Real-time Computing Without Stable States: A New Framework for Neural
Computation Based on Perturbations
W. Maass, T. Natschlaeger, and H. Markram
A key challenge for neural modeling is to explain how a continuous stream of
multi-modal input from a rapidly changing environment can be processed by
stereotypical recurrent circuits of integrate-and-fire neurons in real-time.
We propose a new framework for neural computation that provides an
alternative to previous approaches based on attractor neural networks. It is
shown that the inherent transient dynamics of the high-dimensional dynamical
system formed by a neural circuit may serve as a universal source of
information about past stimuli, from which readout neurons can extract
particular aspects needed for diverse tasks in real-time. Stable internal
states are not required for giving a stable output, since transient internal
states can be transformed by readout neurons into stable target outputs due
to the high dimensionality of the dynamical system. Our approach is based on
a rigorous computational model, the liquid state machine, that unlike Turing
machines, does not require sequential transitions between discrete internal
states. Like the Turing machine paradigm it allows for universal
computational power under idealized conditions, but for real-time processing
of time-varying input. The resulting new framework for neural computation has
novel implications for the interpretation of neural coding, for the design of
experiments and data-analysis in neurophysiology, and for neuromorphic
Reference: W. Maass, T. Natschlaeger, and H. Markram.
Real-time computing without stable states: A new framework for neural
computation based on perturbations.
Neural Computation, 14(11):2531-2560, 2002.