Abstract
In this paper, a method is introduced in order to qualify the performance of dynamic neural fields (DNF). The method is applied to Amari’s DNF equations, in order to drive the tuning of its free parameters. An original evaluation procedure is presented, and then applied to some input evolution scenarios. Such scenarios define an applicative context, for which the parameters with the lowest evaluation are optimal.
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Jones, E.: Microcolumns in the cerebral cortex. PNAS 97(10), 5019–5021 (2000)
Elbert, T., Rockstroh, B.: Reorganization of human cerebral cortex: The range of changes following use and injury. The Neuroscientist 10(2), 129–141 (2004)
Stavrinou, M., Penna, S., Pizzella, V., Torquati, K., Cianflone, F., Franciotti, R., Bezerianos, A., Romani, G., Rossini, P.: Temporal dynamics of plastic changes in human primary somatosensory cortex after finger webbing. Cerebral Cortex 17(9), 2134–2142 (2007)
Kohonen, T.: Self-Organization and Associative Memory. Springer Series in Information Sciences, vol. 8. Springer, Heidelberg (1989)
Amari, S.I.: Dynamics of pattern formation in lateral-inhibition type neural fields. Biological Cybernetics 27, 77–87 (1977)
Taylor, J.G.: Neural networks for consciousness. Neural Netowrks 10(7), 1207–1225 (1997)
Pinto, D., Ermentrout, G.: Spatially structured activity in synaptically coupled neuronal networks: I. traveling fronts and pulses. SIAM J. Appl. Math. 62, 206–225 (2001)
Pinto, D., Ermentrout, G.: Spatially structured activity in synaptically coupled neuronal networks: Ii. lateral inhibition and standing pulses. SIAM J. Appl. Math. 62, 226–243 (2001)
Rougier, N.P., Vitay, J.: Emergence of attention within a neural population. Neural Networks 5(19), 573–581 (2006)
Ménard, O., Frezza-Buet, H.: Model of multi-modal cortical processing: Coherent learning in self-organizing modules. Neural Networks 18(5-6), 646–655 (2005)
Mikhailova, I., Goerick, C.: Conditions of activity bubble uniqueness in dynamic neural fields. Biological Cybernetics 92(2), 82–91 (2005)
Alecu, L., Frezza-Buet, H.: Are neural fields suitable for vector quantization? In: Proc. of The Seventh International Conference on Machine Learning and Applications (ICMLA 2008). IEEE, Los Alamitos (2008)
Alecu, L., Frezza-Buet, H.: Reconciling neural fields to self-organization. In: European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning (ESANN), April 2009, pp. 571–576 (2009)
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Alecu, L., Frezza-Buet, H. (2009). Application-Driven Parameter Tuning Methodology for Dynamic Neural Field Equations. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10677-4_15
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DOI: https://doi.org/10.1007/978-3-642-10677-4_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10676-7
Online ISBN: 978-3-642-10677-4
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