paper on computation with spike trains

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A preprint of the following paper is now online available:

           REAL-TIME COMPUTING WITHOUT STABLE STATES:

 A NEW FRAMEWORK FOR NEURAL COMPUTATION BASED ON PERTURBATIONS
                               

                           by

   Wolfgang Maass, Thomas Natschläger, and Henry Markram.
         
(Graz Univ. of Technology, Austria, and Weizmann Institute, Israel)

ABSTRACT:

This paper has the goal to establish a theoretical framework for
computations on spike trains that can also be applied to biologically
realistic models for recurrent circuits of spiking neurons. This new
theoretical framework, the liquid state machine, differs strongly from
the computational models that have emerged from computer science and
artificial neural networks: it is not based on transitions between
stable
internal states or attractors, but rather exploits the natural transient
dynamics of recurrent neural circuits as a potentially powerful analog
memory device. It directs attention to the investigation of trajectories
of transient internal states in very high dimensional dynamical systems,
thereby providing a complement to the analysis of attractors in
low dimensional dynamical systems that have so far been used as primary
sources of inspiration for understanding the dynamics of neural
computation.

Like the Turing machine this model allows for basically unlimited
computational power under idealized conditions, but for real-time
computing
on time-varying inputs --such as spike trains-- with
fading memory (rather than for offline-computing on static discrete
inputs like the Turing machine).

Based on this new framework we have for the first time been able to
carry out complex real-time computations on spike trains with
biologically realistic computer models of neural microcircuits.

This approach also suggests a radically new approach towards
neuromorphic engineering: Look directly for efficient hardware
implementations
of adaptive liquid state machines in order to build devices for
real-time
processing of sensory inputs that capture aspects of the organisation of
neural
computation.

Learning issues in the context of this model (especially biologically
plausible algorithms for unsupervised learning and applications of
reinforcement learning) are topics of current research.

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This paper is online available (PDF, 243 KB) as # 130 from

http://www.igi.tugraz.at/maass/publications.html

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