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Membrane Computing. An introduction. Chinese Edition

Fri, 11/16/2012 - 15:56 — manu

Paun, G. (2012). Membrane Computing. An introduction. Chinese Edition. iii+391.

Time-free solution to SAT problem using P systems with active membranes

Wed, 11/27/2013 - 22:07 — manu

Song, T., Macías-Ramos L. F., Pan L., & Pérez-Jiménez M. J. (2013). Time-free solution to SAT problem using P systems with active membranes. Theoretical Computer Science.

Sevilla Carpets Revisited: Enriching Membrane Computing Toolbox

Sun, 11/16/2014 - 17:24 — ariscosn

Orellana-Martín, D., Graciani-Díaz C., Macías-Ramos L. F., Martínez-del-Amor M. A., Riscos-Núñez A., Romero-Jiménez Á., et al. (2014). Sevilla Carpets Revisited: Enriching Membrane Computing Toolbox. Fundamenta Informaticae. 134, 153–166.

Sequential spiking neural P systems with structural plasticity based on max/min spike number

Thu, 03/17/2016 - 18:38 — manu

Cabarle, F. G. C., Adorna H. N., & Pérez-Jiménez M. J. (2015). Sequential spiking neural P systems with structural plasticity based on max/min spike number. Neural Computing and Applications. 1-11.

P Systems with Active Cells

Fri, 10/06/2017 - 12:14 — dorellana

Orellana-Martín, D. (2017). P Systems with Active Cells. Fifteenth Brainstorming Week on Membrane Computing (BWMC2017). 175-189.

A path to computational efficiency through membrane computing

Tue, 07/10/2018 - 16:00 — dorellana

Orellana-Martín, D., Valencia-Cabrera L., Riscos-Núñez A., & Pérez-Jiménez M. J. (2018). A path to computational efficiency through membrane computing. Theoretical Computer Science.

Dependency Graph Technique Revisited

Mon, 10/21/2019 - 18:27 — mmartinez

Valencia-Cabrera, L., Orellana-Martín D., Pérez-Hurtado I., & Pérez-Jiménez M. J. (2019). Dependency Graph Technique Revisited. 20th International Conference on Membrane Computing, CMC20. 513-522.

Simulation of Spiking Neural P Systems with Sparse Matrix-Vector Operations

Mon, 05/03/2021 - 11:54 — mmartinez

Martínez-del-Amor, M. A., Orellana-Martín D., Pérez-Hurtado I., Cabarle F. G. C., & Adorna H. N. (2021). Simulation of Spiking Neural P Systems with Sparse Matrix-Vector Operations. Processes. 9(4),

All NP-Problems Can Be Solved in Polynomial Time by Accepting Networks of Splicing Processors of Constant Size

We present several results concerning the computational power and complexity of Accepting Networks of Splicing Processors (ANSP). We show that every recursively enumerable language can be accepted by an ANSP of size 7 out of which 6 do not depend on the given language. Then we propose a method for constructing, given an NP-language, an ANSP of size 7 accepting that language in polynomial time. Unlike the previous case, all nodes of this ANSP depend on the given language.

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Topics in general recursion and interactive theorem proving

Prof. Dr. Vladimir Komendantsky

INRIA Sophia Antipolis, France

Abstract:

In my talk I briefly introduce a powerful proof assistant Coq, one of the most widely used automated
theorem proving tools today.
I illustrate the uses of Coq by providing several methods allowing to incorporate general recursive
(e.g., potentially non-terminating) recursive functions inside the underlying theory of this proof assistant.

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