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An approach to variable-order prediction via multiple distal dendrites of neurons

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Abstract

In this paper, we proposed an extended version of binary code selection algorithm (BCSA) for the variable-order prediction by introducing multiple distal dendrites into BCSA. The proposed model of artificial neurons has a single proximal dendrite to receive the feed-forward inputs (sequences) from the world and multiple distal dendrites to receive the horizontal inputs from nearby neurons. During training, each distal dendrite is able to remember the states of neurons activated at different time and store the temporal correlations. After training, each distal dendrite independently recalls the temporal correlations contained in sequences and makes a local prediction. The variable-order prediction can be obtained by combining these local predictions made by multiple distal dendrites. Experiments show that the proposed method outperforms BCSA and other methods, such as back-propagation networks and radial basis function networks, especially while processing complex sequences.

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References

  1. Hawkins J, Blakeslee S (2007) On intelligence. Macmillan, London

    Google Scholar 

  2. Mountcastle VB (1997) The columnar organization of the neocortex. Brain 120(4):701–722

    Article  Google Scholar 

  3. Stuart G, Spruston N, Häusse M (2008) Dendrites. Oxford University Press, Oxford

    Google Scholar 

  4. Rall W (1967) Distinguishing theoretical synaptic potentials computed for different soma-dendritic distributions of synaptic input. J Neurophysiol 30(5):1138–1168

    Article  Google Scholar 

  5. Rall W, Burke R, Smith T, Nelson PG, Frank K (1967) Dendritic location of synapses and possible mechanisms for the monosynaptic epsp in motoneurons. J Neurophysiol 30(5):884–915

    Google Scholar 

  6. Rall W, Shepherd GM (1968) Theoretical reconstruction of field potentials and dendrodendritic synaptic interactions in olfactory bulb. J Neurophysiol 31(6):884–915

    Article  Google Scholar 

  7. Segev I (2006) What do dendrites and their synapses tell the neuron? J Neurophysiol 95(3):1295–1297

    Article  Google Scholar 

  8. Hoekstra J, Rouw E (2000) Modeling of dendritic computation: the single dendrite. In: Computing anticipatory systems: CASYS’99-third international conference, vol 517. AIP Publishing, pp 308–322

  9. Tang Z, Tamura H, Kuratu M, Ishizuka O, Tanno K (2001) A model of the neuron based on dendrite mechanisms. Electron Commun Jpn (Part III: Fundam Electron Sci) 84(8):11-24

    Article  Google Scholar 

  10. Liu G (2004) Local structural balance and functional interaction of excitatory and inhibitory synapses in hippocampal dendrites. Nat Neurosci 7(4):373–379

    Article  Google Scholar 

  11. Gasparini S, Magee JC (2006) State-dependent dendritic computation in hippocampal ca1 pyramidal neurons. J Neurosci 26(7):2088–2100

    Article  Google Scholar 

  12. Spruston N (2008) Pyramidal neurons: dendritic structure and synaptic integration. Nat Rev Neurosci 9(3):206–221

    Article  MathSciNet  Google Scholar 

  13. Knoblauch A (2009) Structural plasticity, cortical memory, and the spacing effect. BMC Neurosci 10(Suppl1):O16

    Article  MathSciNet  Google Scholar 

  14. Blasio Gd, Moreno Díaz A, Moreno Díaz R (2011) Dendritic-like reliable computation in artificial neurons. In: Actas de la 13th international conference on computer aided systems theory, EUROCAST 2011. Instituto Universitario de Ciencias y Tecnologías Cibernéticas, pp 66–68

  15. Sha Z, Hu L (2012) The algorithm improvement of the neuron model based on dendrites mechanism. Int J Comput Sci Netw Secur 12(10):1–5

    Google Scholar 

  16. Gollo LL, Kinouchi O, Copelli M (2013) Single-neuron criticality optimizes analog dendritic computation. Sci Rep 3(11):3222–3222

    Article  Google Scholar 

  17. George S, Hasler J, Koziol S, Nease S, Ramakrishnan S (2013) Low power dendritic computation for wordspotting. J Low Power Electron Appl 3(2):73–98

    Article  Google Scholar 

  18. Butz M, van Ooyen A (2013) A simple rule for dendritic spine and axonal bouton formation can account for cortical reorganization after focal retinal lesions. PLoS Comput Biol 9(10):e1003259

    Article  Google Scholar 

  19. Chen X, Sneyd J (2014) A computational model of the dendron of the gnrh neuron. Bull Math Biol 77(6):1–23

    MathSciNet  MATH  Google Scholar 

  20. Montegranario H, Espinosa J (2014) Radial basis functions. In: Variational regularization of 3D data. Springer, New York, pp 69–81

  21. Balabin RM, Lomakina EI (2011) Support vector machine regression (SVR/LS-SVM) an alternative to neural networks (ANN) for analytical chemistry? comparison of nonlinear methods on near infrared (NIR) spectroscopy data. Analyst 136(8):1703–1712

    Article  Google Scholar 

  22. Sato T, Uchida G, Tanifuji M (2009) Cortical columnar organization is reconsidered in inferior temporal cortex. Cerebral Cortex 19(8):1870–1888

    Article  Google Scholar 

  23. Hoyer PO, Hyvärinen A (2002) A multi-layer sparse coding network learns contour coding from natural images. Vision Research 42(12):1593–1605

