By extending the pulsed recurrent random neural network (RNN) dis- cussed in Gelenbe (1989, 1990, 1991), we propose a recurrent random neu- ral network model in which each neuron processes several distinctly char- acterized streams of "signals" or data. The idea that neurons may be able to distinguish between the pulses they receive and use them in a distinct manner is biologically plausible. In engineering applications, the need to process different streams of information simultaneously is commonplace (e.g., in image processing, sensor fusion, or parallel processing systems).
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