Online adaptation and over-trial learning in macaque visuomotor control
Daniel A. Braun1,2*,
Ad Aertsen1,2,
Rony Paz
3,
Eilon Vaadia
4,5,6,
Stefan Rotter1,2 and
Carsten Mehring1,2,7,8
- 1 Bernstein Center Freiburg, Freiburg, Germany
- 2 Faculty of Biology, University of Freiburg, Freiburg, Germany
- 3 Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
- 4 Department of Medical Neurobiology,
Institute for Medical Research Israel-Canada, Faculty of Medicine, The
Hebrew University of Jerusalem, Jerusalem, Israel
- 5 Interdisciplinary Center for Neural Computation, The Hebrew University of Jerusalem, Jerusalem, Israel
- 6 Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
- 7 Department of Bioengineering, Imperial College London, London, UK
- 8 Department of Electrical and Electronic Engineering, Imperial College London, London, UK
When faced with unpredictable environments, the human motor system
has been shown to develop optimized adaptation strategies that allow
for online adaptation during the control process. Such online adaptation
is to be contrasted to slower over-trial learning that corresponds to a
trial-by-trial update of the movement plan. Here we investigate the
interplay of both processes, i.e., online adaptation and over-trial
learning, in a visuomotor experiment performed by macaques. We show that
simple non-adaptive control schemes fail to perform in this task, but
that a previously suggested adaptive optimal feedback control model can
explain the observed behavior. We also show that over-trial learning as
seen in learning and aftereffect curves can be explained by learning in a
radial basis function network. Our results suggest that both the
process of over-trial learning and the process of online adaptation are
crucial to understand visuomotor learning.
Keywords: visuomotor learning, motor control, online adaptation, over-trial learning
Citation: Braun DA, Aertsen A, Paz R, Vaadia E,
Rotter S and Mehring C (2011) Online adaptation and over-trial learning
in macaque visuomotor control. Front. Comput. Neurosci. 5:27. doi: 10.3389/fncom.2011.00027
Received: 06 October 2010;
Accepted: 22 May 2011;
Published online: 14 June 2011.
Copyright: © 2011 Braun, Aertsen, Paz, Vaadia, Rotter
and Mehring. This is an open-access article subject to a non-exclusive
license between the authors and Frontiers Media SA, which permits use,
distribution and reproduction in other forums, provided the original
authors and source are credited and other Frontiers conditions are
complied with.
*Correspondence: Daniel A. Braun, Computational and
Biological Learning Lab, Department of Engineering, University of
Cambridge, Cambridge CB2 1PZ, UK. e-mail: dab54@cam.ac.uk