Improve Discontinuous Output Change in SpikeProp
Haruhiko Takase*, Hiroharu Kawanaka*, and Shinji Tsuruoka**
*Graduate School of Engineering, Mie University, 1577, Kurima-Machiya, Tsu, Mie 514-8507, Japan
**Graduate School of Regional Innovation Studies, Mie University, 1577, Kurima-Machiya, Tsu, Mie 514-8507, Japan
-  W. Maass and C. M. Bishop, “Pulsed Neurla Networks,” Cambridge, MA, MIT press, 1998.
-  W. Maass, “Noisy Spiking neurons with Temporal Coding have more Computational Power than Sigmoidal Neurons,” Neural Information Processing Systems, pp. 211-217, 1996.
-  A. Kasiński and F. Ponulak, “Comparison of supervised learning methods for spike time coding in spiking neural networks,” Int. J. of Applied Mathematics and Computer Science, Vol.16, No.1, pp. 101-113, 2006.
-  S. GomesWysoski, L. Benuskova, and N. Kasabov, “Evolving spiking neural networks for audiovisual information processing,” Neural Networks, Vol.23, pp. 819-835, 2010.
-  S. M. Bohte, J. N. Kok, and H. La Poutŕe, “Error-backpropagation in temporally encoded networks of spiking neurons,” Neurocomputing, Vol.48, pp. 17-37, 2002.
-  D. C. Plaut, S. J. Nowlan, and G. E. Hinton, “Experiments on Learning by Back Propagation,” Technical Report CMU-CS-86-126, Pittsburgh, PA, 1986.
-  M. Ishikawa, “Structural learning with forgetting,” Neural Networks, Vol.9, No.3, pp. 509-521, 1996.
-  D. E. Rumelhart, J. L. McClelland, “Parrallel distributed processing: Explorations in the microstructure of cognition,” Vol.1, Cambridge, MA, MIT Press, 1986.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.