Abstract
The advantages of using sparse coding to represent neural information have previously been suggested. It has been proposed for the neural information processing of sensory information, particularly visual information, but its role in processing motor information is poorly understood. In this study, therefore, we considered a motor-related system to determine the benefits of using sparse coding for motor command representation in terms of its energy cost, robustness against noise, and adaptability to damage. We compared the properties contributed by sparse coding and dense coding by simulating a task involving a reaching movement with a two-joint arm model. Our results showed that sparse coding was more beneficial than dense coding for each of the properties that we investigated, which suggests that it is worthy of study as a possible approach to representing the coding of motor-related information in the central nervous system.
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
References
Willshaw, D.J., Buneman, O.P., Longuet-Higgins, H.C.: Non-holographic associative memory. Nature 222, 960–962 (1969)
Tsodyks, M.V., Feigelman, M.V.: Enhanced storage capacity in neural networks with low level of activity. Europhys. Lett. 6, 101 (1988)
Olshausen, B.A., Field, D.J.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381, 607–609 (1996)
Takiyama, K.: Sensorimotor transformation via sparse coding. Sci. Rep. 5, 9648 (2015). doi:10.1038/srep09648
Uno, Y., Kawato, M., Suzuki, R.: Formation and control of optimal trajectory in human multijoint arm movement. Biol. Cybern. 61, 89–101 (1989)
Ikeda, S., Sakaguchi, Y.: Motor planning as an optimization of command representation. In: Proceedings of 48th IEEE Conference on Decision and Control (2009)
Katayama, K., Kawato, M.: Virtual trajectory and stiffness ellipse during multijoint arm movement predicted by neural inverse models. Biol. Cybern. 69, 353–362 (1993)
Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)
Harris, C.M., Wolpert, D.M.: Signal-dependent noise determines motor planning. Nature 394, 780–784 (1998)
Kalaska, J.F., Cohen, D.A., Hyde, M.L., Prud’homme, M.: A comparison of movement direction-related versus load direction-related activity in primate motor cortex, using a two-dimenstional reaching task. J. Neurosci. 9, 2080–2102 (1989)
Binenstock, E.L., Cooper, L.N., Munro, P.W.: Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. J. Neurosci. 2, 32–48 (1982)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Hinakawa, N., Kitano, K. (2017). Robust and Adaptable Motor Command Representation with Sparse Coding. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10613. Springer, Cham. https://doi.org/10.1007/978-3-319-68600-4_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-68600-4_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68599-1
Online ISBN: 978-3-319-68600-4
eBook Packages: Computer ScienceComputer Science (R0)