Delbruck, T., Liu, S., Dudek, P., Häfliger, P., Renaud, S., et al.: Neuromorphic silicon neuron circuits. Frontiers in Neuroscience 5 (2011)
Indiveri, G., Chicca, E., Douglas, R.J.: Artificial cognitive systems: From VLSI networks of spiking neurons to neuromorphic cognition. Cognitive Computation 1(2), 119–127 (2009)
Indiveri, G., Stefanini, F., Chicca, E.: Spike-based learning with a generalized inte-grate and fire silicon neuron. In: 2010 IEEE Int. Symp. Circuits and Syst (ISCAS 2010), Paris, May 30-June 02, pp. 1951–1954 (2010)
Indiviery, G., Horiuchi, T.: Frontiers in Neuromorphic Engineering. Frontiers in Neuroscience 5, 118 (2011)
Isa, T., Fetz, E.E., Muller, K.: Recent advances in brain-machine interfaces. Neural Networks, Brain-Machine Interface 22(9), 1201–1202 (2009)
Izhikevich, E.: Simple model of spiking neurons. IEEE Trans. on Neural Networks 14(6), 1569–1572 (2003)
Izhikevich, E.M.: Which model to use for cortical spiking neurons? IEEE TNN 15(5), 1063–1070 (2004)
Izhikevich, E.M., Edelman, G.M.: Large-Scale Model of Mammalian Thalamocortical Systems. PNAS 105, 3593–3598 (2008)
Izhikevich, E.: Polychronization: Computation with Spikes. Neural Computation 18, 245–282 (2006)
Johnston, S.P., Prasad, G., Maguire, L., McGinnity, T.M.: FPGA Hard-ware/software co-design methodology - towards evolvable spiking networks for robotics application. Int. J. Neural Systems 20(6), 447–461 (2010)
Kasabov, N.: Foundations of Neural Networks. In: Fuzzy Systems and Knowledge Engineering, p. 550. MIT Press, Cambridge (1996)
Kasabov, N., Hu, Y.: Integrated optimisation method for personalised modelling and case study applications. Int. Journal of Functional Informatics and Personalised Medicine 3(3), 236–256 (2010)
Kasabov, N.: Data Analysis and Predictive Systems and Related Methodologies – Person-alised Trait Modelling System. PCT/NZ2009/000222, NZ Patent
Kasabov, N., Dhoble, K., Nuntalid, N., Mohemmed, A.: Evolving Probabilistic Spiking Neural Networks for Spatio-temporal Pattern Recognition: A Preliminary Study on Moving Object Recognition. In: Lu, B.-L., Zhang, L., Kwok, J. (eds.) ICONIP 2011, Part III. LNCS, vol. 7064, pp. 230–239. Springer, Heidelberg (2011)
Kasabov, N., Evolving connectionist systems: The knowledge engineering approach. Springer (2003, 2007)
Kasabov, N.: Global, local and personalised modelling and profile discovery in Bioinformatics: An integrated approach. Pattern Recogn. Letters 28(6), 673–685 (2007)
Kasabov, N., Benuskova, L., Wysoski, S.: A Computational Neurogenetic Model of a Spiking Neuron. In: Proc. IJCNN 2005 Conf., vol. 1, pp. 446–451. IEEE Press (2005)
Kasabov, N., Schliebs, R., Kojima, H.: Probabilistic Computational Neurogenetic Framework: From Modelling Cognitive Systems to Alzheimer’s Disease. IEEE Trans. Autonomous Mental Development 3(4), 1–12 (2011)
Kasabov, N., Schliebs, S., Mohemmed, A.: Modelling the Effect of Genes on the Dynamics of Probabilistic Spiking Neural Networks for Computational Neurogenetic Modelling. In: Proc. 6th meeting on Computational Intelligence for Bioinformatics and Biostatistics, CIBB 2011, Gargangio, Italy, June 30-July 2. LNCS (LNBI). Springer (2011)
Kasabov, N.: To spike or not to spike: A probabilistic spiking neuron model. Neural Netw. 23(1), 16–19 (2010)
Kilpatrick, Z.P., Bresloff, P.C.: Effect of synaptic depression and adaptation on spatio-temporal dynamics of an excitatory neural networks. Physica D 239, 547–560 (2010)
Kistler, G., Gerstner, W.: Spiking Neuron Models - Single Neurons, Populations, Plasticity. Cambridge Univ. Press (2002)
Legenstein, R., Naeger, C., Maass, W.: What Can a Neuron Learn with Spike-Timing-Dependent Plasticity? Neural Computation 17(11), 2337–2382 (2005)
Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain–computer interfaces. J. Neural Eng. 4(2), R1–R15 (2007)
Maass, W., Markram, H.: Synapses as dynamic memory buffers. Neural Network 15(2), 155–161 (2002)
Maass, W., Zador, A.M.: Computing and learning with dynamic synapses. In: Pulsed Neural Networks, pp. 321–336. MIT Press (1999)
Maass, W., Natschlaeger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14(11), 2531–2560 (2002)
Meng, Y., Y. Jin, J. Yin, and M. Conforth (2010) Human activity detection using spiking neural networks regulated by a gene regulatory network. Proc. Int. Joint Conf. on Neural Net-works (IJCNN), IEEE Press, pp.2232-2237, Barcelona, July 2010.
