Liquid State Machines: Motivation, Theory, and Applications
The Liquid State Machine (LSM) has emerged as a computational model that is
more adequate than the Turing machine for describing computations in
biological networks of neurons. Characteristic features of this new model are
(i) that it is a model for adaptive computational systems, (ii) that it
provides a method for employing randomly connected circuits, or eve "found"
physical objects for meaningful computations, (iii) that it provides a
theoretical context where heterogeneous, rather than stereotypical, local
gates or processors increase the computational power of a circuit, (iv) that
it provides a method for multiplexing different computations (on a common
input) within the same circuit. This chapter reviews the motivation for this
model, its theoretical background, and current work on implementations of
this model in innovative artificial computing devices.
Reference: W. Maass.
Liquid state machines: Motivation, theory, and applications.
In B. Cooper and A. Sorbi, editors, Computability in Context: Computation
and Logic in the Real World, pages 275-296. Imperial College Press, 2010.