Abstract
Hopfield model with bipolar neural states (-1, 1), has an advantage of output signal to-noise ratio 2 times that of the unipolar neural states (0,1). Bipolar implementation is achieved by biasing the interconnection weights to yield a non-negative memory matrix. This added effect is removed from the output by an addition of distributed background and a dynamic thresholding. The output estimate of the bipolar neural network is derived with all its terms unipolarly expressed. We can use unipolar data to have for a bipolar like neural network. Hence the computing can be performed in a single channel system. Computer simulations have been performed for bipolar 2-D optical neural networks.
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