Neuromorphic computing with nanoscale spintronic oscillators

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Real-time computing without stable states: a new framework for neural computation based on perturbations.

Wolfgang Maass, Thomas Natschläger, Henry Markram (2002)

A key challenge for neural modeling is to explain how a continuous stream of multimodal 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 computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. It does not require a task-dependent construction of neural circuits. Instead, it is based on principles of high-dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry. It is shown that the inherent transient dynamics of the high-dimensional dynamical system formed by a sufficiently large and heterogeneous neural circuit may serve as universal analog fading memory. Readout neurons can learn to extract in real time from the current state of such recurrent neural circuit information about current and past inputs that may be needed for diverse tasks. 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 well-defined discrete internal states. It is supported, as the Turing machine is, by rigorous mathematical results that predict universal computational power under idealized conditions, but for the biologically more realistic scenario of real-time processing of time-varying inputs. Our approach provides new perspectives for the interpretation of neural coding, the design of experiments and data analysis in neurophysiology, and the solution of problems in robotics and neurotechnology.

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Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication.

Herbert Jaeger, Harald Haas (2004)

We present a method for learning nonlinear systems, echo state networks (ESNs). ESNs employ artificial recurrent neural networks in a way that has recently been proposed independently as a learning mechanism in biological brains. The learning method is computationally efficient and easy to use. On a benchmark task of predicting a chaotic time series, accuracy is improved by a factor of 2400 over previous techniques. The potential for engineering applications is illustrated by equalizing a communication channel, where the signal error rate is improved by two orders of magnitude.

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Is Open Access

Microwave Oscillations of a Nanomagnet Driven by a Spin-Polarized Current

D C Ralph, R. J. Schoelkopf, N C Emley … (2003)

We describe direct electrical measurements of microwave-frequency dynamics in individual nanomagnets that are driven by spin transfer from a DC spin-polarized current. We map out the dynamical stability diagram as a function of current and magnetic field, and we show that spin transfer can produce several different types of magnetic excitations, including small-angle precession, a more complicated large-angle motion, and a high-current state that generates little microwave signal. The large-angle mode can produce a significant emission of microwave energy, as large as 40 times the Johnson-noise background.