oliver.obst.eu

In a paper that was recently accepted at the European Symposium on Artificial Neural Networks (ESANN 2009), we look at different ways to influence the performance of echo state networks. Traditionally, echo state networks and other reservoir computing approaches use a fixed random connected reservoir, which leads to significant variation in performance. Only few problem specific optimisation procedures are known to date. We study a general initialization method using permutation matrices and derive a new unsupervised learning rule based on intrinsic plasticity (IP) for echo state networks. Using three different benchmarks, we show that networks with permutation matrices for the reservoir connectivity have much longer memory than the other methods, but are also able to perform highly non-linear mappings. We also show that IP based on sigmoid transfer functions is limited concerning the output distributions that can be achieved.

Studies on Reservoir Initialization and Dynamics Shaping in Echo State Networks,
J. Boedecker, O. Obst, N.M. Mayer, M. Asada. The full paper will be available after the conference (April) is now available.

neural networks, intrinsic plasticity, echo state networks, reservoir computing

UC 2008, the Seventh International Conference on Unconventional Computation will take place in Vienna, August 25-28, 2008.

Original papers are solicited in all areas of unconventional computation; typical, but not exclusive, topics are: natural computing including quantum, cellular, molecular, neural, and membrane computing as well as evolutionary paradigms; chaos and dynamical systems based computing; proposals for computations going beyond the Turing model.

Submissions are due on April 14th, 2008. The call for papers and the conference poster are available from the conference homepage.

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