# Liquid State Machines: Motivation, Theory, and Applications

### Abstract:

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.