Abstract
This paper proposes a novel plastic neural model (PNM) at the single neuron level and a specified learning algorithm to train it. The dendritic structure of PNM presents its diversity to fulfill each particular task. During the training process, PNM divides the Euclidean space of the training instances into several appropriate hypercubes, which have the same dimensional number. And then, each hypercube is transformed into a corresponding dendritic branch in PNM. A suitable dendritic structure of PNM has been proved to have powerful computational capabilities to solve the classification problems. Both theoretical analysis and empirical study of the proposed model are demonstrated in this paper. Five benchmark problems are employed to verify the effectiveness of PNM in our experiment. The results have verified that PNM can provide competitive classification performance compared with several widely-used classifiers.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adankon, M.M., Cheriet, M.: Support vector machine. Encycl. Biometrics, 1303–1308 (2009)
Anderson, J., Binzegger, T., Kahana, O., Martin, K., Segev, I.: Dendritic asymmetry cannot account for directional responses of neurons in visual cortex. Nat. Neurosci. 2(9), 820–824 (1999)
Archie, K.A., Mel, B.W.: A model for intradendritic computation of binocular disparity. Nat. Neurosci. 3(1), 54–63 (2000)
Cazé, R.D., Humphries, M., Gutkin, B.: Passive dendrites enable single neurons to compute linearly non-separable functions. PLoS Comput. Biol. 9(2), 867 (2013)
Chen, S., Cowan, C.F., Grant, P.M.: Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. Neural Netw. 2(2), 302–309 (1991)
Costa, R.P., Sjöström, P.J.: One cell to rule them all, and in the dendrites bind them. Front. Synaptic Neurosci. 3 (2011)
Csáji, B.C.: Approximation with artificial neural networks. Faculty of Sciences, Etvs Lornd University, Hungary 24, 48 (2001)
Dayhoff, J.E.: Neural network architectures: an introduction. Van Nostrand Reinhold Co. (1990)
Derrac, J., GarcÃa, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1(1), 3–18 (2011)
Euler, T., Detwiler, P.B., Denk, W.: Directionally selective calcium signals in dendrites of starburst amacrine cells. Nature 418(6900), 845–852 (2002)
Fahlman, S.E.: An empirical study of learning speed in back-propagation networks (1988)
Ferster, D., Jagadeesh, B.: Epsp-ipsp interactions in cat visual cortex studied with in vivo whole-cell patch recording. J. Neurosci. 12(4), 1262–1274 (1992)
Fine, T.L.: Feedforward Neural Network Methodology. Springer, New York (2006). https://doi.org/10.1007/b97705
GarcÃa, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms behaviour: a case study on the cec2005 special session on real parameter optimization. J. Heuristics 15(6), 617–644 (2009)
Gardner, M.W., Dorling, S.: Artificial neural networks (the multilayer perceptron) a review of applications in the atmospheric sciences. Atmos. Environ. 32(14–15), 2627–2636 (1998)
Gurney, K.N.: Information processing in dendrites: I. input pattern generalization. Neural Netw. 14(8), 991–1004 (2001)
Gurney, K.N.: Information processing in dendrites: II. information theoretic complexity. Neural Netw. 14(8), 1005–1022 (2001)
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)
Ji, J., Gao, S., Cheng, J., Tang, Z., Todo, Y.: An approximate logic neuron model with a dendritic structure. Neurocomputing 173, 1775–1783 (2016)
Larose, D.T.: k-nearest neighbor algorithm. In: Discovering Knowledge in Data: An Introduction to Data Mining, pp. 90–106 (2005)
Lee, Y., Oh, S.H., Kim, M.W.: An analysis of premature saturation in back propagation learning. Neural Netw. 6(5), 719–728 (1993)
Legenstein, R., Maass, W.: Branch-specific plasticity enables self-organization of nonlinear computation in single neurons. J. Neurosci. 31(30), 10787–10802 (2011)
Magee, J.C.: Dendritic integration of excitatory synaptic input. Nat. Rev. Neurosci. 1(3), 181–190 (2000)
Magee, J.C.: Dendritic voltage-gated ion channels. Dendrites, 225–251 (2008)
Magoulast, G., Vrahatis, M., Androulakis, G.: On the alleviation of the problem of local minima in back-propagation. Nonlinear Anal. Theor. Methods Appl. 30(7), 4545–4550 (1997)
McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115–133 (1943)
Mehrotra, K., Mohan, C.K., Ranka, S.: Elements of Artificial Neural Networks. MIT Press, Cambridge (1997)
Minsky, M., Papert, S.: Perceptrons (1969)
Mirjalili, S., Lewis, A.: S-shaped versus v-shaped transfer functions for binary particle swarm optimization. Swarm Evol. Comput. 9, 1–14 (2013)
Newman, A.A.D.: UCI repository of machine learning database (school of information and computer science), University of California, Irvine (2007)
Oesch, N., Euler, T., Taylor, W.R.: Direction-selective dendritic action potentials in rabbit retina. Neuron 47(5), 739–750 (2005)
Poirazi, P., Mel, B.W.: Impact of active dendrites and structural plasticity on the memory capacity of neural tissue. Neuron 29(3), 779–796 (2001)
Rosenblatt, F.: The perceptron, a perceiving and recognizing automaton Project Para. Cornell Aeronautical Laboratory (1957)
Salinas, E., Abbott, L.: A model of multiplicative neural responses in parietal cortex. Proc. Natl. Acad. Sci. 93(21), 11956–11961 (1996)
Schachter, M.J., Oesch, N., Smith, R.G., Taylor, W.R.: Dendritic spikes amplify the synaptic signal to enhance detection of motion in a simulation of the direction-selective ganglion cell. PLoS Comput. Biol. 6(8), e1000, 899 (2010)
Simon, H.: Neural Networks, A Comprehensive Foundation. Prentice-Hall, Englewood Cliffs (1999)
Sjöström, P.J., Rancz, E.A., Roth, A., Häusser, M.: Dendritic excitability and synaptic plasticity. Physiol. Rev. 88(2), 769–840 (2008)
Todo, Y., Tamura, H., Yamashita, K., Tang, Z.: Unsupervised learnable neuron model with nonlinear interaction on dendrites. Neural Networks 60, 96–103 (2014)
Varma, S., Simon, R.: Bias in error estimation when using cross-validation for model selection. BMC Bioinf. 7(1), 91 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ji, J., Dong, M., Tang, C., Zhao, J., Song, S. (2020). A Novel Plastic Neural Model with Dendritic Computation for Classification Problems. In: Huang, DS., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12463. Springer, Cham. https://doi.org/10.1007/978-3-030-60799-9_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-60799-9_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60798-2
Online ISBN: 978-3-030-60799-9
eBook Packages: Computer ScienceComputer Science (R0)