Spiking neural nets with symbolic internal state

A type of spiking neural net in which the internal state functions of the neurons are represented symbolically rather than numerically is described. This makes possible subtle forms of short term memory, but also raises both computational and mathematical problems. It is shown how interval arithmetic can be used for event driven simulation. Computational times are given for some tests of an implementation resulting from these ideas. Applications are briefly discussed.