Learning and Recognition of Multiple Fluctuating Temporal Patterns Using S-CTRNN
In the present study, we demonstrate the learning and recognition capabilities of our recently proposed recurrent neural network (RNN) model called stochastic continuous-time RNN (S-CTRNN). S-CTRNN can learn to predict not only the mean but also the variance of the next state of the learning targets. The network parameters consisting of weights, biases, and initial states of context neurons are optimized through maximum likelihood estimation (MLE) using the gradient descent method. First, we clarify the essential difference between the learning capabilities of conventional CTRNN and S-CTRNN by analyzing the results of a numerical experiment in which multiple fluctuating temporal patterns were used as training data, where the variance of the Gaussian noise varied among the patterns. Furthermore, we also show that the trained S-CTRNN can recognize given fluctuating patterns by inferring the initial states that can reproduce the patterns through the same MLE scheme as that used for network training.
Keywordsrecurrent neural network S-CTRNN variance estimation
Unable to display preview. Download preview PDF.
- 3.Sutskever, I., Martens, J., Hinton, G.E.: Generating Text with Recurrent Neural Networks. In: Proceedings of the 28th International Conference on Machine Learning (2011)Google Scholar
- 7.Tani, J.: Model-based learning for mobile robot navigation from the dynamical systems perspective. IEEE Transactions on Systems, Man, and Cybernetics 26(3), 421–436 (1996)Google Scholar
- 9.Yamashita, Y., Tani, J.: Emergence of functional hierarchy in a multiple timescale neural network model: A humanoid robot experiment. PLoS Computational Biology 4(11), e1000220(2008)Google Scholar
- 10.Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Internal Representations by Error Propagation. In: Rumelhart, D.E., McClelland, D. (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, pp. 318–362. MIT Press, Cambridge (1986)Google Scholar
- 11.Namikawa, J., Nishimoto, R., Arie, H., Tani, J.: Synthetic approach to understanding meta-level cognition of predictability in generating cooperative behavior. In: Advances in Cognitive Neurodynamics (III) Proceedings of the Third International Conference on Cognitive Neurodynamics 2011 (2013)Google Scholar