Conference Proceedings
Spike-timing dependent plasticity in recurrently connected networks with fixed external inputs
M Gilson, DB Grayden, JL Van Hemmen, DA Thomas, AN Burkitt
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics | SPRINGER-VERLAG BERLIN | Published : 2008
Abstract
This paper investigates spike-timing dependent plasticity (STDP) for recurrently connected weights in a network with fixed external inputs (homogeneous Poisson pulse trains). We use a dynamical system to model the network activity and predict its asymptotic evolution, which turns out to qualitatively depend on the learning parameters and the correlation structure of the inputs. Our predictions are supported by numerical simulations of Poisson neuron networks in general cases as well as for certain cases when using Integrate-And-Fire (IF) neurons. © 2008 Springer-Verlag Berlin Heidelberg.
Grants
Awarded by Australian Research Council
Funding Acknowledgements
The authors thank Iven Mareels, Chris Trengove, Sean Byrnes and Hamish Meffin for useful discussions that introduced significant improvements.MG is funded by two scholarships from The University of Melbourne and NICTA. ANB and DBG acknowledge funding from the Australian Research Council (ARC Discovery Projects #DP0453205 and #DP0664271) and The Bionic Ear Institute.