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Contents 1 NIPS 2006 Workshop on Echo State Networks and Liquid State Machines 1.1 Organizers 1.2 Motivation, Goals and Details of this Workshop 1.3 Poster Presentations 1.4 "Reservoir Computing" email list

NIPS 2006 Workshop on Echo State Networks and Liquid State Machines

on Saturday December 9, see http://www.nips.cc/Conferences/2006/Program/#Workshops

Organizers

Herbert Jaeger, International University Bremen

Wolfgang Maass, University of Technology Graz

Jose C. Principe, University of Florida

Motivation, Goals and Details of this Workshop

A new approach to analyzing and training recurrent neural networks (RNNs) has emerged over the last few years. The central idea is to regard a sparsely connected recurrent circuit as a nonlinear, excitable medium, which is driven by input signals (possibly in conjunction with feedbacks from readouts). This recurrent circuit is – like a kernel in Support Vector Machine applications – not adapted during learning. Rather, very simple (typically linear) readouts are trained to extract desired output signals. Despite its simplicity, it was recently shown that such simple networks have (in combination with feedback from readouts) universal computational power, both for digital and for analog computation. There are currently two main flavours of such networks. Echo state networks were developed from a mathematical and engineering background and are composed of simple sigmoid units, updated in discrete time. Liquid state machines were conceived from a mathematical and computational neuroscience perspective and usually are made of biologically more plausible spiking neurons with a continuous-time dynamics.

These approaches have quickly gained popularity because of their simplicity, expressiveness, and ease of training. In addition they provide a new perspective for modeling cortical computation that differs in several aspects from previous models. Generic cortical microcircuits are seen from this perspective as explicit implementations of kernels (in the sense of SVMs), raising the question how such explicit kernels can be optimized by unsupervised learning procedures for a particular inputs statistics and a particular range of computational tasks.

Quite a number of researchers have started to work on this approach, and a first special issue of a journal (Neural Networks) dedicated to this topic is currently assembled. Furthermore results of neurobiological experiments that test predictions of this approach have just been completed, and further experiments are currently in the planning stage.

The goals of this workshop are to

• provide a resume of the current state of knowledge, in particular regarding theory and results of firsts experimental tests of its predictions in neuroscience
• discuss consequences of this approach for computational and theoretical neuroscience
• to encourage new applications (e.g. in reinforcement learning, speech processing, handwriting recognition auditory processing)
• to guide future research by working out the essential open problems.

The target audience consists of neuroscientists, cognitive scientists, theoreticians, neural network researchers, and engineers.

Morning session: 7:30am–10:30am

• 7:30am Good vibrations: the issue of optimizing dynamical reservoirs, Herbert Jaeger, International University Bremen
• 7:55am Liquid State Machines: Review and new perspectives, Wolfgang Maass ( Slides of the talk)
• 8:20am How to design a reservoir or liquid for particular tasks, Jose C. Principe
• 8:45am coffee break and poster-viewing
• 9:15am Experimental tests of state-dependent networks, and plasticity within recurrent networks, Dean Buonomano, UCLA ( Slides of the talk)
• 9:40pm The brain as a liquid state machine, Danko Nikolic, Max-Planck Institute for Brain Research, Frankfurt ( Slides of the talk)
• 10:05pm Generating music sequences with an echo state network, Douglas Eck, University of Montreal, Canada

Afternoon session: 3:30pm–6:30pm

• 3:30pm Systematic quantification of memory capacity in recurrent neuronal networks, Olivia White, MIT
• 3:55pm Intrinsic plasticity for online reservoir computing, Jochen Steil, University of Bielefeld ( Slides of the talk)
• 4:20pm Linking histories with horizons: Modeling non-Markovian reinforcement learning domains with echo state networks, Keith Bush, Colorado State University (Slides of the talk see http://www.esn-lsm.tugraz.at/slides/nips06talk_bush.pdf)
• 4:45pm coffee break and poster-viewing
• 5:15pm An experimental comparison of reservoir computing methods, David Verstraeten, University of Gent, Belgium ( Slides of the talk)
• 5:40pm On implementing reservoir computing, Benjamin Schrauwen, University of Gent, Belgium ( Slides of the talk)
• 6:05am Modified ESN using lateral inhibition, Simon Haykin, McMaster University, Canada

Poster Presentations

• Computational consequences of lamina-specific structure in cortical microcircuit models, Stefan Haeusler, Wolfgang Maass, University of Technology, Graz

• Control of a system of partial differential equations with ESN, Danil Prokhorov, Toyota Tech Center

• Reservoir size reduction in echo state networks for classification problems, Xavier Dutoit, Hendrik Van Brussel, Marnix Nuttin, K.U.Leuven, Belgium

• Online reservoir learning of task movements and inverse kinematics for redundant robots, Jochen J. Steil, Univerity of Bielefeld, Germany

• Implementing the belief-propagation algorithm with networks of spiking neurons - an approach based on liquid-state-machines, Andreas Steimer, Uni/ETH-Zuerich, Switzerland

• Gait analysis of autistic children with echo-state networks, Basilio Noris, M. Nobile, L. Piccinini, M. Berti, E. Mani, M. Molteni, F. Keller, D. Campolo, A.G. Billard

• Learning grammatical structure with echo state networks, Matthew H. Tong, Adam D. Bickett, Eric M. Christiansen, Gary W. Cottrell

• Analyzing the framework of “Reservoir Computing” for hardware implementation, Arfan Ghani, T.M. McGinnity, L.P. Maguire, J.G. Harkin, University of Ulster, UK

• Reservoir shaping via reinforcement learning to improve liquid state machines, David Norton, Dan Ventura, Brigham Young University

• Is self-prediction a useful paradigm for echo state networks that are driven by robotic sensory input? Norbert Michael Mayer, Minoru Asada, Osaka University, Japan

• Exploiting iso-error pathways in the N,k-plane to improve echo state network performance, Keith A. Bush, Charles W. Anderson, Colorado State University

"Reservoir Computing" email list

If your are interested in communications within the budding "Reservoir Computing" community, consider subscribing to the reservoir computing email list. This list is independent of this NIPS workshop. This page will be part of a collaborative website for recurrent neural models after the workshop.

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