Abstract
The cerebellar system is implicated in motor learning for movement coordination. In this paper, we suggest a simplified cerebellar model with priority-based delayed eligibility trace learning rule (S-CDE) that enables a mobile agent to randomly navigate in an environment. The depth information from a simulated laser sensor is encoded as neuronal region activity for velocity and turn rate control. A priority-based delayed eligibility trace learning rule is proposed to maximize the usage of input signals for learning in synapses on Purkinje cell and cells in the deep cerebellar nuclei. Asymmetric weighted sum and velocity signal conversion algorithms are designed to facilitate training in an environment containing turns of varying curvatures. S-CDE is developed as a brain-based device and tested on a simulated mobile agent which had to randomly navigate maps of Singapore and Hong Kong expressways.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Houk, J.C., Buckingham, J.T., Barto, A.G.: Learning in and from brain-based devices. Behavioral and Brain Sciences 19(3), 368–383 (1996)
Llinás, R.R.: Cerebellar motor learning vs. cerebellar motor timing: The climbing fiber story. The Journal of Physiology 589(14), 3423–3432 (2011)
Schlerf, J., Ivry, R.B., Diedrichsen, J.: Encoding of sensory prediction errors in the human cerebellum. The Journal of Neuroscience 32(14), 4913–4922 (2012)
Medina, J.F., Carey, M.R., Lisberger, S.G.: The representation of time for motor learning. Neuron 45(1), 157–167 (2005)
Ito, M.: Error detection and representation in the olivo-cerebellar system. Frontiers in Neural Circuits 7(1), 1–8 (2013)
Wolpert, D.M., Miall, R.C., Kawato, M.: Internal models in the cerebellum. Trends in Cognitive Sciences 2(9), 338–347 (1998)
McKinstry, J.L., Edelman, G.M., Krichmar, J.L.: A cerebellar model for predictive motor control tested in a brain-based device. Proceedings of the National Academy of Sciences 103(9), 3387–3392 (2006)
Edelman, G.M.: Learning in and from brain-based devices.. Science 318(5853), 1103–1105 (2007)
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
Shim, V.A., Ranjit, C.S.N., Tian, B., Tang, H. (2013). A Simplified Cerebellum-Based Model for Motor Control in Brain Based Devices. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42054-2_65
Download citation
DOI: https://doi.org/10.1007/978-3-642-42054-2_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42053-5
Online ISBN: 978-3-642-42054-2
eBook Packages: Computer ScienceComputer Science (R0)