Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 29, Issue 8, Pages 3349-3360Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2017.2726119
Keywords
Bio-inspired computing; membrane computing; polarization; spiking neural P (SN P) system; Turing completeness
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Funding
- National Natural Science Foundation of China [61320106005, 61033003]
- Ph.D. Programs Foundation of Ministry of Education of China [20120142130008]
- Innovation Scientists and Technicians Troop Construction Projects of Henan Province [154200510012]
- UEFSCDI Project Remote Forest [PN-II-PTPCCA-2011-3.2-1710]
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Spiking neural P (SN P) systems are a class of parallel computation models inspired by neurons, where the firing condition of a neuron is described by a regular expression associated with spiking rules. However, it is NP-complete to decide whether the number of spikes is in the length set of the language associated with the regular expression. In this paper, in order to avoid using regular expressions, two major and rather natural modifications in their form and functioning are proposed: the spiking rules no longer check the number of spikes in a neuron, but, in exchange, a polarization is associated with neurons and rules, one of the three electrical charges -, 0,+. Surprisingly enough, the computing devices obtained are still computationally complete, which are able to compute all Turing computable sets of natural numbers. On this basis, the number of neurons in a universal SN P system with polarizations is estimated. Several research directions are mentioned at the end of this paper.
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