Journal
NEURAL COMPUTING & APPLICATIONS
Volume 24, Issue 7-8, Pages 1833-1841Publisher
SPRINGER
DOI: 10.1007/s00521-013-1397-8
Keywords
Membrane computing; Spiking neural P system; Homogenous system; Anti-spike; Turing completeness
Categories
Ask authors/readers for more resources
Spiking neural P systems with anti-spikes (ASN P systems, for short) are a class of neural-like computing models in membrane computing, which are inspired by neurons communication through both excitatory and inhibitory impulses (spikes). In this work, we consider a restricted variant of ASN P systems, called homogeneous ASN P systems, where any neuron has the same set of spiking and forgetting rules. As a result, we prove that such systems can achieve Turing completeness. Specifically, it is proved that two categories of pure form of spiking rules (for a spiking rule, if the language corresponding to the regular expression that controls its application is exactly the form of spikes consumed by the rule, then the rule is called pure) are sufficient to compute and accept the family of sets of Turing computable natural numbers.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available