Improved stability and convergence with three factor learning
2007 | conference paper. A publication with affiliation to the University of Göttingen.
Jump to: Cite & Linked | Documents & Media | Details | Version history
Cite this publication
Improved stability and convergence with three factor learning
Porr, B.; Kulvicius, T. & Woergoetter, F. (2007)
Neurocomputing, 70(10-12) pp. 2005-2008. 15th Annual Computational Neuroscience Meeting, Edinburgh, SCOTLAND.
Amsterdam: Elsevier Science Bv. DOI: https://doi.org/10.1016/j.neucom.2006.10.137
Documents & Media
Details
- Authors
- Porr, Bernd; Kulvicius, Tomas; Woergoetter, Florentin
- Abstract
- Donald Hebb postulated that if neurons fire together they wire together. However, Hebbian learning is inherently unstable because synaptic weights will self-amplify themselves: the more a synapse drives a postsynaptic cell the more the synaptic weight will grow. We present a new biologically realistic way of showing how to stabilise synaptic weights by introducing a third factor which switches learning on or off so that self-amplification is minimised. The third factor can be identified by the activity of dopaminergic neurons in ventral tegmental area which leads to a new interpretation of the dopamine signal which goes beyond the classical prediction error hypothesis. (c) 2006 Elsevier B.V. All rights reserved.
- Issue Date
- 2007
- Status
- published
- Publisher
- Elsevier Science Bv
- Journal
- Neurocomputing
- Conference
- 15th Annual Computational Neuroscience Meeting
- Conference Place
- Edinburgh, SCOTLAND
- ISSN
- 0925-2312