On spiking neural p systems and partially blind counter machines

Authors:
Oscar H. Ibarra;Sara Woodworth;Fang Yu;Andrei Păun
Affiliations:
Department of Computer Science, University of California, Santa Barbara, CA;Department of Computer Science, University of California, Santa Barbara, CA;Department of Computer Science, University of California, Santa Barbara, CA;Department of Computer Science/IfM, Louisiana Tech University, Ruston, LA
Venue:
UC'06 Proceedings of the 5th international conference on Unconventional Computation
Year:
2006

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Abstract

A k-output spiking neural P system (SNP) with output neurons, O1, ..., Ok, generates a tuple (n1, ..., nk) of positive integers if, starting from the initial configuration, there is a sequence of steps such that during the computation, each Oi generates exactly two spikes a a (the times the pair a a are generated may be different for different output neurons) and the time interval between the first a and the second a is ni. After the output neurons generate their pairs of spikes, the system eventually halts. We give characterizations of sets definable by partially blind multicounter machines in terms of k-output SNPs operating in a sequential mode. Slight variations of the models make them universal.