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Towards a learning-theoretic analysis of spike-timing dependent plasticity

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons83792

Balduzzi,  D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons75278

Besserve,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Balduzzi, D., & Besserve, M. (2012). Towards a learning-theoretic analysis of spike-timing dependent plasticity. In Advances in Neural Information Processing Systems 25 (pp. 2465-2473).


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-B550-C
Abstract
This paper suggests a learning-theoretic perspective on how synaptic plasticitybenefits global brain functioning. We introduce a model, the selectron, that (i)arises as the fast time constant limit of leaky integrate-and-fire neurons equippedwithspikingtimingdependentplasticity(STDP)and(ii)isamenabletotheoreticalanalysis. We show that the selectron encodes reward estimates into spikes and thatan error bound on spikes is controlled by a spiking margin and the sum of synapticweights. Moreover, the efficacy of spikes (their usefulness to other reward maxi-mizing selectrons) also depends on total synaptic strength. Finally, based on ouranalysis, we propose a regularized version of STDP, and show the regularizationimproves the robustness of neuronal learning when faced with multiple stimuli.