Abstract
Artificial Metaplasticity learning algorithm is inspired by the biological metaplasticity property of neurons and Shannon’s information theory. In this research, Artificial Metaplasticity on multilayer perceptron (AMMLP) is compared with regular Backpropagation by using input sets generated with different probability distributions: Gaussian, Exponential, Uniform and Rayleigh. Artificial Metaplasticity shows better results than regular Backpropagation for Gaussian and Uniform distribution while regular Backpropagation shows better results for Exponential and Rayleigh distributions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jedlicka, P.: Synaptic plasticity, metaplasticity and BCM theory 103(4), 137–143 (2002)
Malenka, R., Bear, M.: LTP and LTD: An Embarrassment of Riches. Neuron 44(1), 5–21 (2004), doi:10.1016/j.neuron.2004.09.012
Abraham, W.C.: Activity-dependent regulation of synaptic plasticity (metaplasticity) in the hippocampus. In: Kato, N. (ed.) The Hippocampus: Functions and Clinical Relevance, pp. 15–26. Elsevier, Amsterdam (1996)
Abraham, W.C.: Metaplasticity: Key Element in Memory and Learning? News in Physiological Sciences 14(2), 85 (1999)
Abraham, W.C., Bear, M.F.: Metaplasticity: The plasticity of synaptic plasticity. Trends in Neurosciences 19, 126–130 (1996), doi:10.1016/S0166-2236(96)80018-X
Kinto, E.A., Del Moral Hernandez, E., Marcano, A., Ropero Peláez, J.: A preliminary neural model for movement direction recognition based on biologically plausible plasticity rules. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2007. LNCS, vol. 4528, pp. 628–636. Springer, Heidelberg (2007)
Chiappalone, M., Vato, A., Berdondini, L., Koudelka, M., Martinoia, S.: Network Dynamics and Synchronous Activity in Cultured Cortical Neurons. Int. J. Neural Syst. 17(2), 87–103 (2007)
Andina, D., Alvarez-Vellisco, A., Jevtic, A., Fombellida, J.: Artificial metaplasticity can improve artificial neural network learning. Intelligent Automation and Soft Computing, SI on Signal Processing and Soft Computing 15, 683–696 (2009) ISSN: 1079-8587
Marcano-Cedeño, A., Quintanilla-Domínguez, J., Andina, D.: Breast cancer classification applying artificial metaplasticity algorithm. Neurocomputing, doi:10.1016/j.neucom.2010.07.019
Marcano-Cedeño, A., Martín de la Bárcena, A., Jiménez-Trillo, J., Piñuela, J.A., Andina, D.: Artificial Metaplasticity Neural Network Applied to Credit Scoring. Int. J. Neural Syst. 21(4), 311–317 (2011), doi:10.1142/S0129065711002857
Marcano-Cedeño, A., Quintanilla-Domínguez, J., Andina, D.: Wood Defects Classification Using Artificial Metaplasticity Neural Network. In: Proc. 35th Annual Conf. on of the IEEE Industrial Electronics Society, Porto, Portugal, pp. 3422–3427 (2009), doi:10.1109/IECON.2009.5415189
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de Pablos Álvaro, M., Andina, D. (2013). Robustness of Artificial Metaplasticity Learning to Erroneous Input Distribution Assumptions. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz López, F., Toledo Moreo, F.J. (eds) Natural and Artificial Models in Computation and Biology. IWINAC 2013. Lecture Notes in Computer Science, vol 7930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38637-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-38637-4_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38636-7
Online ISBN: 978-3-642-38637-4
eBook Packages: Computer ScienceComputer Science (R0)