Implementation of the Hopfield decomposition and DFT into analog hardware

Summary form only given, as follows. A 256-neuron, fully interconnected neural net breadboard has been constructed of off-the-shelf components. To date, it has been used to test a 128-node discrete Fourier transform (DFT) feedforward-type net and a 16-neuron Gaussian decomposition, Hopfield-type network. The DFT feedforward net has a settling time of 100 mu s, dominated by the characteristics of the operational amplifier used. The decomposition network had a settling time of 150 mu s, which is 1.5 times the feedforward loop time. A formalism has been identified for setting the optimal local (gain-determining) feedback resistance. Using this technique, ´false minima´ solutions are avoided completely. Also, unlike the Tank and Hopfield result, the ´gain´ is not run in the high gain limit. This allows the magnitude of the input function to be deduced.<>