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 engineering.

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.