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Abstract

A processing element utilizing the learning algorithm W.sub.i =W.sub.i-1 +A.sub.i where A.sub.i is one of .eta.*(X.sub.i -W.sub.i-1), +1, -1, and 0, and where: W.sub.i =a weight, W.sub.i-1 =a previous weight, .eta.=a plasticity signal, and X.sub.i =an input signal. A method of generating adaptive weight adjustments, A.sub.i, including generating .eta.*(X.sub.i -W.sub.i-1) and additional least significant bits of data representative of the term .eta.*(X.sub.i -W.sub.i-1). Comparing the additional least significant bits of data to a random number and providing >, = and <, comparison signals. Adding one of .eta.*(X.sub.i -W.sub.i-1), +1, -1 and 0, respectively, to the term W.sub.i-1 when one of the following occurs: at least one bit of .eta.*(X.sub.i -W.sub.i-1) equals one; .eta.*(X.sub.i -W.sub.i-1) equals zero, the comparison signal is < and the sign bit is +; .eta.* (X.sub.i -W.sub.i-1) equals zero, the comparison signal is < and the sign bit is -; or .eta.*(X.sub.i -W.sub.i-1) equals zero and the comparison signal is > or =.

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