Abstract: An information processing system includes a feed-forward neural net that is functionally comprised of a plurality of interconnected neurons. At least a particular one of the neurons is operative to receive a plurality of input signals for in response thereto producing an output signal that is a function of the input signals, and of at least one of following quantities: a rate of change of at least one of the neuron input signals; a rate of change of the particular neuron output signal. The net exhibits dynamic behavior as represented by signal-dependent delays, signal dispersion and phase shifts. The net can be trained to display stimulus-response characteristics of substantially any dynamic system.

Abstract: A neural processor, comprising neural calculation apparatus (30, NQ, RQ) which extracts a root Q of a quantity X, said root constituting either a norm of a data or a distance between data. The calculation apparatus calculates (30) by iteration a series of contributions .DELTA.Q.sub.i which are used (NQ, RQ) to update a partial root QP which becomes the root Q at the end of calculation. The calculation can be performed on an arbitrary arithmetic base which determines the number of neurons utilized and also the accuracy of calculation. It is possible to execute the calculation of a partial remainder (NR, RR). Several programming modes are presented.

Abstract: A neural processor, comprising neural calculation apparatus (30, NQ, RQ) which extracts a root Q of a quantity X, said root constituting either a norm of a data or a distance between data. The calculation apparatus calculates (30) by iteration a series of contributions .DELTA.Q.sub.i which are used (NQ, RQ) to update a partial root QP which becomes the root Q at the end of calculation. The calculation can be performed on an arbitrary arithmetic base which determines the number of neurons utilized and also the accuracy of calculation. It is also possible to execute the calculation of a partial remainder (NR, RR). Several programming modes are presented.