Mohemmed, A., Matsuda, S., Schliebs, S., Dhoble, K., Kasabov, N.: Optimization of Spiking Neural Networks with Dynamic Synapses for Spike Sequence Generation using PSO. In: Proc. Int. Joint Conf. Neural Networks, California, USA, pp. 2969–2974. IEEE Press (2011)
Mohemmed, A., Schliebs, S., Matsuda, S., Kasabov, N.: Evolving Spike Pattern Association Neurons and Neural Networks. Neurocomputing (in print)
Mohemmed, A., Schliebs, S., Matsuda, S., Kasabov, N.: SPAN: Spike Pattern Association Neuron for Learning Spatio-Temporal Sequences. International Journal of Neural Systems (in print, 2012)
Natschläger, T., Maass, W.: Spiking neurons and the induction of finite state machines. Theoretical Computer Science - Natural Computing 287(1), 251–265 (2002)
NeMo spiking neural network simulator, http://www.doc.ic.ac.uk/~akf/nemo/index.html
Nichols, E., McDaid, L.J., Siddique, N.H.: Case Study on Self-organizing Spiking Neural Networks for Robot Navigation. International Journal of Neural Systems 20(6), 501–508 (2010)
Norton, D., Ventura, D.: Improving liquid state machines through iterative refinement of the reservoir. Neurocomputing 73, 2893–2904 (2010)
Nuzlu, H., Kasabov, N., Shamsuddin, S., Widiputra, H., Dhoble: An Extended Evolving Spiking Neural Network Model for Spatio-Temporal Pattern Classification. In: Proc. IJCNN, California, USA, pp. 2653–2656. IEEE Press (2011)
Nuzly, H., Kasabov, N., Shamsuddin, S.: Probabilistic Evolving Spiking Neural Network Optimization Using Dynamic Quantum Inspired Particle Swarm Optimization. In: ICONIP 2010, Part I. LNCS, vol. 6443 (2010)
Ozawa, S., Pang, S., Kasabov, N.: Incremental Learning of Chunk Data for On-line Pattern Classification Systems. IEEE Trans. Neural Networks 19(6), 1061–1074 (2008)
Pang, S., Ozawa, S., Kasabov, N.: Incremental Linear Discriminant Analysis for Classification of Data Streams. IEEE Trans. SMC-B 35(5), 905–914 (2005)
Pfurtscheller, G., Leeb, R., Keinrath, C., Friedman, D., Neuper, C., Guger, C., Slater, M.: Walking from thought. Brain Research 1071(1), 145–152 (2006)
Ponulak, F., Kasinski, A.: Supervised learning in spiking neural networks with ReSuMe: sequence learning, classification, and spike shifting. Neural Computation 22(2), 467–510 (2010)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–285 (1989)
Rast, A.D., Jin, X., Galluppi, F., Plana, L.A., Patterson, C., Furber, S.: Scalable Event-Driven Native Parallel Processing: The SpiNNaker Neuromimetic System. In: Proc. of the ACM International Conference on Computing Frontiers, Bertinoro, Italy, May 17-19, pp. 21–29 (2010) ISBN 978-1-4503-0044-5
Reinagel, P., Reid, R.C.: Precise firing events are conserved across neurons. Journal of Neuroscience 22(16), 6837–6841 (2002)
Reinagel, R., Reid, R.C.: Temporal coding of visual information in the thalamus. Journal of Neuroscience 20(14), 5392–5400 (2000)
Riesenhuber, M., Poggio, T.: Hierarchical Model of Object Recognition in Cortex. Nature Neuroscience 2, 1019–1025 (1999)
Rokem, A., Watzl, S., Gollisch, T., Stemmler, M., Herz, A.V., Samengo, I.: Spike-timing precision underlies the coding efficiency of auditory receptor neurons. J. Neurophysiol. (2005)
Schliebs, R.: Basal forebrain cholinergic dysfunction in Alzheimer´s disease – interrelationship with β-amyloid, inflammation and neurotrophin signaling. Neurochemical Research 30, 895–908 (2005)
Schliebs, S., Hamed, H.N.A., Kasabov, N.: Reservoir-Based Evolving Spiking Neural Network for Spatio-temporal Pattern Recognition. In: Lu, B.-L., Zhang, L., Kwok, J. (eds.) ICONIP 2011, Part II. LNCS, vol. 7063, pp. 160–168. Springer, Heidelberg (2011)
Schliebs, S., Kasabov, N., Defoin-Platel, M.: On the Probabilistic Optimization of Spiking Neural Networks. International Journal of Neural Systems 20(6), 481–500 (2010)
Schliebs, S., Defoin-Platel, M., Worner, S., Kasabov, N.: Integrated Feature and Parameter Optimization for Evolving Spiking Neural Netw.: Exploring Heterogeneous Probabilistic Model. Neural Netw. 22, 623–632 (2009)
