Imperial College London
Alternate

Professor Claudia Clopath

Faculty of Engineering, Department of Bioengineering

Professor of Computational Neuroscience
 
 
 
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Contact

 

+44 (0)20 7594 1435c.clopath Website

 
 
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Location

 

Royal School of Mines 4.09Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Bouvierm:2018:10.7554/eLife.31599,
author = {Bouvierm, G and Aljadeff, J and Clopath, C and Bimbard, C and Ranft, J and Blot, A and Nadel, J-P and Brunel, N and Hakim, V and Barbour, B},
doi = {10.7554/eLife.31599},
journal = {eLife},
title = {Cerebellar learning using perturbations},
url = {http://dx.doi.org/10.7554/eLife.31599},
volume = {7},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The cerebellum aids the learning of fast, coordinated movements. According tocurrent consensus, erroneously active parallel fibre synapses are depressed by complex spikessignalling movement errors. However, this theory cannot solve the credit assignment problem ofprocessing a global movement evaluation into multiple cell-specific error signals. We identify apossible implementation of an algorithm solving this problem, whereby spontaneous complexspikes perturb ongoing movements, create eligibility traces and signal error changes guidingplasticity. Error changes are extracted by adaptively cancelling the average error. This framework,stochastic gradient descent with estimated global errors (SGDEGE), predicts synaptic plasticityrules that apparently contradict the current consensus but were supported by plasticityexperiments in slices from mice under conditions designed to be physiological, highlighting thesensitivity of plasticity studies to experimental conditions. We analyse the algorithm’s convergenceand capacity. Finally, we suggest SGDEGE may also operate in the basal ganglia.
AU  - Bouvierm,G
AU  - Aljadeff,J
AU  - Clopath,C
AU  - Bimbard,C
AU  - Ranft,J
AU  - Blot,A
AU  - Nadel,J-P
AU  - Brunel,N
AU  - Hakim,V
AU  - Barbour,B
DO  - 10.7554/eLife.31599
PY  - 2018///
SN  - 2050-084X
TI  - Cerebellar learning using perturbations
T2  - eLife
UR  - http://dx.doi.org/10.7554/eLife.31599
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000449729300001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/65302
VL  - 7
ER  -
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