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Computing k-block Morphisms by Spiking Neural P Systems

100%

Nishida T. Y.

tom Vol. 111, nr 4

453-464

In this paper we show that for every k-block morphism (k ≥ 2) there is a spiking neural P system which computes the morphism. This result generalises the result explored by G. Paun, M. Perez-Jimenez, and G. Rozenberg. We give an algorithm which constructs the spiking neural P system, that is, if a k-block morphism is given as an input, the algorithm makes rules of the spiking neural P system which computes the morphism.

Spiking Neural dP Systems

100%

Ionescu M. , Paun G. , Pérez-Jiménez M. J. , Yokomori T.

tom Vol. 111, nr 4

423-436

We bring together two topics recently introduced in membrane computing, the much investigated spiking neural P systems (in short, SN P systems), inspired from the way the neurons communicate through spikes, and the dP systems (distributed P systems, with components which “read” strings from the environment and then cooperate in accepting their concatenation). The goal is to introduce SN dP systems, and to this aim we first introduce SN P systems with the possibility to input, at their request, spikes from the environment; this is done by so-called request rules. A preliminary investigation of the obtained SN dP systems (they can also be called automata) is carried out. As expected, request rules are useful, while the distribution in terms of dP systems can handle languages which cannot be generated by usual SN P systems. We always work with extended SN P systems; the non-extended case, as well as several other natural questions remain open.

Homogeneous Spiking Neural P Systems

80%

Zeng X. , Zhang X. , Pan L.

tom Vol. 97, nr 1/2

275-294

Spiking neural P systems are a class of distributed parallel computing models inspired from the way the neurons communicate with each other by means of electrical impulses (called "spikes"). In this paper, we consider a restricted variant of spiking neural P systems, called homogeneous spiking neural P systems, where each neuron has the same set of rules. The universality of homogeneous spiking neural P systems is investigated. One of universality results is that it is sufficient for homogeneous spiking neural P system to have only one neuron that behaves nondeterministically in order to achieve Turing completeness.

On String Languages Generated by Spiking Neural P Systems

80%

Chen H. , Freund R. , Ionescu M. , Paun G. , Pérez-Jiménez M. J.

tom Vol. 75, nr 1-4

141-162

We continue the study of spiking neural P systems by considering these computing devices as binary string generators: the set of spike trains of halting computations of a given system constitutes the language generated by that system. Although the "direct" generative capacity of spiking neural P systems is rather restricted (some very simple languages cannot be generated in this framework), regular languages are inverse-morphic images of languages of finite spiking neural P systems, and recursively enumerable languages are projections of inverse-morphic images of languages generated by spiking neural P systems.

Three Universal Homogeneous Spiking Neural P Systems Using Max Spike

80%

Păun A. , Sosík P.

tom Vol. 134, nr 1/2

167--182

We improve and extend a recent result showing that spiking neural P systems with the same rules in all neurons of the system (homogenous) and working in the max sequential manner are universal. The previous work in this area reported by the group led by Dr. Linqiang Pan did not put any bound on the number of neurons used. We believe this is an important question for any future practical implementation of such systems that deserves investigation, and we provide some results in this direction. Extending the aforementioned construction with the work of Korec on small register machines one could estimate the size of the previous construction at 105 neurons. We are able to improve this result and to show that an SNP system with 83 neurons having homogenous rules and working in the max sequential manner is universal. Several related results with respect to max-pseudo sequentiality mode are also obtained: 83 neurons are necessary for this case, too. When considering the case of systems without weighted synapses, we show that one needs at most 244 homogenous neurons for reaching universality in the max-pseudo sequentiality case.