Energy Conversion Devices, Inc.

Stanford R Ovhsinsky

Srinivasan Venkatesan

Subhash K Dhar

Boyko Aladjov

Kevin Fok

Thomas J Hopper

An analog neural computing medium, neuron and neural networks are disclosed. The neural computing medium includes a phase change material that has the ability to cumulatively respond to multiple input signals. Input signals induce transformations among a plurality of accumulation states of the disclosed neural computing medium. The accumulation states are characterized by a high electrical resistance. Upon cumulative receipt of energy from one or more input signals that equals or exceeds a threshold value, the neural computing medium fires by transforming to a low resistance state. The disclosed neural computing medium may also be configured to perform a weighting function whereby it weights incoming signals. The disclosed neurons may also include activation units for further transforming signals transmitted by the accumulation units according to a mathematical operation. The artificial neurons, weighting units, accumulation units and activation units may be connected to form artificial neural networks.

Analog neurons and neurosynaptic networks

Fuel cell oxygen electrode and instant startup fuel cells employing such oxygen electrode. The oxygen electrode operates through the mechanism of redox couples which uniquely provide multiple degrees of freedom in selecting the operating voltages available for such fuel cells. Such oxygen electrodes provide the fuel cells in which they are used a &ldquo;buffer&rdquo; or &ldquo;charge&rdquo; of oxidizer available within the oxygen electrode at all times.

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Bloomfield Hills, MI, United States of America

Stanford R Ovhsinsky