Schliebs, S., Mohemmed, A., Kasabov, N.: Are Probabilistic Spiking Neural Networks Suitable for Reservoir Computing? In: Int. Joint Conf. Neural Networks, IJCNN, San Jose, pp. 3156–3163. IEEE Press (2011)
Schliebs, S., Nuntalid, N., Kasabov, N.: Towards Spatio-Temporal Pattern Recognition Using Evolving Spiking Neural Networks. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds.) ICONIP 2010, Part I. LNCS, vol. 6443, pp. 163–170. Springer, Heidelberg (2010)
Schrauwen, B., Van Campenhout, J.: BSA, a fast and accurate spike train encoding scheme. In: Proceedings of the International Joint Conference on Neural Networks, vol. 4, pp. 2825–2830. IEEE (2003)
Soltic, S., Kasabov, N.: Knowledge extraction from evolving spiking neural networks with rank order population coding. International Journal of Neural Systems 20(6), 437–445 (2010)
Sona, D., Veeramachaneni, H., Olivetti, E., Avesani, P.: Inferring cognition from fMRI brain images. In: Proc. of IJCNN. IEEE Press (2011)
Song, S., Miller, K., Abbott, L., et al.: Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience 3, 919–926 (2000)
Theunissen, F., Miller, J.P.: Temporal encoding in nervous systems: a rigorous definition. Journal of Computational Neuroscience 2(2), 149–162 (1995)
Thorpe, S., Gautrais, J.: Rank order coding. Computational Neuroscience: Trends in Research 13, 113–119 (1998)
Thorpe, S., Delorme, A., et al.: Spike-based strategies for rapid processing. Neural Netw. 14(6-7), 715–725 (2001)
van Schaik, A., Shih-Chii Liu, L.: AER EAR: a matched silicon cochlea pair with address event representation interface. In: Proc. of ISCAS - IEEE Int. Symp. Circuits and Systems, May 23-26, vol. 5, pp. 4213–4216 (2005)
Verstraeten, D., Schrauwen, B., D’Haene, M., Stroobandt, D.: An experimental unification of reservoir computing methods. Neural Networks 20(3), 391–403 (2007)
Villa, A.E.P., et al.: Cross-channel coupling of neuronal activity in parvalbumin-deficient mice susceptible to epileptic seizures. Epilepsia 46(suppl. 6), 359 (2005)
Wang, X., Hou, Z.G., Zou, A., Tan, M., Cheng, L.: A behavior controller for mobile robot based on spiking neural networks. Neurocomputing 71(4-6), 655–666 (2008)
Watts, M.: A Decade of Kasabov’s Evolving Connectionist Systems: A Review. IEEE Trans. Systems, Man and Cybernetics- Part C: Appl. and Reviews 39(3), 253–269 (2009)
Weston, I., Ratle, F., Collobert, R.: Deep learning via semi-supervised embedding. In: Proc. 25th Int. Conf. Machine Learning, pp. 1168–1175 (2008)
Widiputra, H., Pears, R., Kasabov, N.: Multiple Time-Series Prediction through Multiple Time-Series Relationships Profiling and Clustered Recurring Trends. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part II. LNCS, vol. 6635, pp. 161–172. Springer, Heidelberg (2011)
Widrow, B., Lehr, M.: 30 years of adaptive neural networks: perceptron, madaline, and backpropagation. Proceedings of the IEEE 78(9), 1415–1442 (1990)
Wysoski, S., Benuskova, L., Kasabov, N.: Evolving spiking neural networks for audiovisual information processing. Neural Networks 23(7), 819–835 (2010)
Jin, X., Lujan, M., Plana, L.A., Davies, S., Temple, S., Furber, S.: Modelling Spiking Neural Networks on SpiNNaker. Computing in Science & Engineering 12(5), 91–97 (2010) ISSN 1521-961
Yu, Y.C., et al.: Specific synapses develop preferentially among sister excitatory neurons in the neocortex. Nature 458, 501–504 (2009)
Zhdanov, V.P.: Kinetic models of gene expression including non-coding RNAs. Phys. Reports 500, 1–42 (2011)
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Kasabov, N. (2012). Evolving Spiking Neural Networks and Neurogenetic Systems for Spatio- and Spectro-Temporal Data Modelling and Pattern Recognition. In: Liu, J., Alippi, C., Bouchon-Meunier, B., Greenwood, G.W., Abbass, H.A. (eds) Advances in Computational Intelligence. WCCI 2012. Lecture Notes in Computer Science, vol 7311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30687-7_12
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