An analog neural computing medium, neuron and neural networks comprising same are disclosed. The neural computing medium includes a phase change material that has the ability to cumulatively respond to multiple synchronous or asynchronous input signals. The introduction of input signals induces transformations among a plurality of accumulation states of the disclosed neural computing medium. The accumulation states are characterized by a high electrical resistance that is substantially identical for all accumulation states. The high electrical resistance prevents the neural computing medium from transmitting signals. 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 that is capable of transmitting signals. The neural computing medium thus closely mimics the neurosynaptic function of a biological neuron. The disclosed neural computing medium may also be configured to perform a weighting function whereby it weights incoming signals and transmits modified signals. The neural computing medium may thus be configured to provide an accumulation function or weighting function and may readily be reconfigured from one function to the other. 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 in a variety of ways to form artificial neural networks. Embodiments of several neural networks are disclosed.
Analog neurons and neurosynaptic networks