Institute of Advanced Study

Stephen L Adler

A neural network architecture has phase-coherent alternating current neural input signals. Each input v.sub.k.sup.in is a two-phase pair of signals 180 degrees out of phase. Capacitive coupling of both signals of n input pairs to a summation line gives a non-dissipative realization of the weighted sum ##EQU1## with general real neural weights w.sub.ik. An alternating current offset signal proportional to u.sub.i is also capacitively coupled to the summation line. The signal on the summation line is passed through a low input capacitance follower/amplifier, a rectifier and a filter, producing a direct current signal proportional to the magnitude ##EQU2## This signal is compared with a direct current threshold proportional to t.sub.i, and the resultant is used to gate a two-phase alternating current output signal. The output is therefore functionally related to the inputs by ##EQU3## with .theta. the Heaviside step function. This generalized neuron can directly compute the 'Exclusive Or' (XOR) logical operation. Alternative forms of the alternating current neuron using phase-shifters permit complex number inputs, outputs and neural weightings.

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Stephen L Adler