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POSTER PRESENTATION Open Access
Stochastic gradient ascent learning with spiketiming dependent plasticityJoana Vieira*, Orlando Arévalo, Klaus Pawelzik
From Twentieth Annual Computational Neuroscience Meeting: CNS*2011Stockholm, Sweden. 23-28 July 2011
Stochastic gradient ascent learning exploits correlationsof parameter variations with overall success of a system.This algorithmic idea has been related to neuronal net-work learning by postulating eligibility traces atsynapses, which make them selectable for synapticchanges depending on later reward signals ([1] and [2]).Formalizations of the synaptic and neuronal dynamicssupporting gradient ascent learning in terms of differen-tial equations exhibit strong similarities with a recent
formulation of spike timing dependent plasticity (STDP)[3] when it is combined with a reward signal. Here wepresent conditions under which reward modulatedSTDP is in fact guaranteed to maximize expectedreward. We present numerical simulations underliningthe relevance of realistic STDP models for rewarddependent learning. In particular, we find that the non-linear adaptation to pre- and post-synaptic activities ofSTDP [3] contributes to stable learning.
* Correspondence: [email protected] for Theoretical Physics, University of Bremen, Bremen, D-28359,Germany
Figure 1 Learning the XOR function with a reward modulated STDP rule. Left: Output activity versus training episode in a feed forward networkwith Poisson-like neurons (2 input nodes, 10 hidden nodes and 1 output node). The output activity for the [true, false] and [false, true] inputsbecomes stronger, while the output for the [true, true] and [false, false] inputs becomes weak after training. Right: Accumulated administeredreward for the four input patterns versus training episode.
Vieira et al. BMC Neuroscience 2011, 12(Suppl 1):P250http://www.biomedcentral.com/1471-2202/12/S1/P250
© 2011 Vieira et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited.
Published: 18 July 2011
References1. Sebastian Seung H: Learning in Spiking Neural Networks by
Reinforcement of Stochastic Synaptic Transmission. Neuron 2003,40(6):1063-1073.
2. Xiaohui Xie, Sebastian Seung H: Learning in neural networks byreinforcement of irregular spiking. Phys Rev E 2004, 69(4):041909-1-041909-10.
3. Schmiedt Joscha T, Christian Albers, Klaus Pawelzik: Spike timing-dependent plasticity as dynamic filter. In Advances in Neural InformationProcessing Systems 23 J. Lafferty and C. K. I. Williams and J. Shawe-Taylorand R.S. Zemel and A. Culotta 2010, 2110-2118.
doi:10.1186/1471-2202-12-S1-P250Cite this article as: Vieira et al.: Stochastic gradient ascent learning withspike timing dependent plasticity. BMC Neuroscience 2011 12(Suppl 1):P250.
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Vieira et al. BMC Neuroscience 2011, 12(Suppl 1):P250http://www.biomedcentral.com/1471-2202/12/S1/P250
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