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Classical conditioning of motor responses: What is the learning mechanism?

Hesslow, Germund LU ; Jirenhed, Dan-Anders LU ; Rasmussen, Anders LU and Johansson, Fredrik LU (2013) In Neural Networks 47(Mar,28). p.81-87
Abstract
According to a widely held assumption, the main mechanism underlying motor learning in the cerebellum, such as eyeblink conditioning, is long-term depression (LTD) of parallel fibre to Purkinje cell synapses. Here we review some recent physiological evidence from Purkinje cell recordings during conditioning with implications for models of conditioning. We argue that these data pose four major challenges to the LTD hypothesis of conditioning. (i) LTD cannot account for the pause in Purkinje cell firing that is believed to drive the conditioned blink. (ii) The temporal conditions conducive to LTD do not match those for eyeblink conditioning. (iii) LTD cannot readily account for the adaptive timing of the conditioned response. (iv) The data... (More)
According to a widely held assumption, the main mechanism underlying motor learning in the cerebellum, such as eyeblink conditioning, is long-term depression (LTD) of parallel fibre to Purkinje cell synapses. Here we review some recent physiological evidence from Purkinje cell recordings during conditioning with implications for models of conditioning. We argue that these data pose four major challenges to the LTD hypothesis of conditioning. (i) LTD cannot account for the pause in Purkinje cell firing that is believed to drive the conditioned blink. (ii) The temporal conditions conducive to LTD do not match those for eyeblink conditioning. (iii) LTD cannot readily account for the adaptive timing of the conditioned response. (iv) The data suggest that parallel fibre to Purkinje cell synapses are not depressed after learning a Purkinje cell CR. Models based on metabotropic glutamate receptors are also discussed and found to be incompatible with the recording data. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Neural Networks
volume
47
issue
Mar,28
pages
81 - 87
publisher
Elsevier
external identifiers
  • WOS:000325446800011
  • PMID:23597758
  • Scopus:84884141397
ISSN
1879-2782
DOI
10.1016/j.neunet.2013.03.013
project
Cognition, Communication and Learning
language
English
LU publication?
yes
id
deda081e-2253-4680-82b1-34dff6ec825c (old id 3733593)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/23597758?dopt=Abstract
date added to LUP
2013-05-04 19:39:44
date last changed
2017-03-12 03:12:33
@article{deda081e-2253-4680-82b1-34dff6ec825c,
  abstract     = {According to a widely held assumption, the main mechanism underlying motor learning in the cerebellum, such as eyeblink conditioning, is long-term depression (LTD) of parallel fibre to Purkinje cell synapses. Here we review some recent physiological evidence from Purkinje cell recordings during conditioning with implications for models of conditioning. We argue that these data pose four major challenges to the LTD hypothesis of conditioning. (i) LTD cannot account for the pause in Purkinje cell firing that is believed to drive the conditioned blink. (ii) The temporal conditions conducive to LTD do not match those for eyeblink conditioning. (iii) LTD cannot readily account for the adaptive timing of the conditioned response. (iv) The data suggest that parallel fibre to Purkinje cell synapses are not depressed after learning a Purkinje cell CR. Models based on metabotropic glutamate receptors are also discussed and found to be incompatible with the recording data.},
  author       = {Hesslow, Germund and Jirenhed, Dan-Anders and Rasmussen, Anders and Johansson, Fredrik},
  issn         = {1879-2782},
  language     = {eng},
  number       = {Mar,28},
  pages        = {81--87},
  publisher    = {Elsevier},
  series       = {Neural Networks},
  title        = {Classical conditioning of motor responses: What is the learning mechanism?},
  url          = {http://dx.doi.org/10.1016/j.neunet.2013.03.013},
  volume       = {47},
  year         = {2013},
}