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, USA 93106;Department of Computer Science, University of California, Santa Barbara, USA 93106;Department of Computer Science, University of California, Santa Barbara, USA 93106;Department of Computer Science/IfM, Louisiana Tech University, Ruston, USA 71272
Venue:
Natural Computing: an international journal
Year:
2008

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Abstract

A k-output spiking neural P system (SNP) with output neurons, $${{O_1},\ldots{,{O_k}}}$$ , generates a tuple $${({n_1},\ldots{,{n_k}})}$$ of positive integers if, starting from the initial configuration, there is a sequence of steps such that during the computation, each O i generates exactly two spikes aa (the times the pair aa are generated may be different for different output neurons) and the time interval between the first a and the second a is n i . 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.