Emulation of finite state automata with networks of synfire rings

We propose a novel paradigm of neural computation based on synfire rings, i.e., synfire chains that loop back in on themselves. We show that any finite state automaton can be simulated by a Boolean recurrent neural network made up of synfire rings. More precisely, if the given automaton and its corresponding network are run in parallel on a same input stream, then the successive computational states of the automaton are perfectly reflected by the consecutive sustained activities of the network's synfire rings. Our construction turns out to be robust with respect to the removal of a number of connections. These considerations support the idea that a robust paradigm of neural computation based on sustained activities of cell assemblies is indeed possible.