    Article  Google Scholar 

  24. Hawkins J, Ahmad S, Dubinsky D (2012) Hierarchical temporal memory including htm cortical learning algorithms. Techical Report

  25. Olshausen BA, Field DJ (2004) Sparse coding of sensory inputs. Curr Opin Neurobiol 14(4):481–487

    Article  Google Scholar 

  26. Attwell D, Laughlin SB (2001) An energy budget for signaling in the grey matter of the brain. J Cereb Blood Flow Metab 21(10):1133–1145

    Article  Google Scholar 

  27. Lennie P (2003) The cost of cortical computation. Curr Biol 13(6):493–497

    Article  Google Scholar 

  28. Olshausen BA et al (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583):607–609

    Article  Google Scholar 

  29. Changizi MA (2001) Universal scaling laws for hierarchical complexity in languages, organisms, behaviors and other combinatorial systems. J Theor Biol 211(3):277–295

    Article  Google Scholar 

  30. Rivest RL, Sloan R (1994) A formal model of hierarchical concept-learning. Inf Comput 114(1):88–114

    Article  MathSciNet  MATH  Google Scholar 

  31. Simon HA (1996) Sciences of the artificial, vol 136. MIT Press, Cambridge

    Google Scholar 

  32. George D, Hawkins J (2009) Towards a mathematical theory of cortical micro-circuits. PLoS Comput Biol 5(10):e1000532

    Article  MathSciNet  Google Scholar 

  33. Bobier BA, Wirth M (2008) Content-based image retrieval using hierarchical temporal memory. In: Proceedings of the 16th ACM international conference on multimedia. ACM, pp 925–928

  34. Starzyk JA, He H (2009) Spatio-temporal memories for machine learning: a long-term memory organization. Neural Netw IEEE Trans 20(5):768–780

    Article  Google Scholar 

  35. Starzyk JA, He H (2007) Anticipation-based temporal sequences learning in hierarchical structure. Neural Netw IEEE Trans 18(2):344–358

    Article  Google Scholar 

  36. Mountcastle VB (1978) An organizing principle for cerebral function: the unit model and the distributed system. MIT Press, Cambridge

    Google Scholar 

  37. Horton JC, Adams DL (2005) The cortical column: a structure without a function. Philos Trans R Soc B: Biol Sci 360(1456):837–862

    Article  Google Scholar 

  38. Rinkus GJ (2010) A cortical sparse distributed coding model linking mini-and macrocolumn-scale functionality. Front Neuroanat 4(2):1–13

    Google Scholar 

  39. Kuang Y, Zhang Y, Zhang L (2013) An improved code selection algorithm for fault prediction. Neural Comput Appl 22(7–8):1763–1772

    Article  Google Scholar 

  40. Hawkins J, George D (2011) Hierarchical temporal memory: concepts, theory and terminology. Whitepaper, Numenta Inc, Hayes

    Google Scholar 

  41. Pradhan B, Lee S (2010) Landslide susceptibility assessment and factor e?ect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw 25(6):747–759

    Article  Google Scholar 

  42. Shifei D, Gang M, Zhongzhi S (2014) A rough RBF neural network based on weighted regularized extreme learning machine. Neural Process Lett 40(3):245–260

    Article  Google Scholar 

  43. Zhizheng L, Ning L (2014) Efficient feature scaling for support vector machines with a quadratic kernel. Neural Process Lett 39(3):235–246

    Article  Google Scholar 

  44. Hebb DO (2002) The organization of behavior: a neuropsychological theory. Psychology Press, Routledge

    Google Scholar 

  45. Rinkus GJ (1986) A combinatorial neural network exhibiting episodic and semantic memory properties for spatio-temporal patterns. Dissertation, Boston University

  46. Willerman L, Schultz R, Rutledge JN, Bigler ED (1991) In vivo brain size and intelligence. Intelligence 15(2):223–228

    Article  Google Scholar 

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Acknowledgments

This work was supported by China Postdoctoral Science Foundation under Grant No. 2014M560730, National Nature Science Foundation of China under Grant No. 61304187, Science Foundation of Science & Technology Department of Sichuan Province under Grant No. 2015JY0071, and Nature Science Foundation of Chengdu Normal University under Grants Nos. CS14ZD02 and YJRC2014-9.

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Authors and Affiliations

  1. College of Computer Science, Chengdu Normal University, Chengdu, 611130, People’s Republic of China

    Xinyi Zhou & Yin Kuang

  2. College of Computer Science, Neijiang Normal University, Neijiang, 641002, People’s Republic of China

    Nianqing Tang

  3. Institute of Intelligent Computing and Information Technology, Chengdu Normal University, Chengdu, 611130, People’s Republic of China

    Yin Kuang

  4. Postdoctoral Research Station, Chengdu Information Technology of Chinese Academy of Sciences Co., Ltd, Chengdu, 610041, People’s Republic of China

    Yin Kuang & Zhong Liu

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  1. Xinyi Zhou
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  2. Nianqing Tang
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  4. Zhong Liu
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Correspondence to Yin Kuang.

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Zhou, X., Tang, N., Kuang, Y. et al. An approach to variable-order prediction via multiple distal dendrites of neurons. Neural Comput & Applic 29, 1–12 (2018). https://doi.org/10.1007/s00521-016-2518-y

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  • DOI: https://doi.org/10.1007/s00521-016-2518-y

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