42 research outputs found
Spiking Neural P Systems with Functional Astrocytes
Spiking Neural P Systems (SN P Systems, for short) is a
developing field within the universe of P Systems. New variants arise
constantly as the study of their properties, such as computational
completeness and computational efficiency, grows. Variants frequently
incorporate new ingredients into the original model inspired by real
neurophysiological structure of the brain. A singular element present
within that structure is the astrocyte. Astrocytes, also known collectively
as astroglia, are characteristic star-shaped glial cells in the brain and
spinal cord. In this paper, a new variant of Spiking Neural P Systems
incorporating astrocytes is introduced. These astrocytes are modelled
as computing devices capable of performing function computation in a
single computation step. In order to experimentally study the action of
Spiking Neural P Systems with astrocytes, it is necessary to develop
software providing the required simulation tools. Within this trend, P–
Lingua offers a standard language for the definition of P Systems. Part
of the same software project, pLinguaCore library provides particular
implementations of parsers and simulators for the models specified in
P–Lingua. Along with the new SN P System variant with astrocytes, an
extension of the P–Lingua language allowing definition of these systems is
presented in this paper, as well as an upgrade of pLinguaCore, including
a parser and a simulator that supports the aforementioned variant.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08-TIC-0420
A new P-Lingua toolkit for agile development in membrane computing
Membrane computing is a massively parallel and non-deterministic bioinspired computing paradigm whose models are called P systems. Validating and testing such models is a challenge which is being overcome by developing simulators. Regardless of their heterogeneity, such simulators require to read and interpret the models to be simulated. To this end, P-Lingua is a high-level P system definition language which has been widely used in the last decade. The P-Lingua ecosystem includes not only the language, but also libraries and software tools for parsing and simulating membrane computing models. Each version of P-Lingua supported new types or variants of P systems. This leads to a shortcoming: Only a predefined list of variants can be used, thus making it difficult for researchers to study custom ones. Moreover, derivation modes cannot be user-defined, i.e, the way in which P system computations should be generated is determined by the simulation algorithm in the source code.
The main contribution of this paper is a completely new design of the P-Lingua language, called P-Lingua 5, in which the user can define custom variants and derivation modes, among other improvements such as including procedural programming and simulation directives. It is worth mentioning that it has backward-compatibility with previous versions of the language. A completely new set of command-line tools is provided for parsing and simulating P-Lingua 5 files. Finally, several examples are included in this paper covering the most common P system types.Agencia Estatal de Investigación TIN2017-89842-
The Computational Complexity of Tissue P Systems with Evolutional Symport/Antiport Rules
Tissue P systems with evolutional communication (symport/antiport) rules are computational models inspired by biochemical
systems consisting of multiple individuals living and cooperating in a certain environment, where objects can be modified when
moving from one region to another region. In this work, cell separation, inspired from membrane fission process, is introduced in
the framework of tissue P systems with evolutional communication rules.The computational complexity of this kind of P systems
is investigated. It is proved that only problems in class P can be efficiently solved by tissue P systems with cell separation with
evolutional communication rules of length at most (��, 1), for each natural number �� ≥ 1. In the case where that length is upper
bounded by (3, 2), a polynomial time solution to the SAT problem is provided, hence, assuming that P ̸= NP a new boundary
between tractability and NP-hardness on the basis of the length of evolutional communication rules is provided. Finally, a new
simulator for tissue P systems with evolutional communication rules is designed and is used to check the correctness of the solution
to the SAT problem
Population Dynamics P Systems on CUDA
Population Dynamics P systems (PDP systems, in short)
provide a new formal bio-inspired modeling framework, which has
been successfully used by ecologists. These models are validated using
software tools against actual measurements. The goal is to use P systems
simulations to adopt a priori management strategies for real ecosystems.
Software for PDP systems is still in an early stage. The simulation
of PDP systems is both computationally and data intensive for large
models. Therefore, the development of efficient simulators is needed for
this field. In this paper, we introduce a novel simulator for PDP systems
accelerated by the use of the computational power of GPUs. We discuss
the implementation of each part of the simulator, and show how to
achieve up to a 7x speedup on a NVIDA Tesla C1060 compared to an
optimized multicore version on a Intel 4-core i5 Xeon for large systems.
Other results and testing methodologies are also included.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08-TIC-0420
Membrane Computing for Real Medical Image Segmentation
In this paper, membrane-based computing image segmentation, both region-based and edge-based, is proposed for medical images that involve two types of neighborhood relations between pixels. These neighborhood relations—namely, 4-adjacency and 8-adjacency of a membrane computing approach—construct a family of tissue-like P systems for segmenting actual 2D medical images in a constant number of steps; the two types of adjacency were compared using different hardware platforms. The process involves the generation of membrane-based segmentation rules for 2D medical images. The rules are written in the P-Lingua format and appended to the input image for visualization. The findings show that the neighborhood relations between pixels of 8-adjacency give better results compared with the 4-adjacency neighborhood relations, because the 8-adjacency considers the eight pixels around the center pixel, which reduces the required communication rules to obtain the final segmentation results. The experimental results proved that the proposed approach has superior results in terms of the number of computational steps and processing time. To the best of our knowledge, this is the first time an evaluation procedure is conducted to evaluate the efficiency of real image segmentations using membrane computing