@article {2477,
	title = {A Framework for Evolving Spiking Neural P Systems},
	journal = {International Journal of Unconventional Computing},
	volume = {16},
	year = {2021},
	pages = {121-139},
	url = {https://www.oldcitypublishing.com/journals/ijuc-home/ijuc-issue-contents/ijuc-volume-16-number-2-3-2021/},
	author = {L.J. Casauay and I.C. Macababayao and F.G. Cabarle and R.T. de la Cruz and Henry Adorna and XiangXiang Zeng and Miguel {\'A}. Mart{\'\i}nez-del-Amor}
}
@article {876,
	title = {Improving GPU Simulations of Spiking Neural P Systems},
	journal = {Romanian Journal of Information Science and Technology},
	volume = {15},
	year = {2012},
	month = {06/2012},
	pages = {5-20},
	publisher = {EDITURA ACADEMIEI ROM{\^A}NE},
	edition = {Bio-Inspired Computing {\textendash} Theory and Applications (BIC-TA) - Selected papers},
	address = {Bucure{\c s}ti, Rom{\^a}nia},
	abstract = {In this work we present further extensions and improvements
of a Spiking Neural P system (for short, SNP systems) simulator on graphics
processing units (for short, GPUs). Using previous results on representing SNP
system computations using linear algebra, we analyze and implement a compu-
tation simulation algorithm on the GPU. A two-level parallelism is introduced
for the computation simulations. We also present a set of benchmark SNP sys-
tems to stress test the simulation and show the increased performance obtained
using GPUs over conventional CPUs. For a 16 neuron benchmark SNP system
with 65536 nondeterministic rule selection choices, we report a 2.31 speedup of
the GPU-based simulations over CPU-based simulations.
},
	keywords = {CUDA, GPU Computing, Membrane computing, spiking neural network, spiking neural P systems},
	issn = {1453-8245},
	url = {http://www.imt.ro/romjist/Volum15/Number15_1/cuprins15_1.htm},
	author = {Francis Cabarle and Henry Adorna and Miguel A. Mart{\'\i}nez-del-Amor and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {860,
	title = {On the Simulations of Evolution-Communication P Systems with Energy without Antiport Rules for GPUs},
	journal = {Tenth Brainstorming Week on Membrane Computing},
	volume = {I},
	year = {2012},
	month = {02/2012},
	pages = {267-290},
	publisher = {F{\'e}nix Editora},
	address = {Seville, Spain},
	abstract = {In this report, we present our initial proposal on simulating computations on
a restricted variant of Evolution-Communication P system with energy (ECPe system)
which will then be implemented in Graphics Processing Units (GPUs). This ECPe sys-
tems variant prohibits the use of antiport rules for communication. Several possible levels
of parallelizations for simulating ECPe systems computations on GPUs are emphasized.
Our work is based on a localized matrix representation for the mentioned variant given
in a previous literature. Our proposal employs a methodology for forward computing also
discussed in the said literature.},
	keywords = {GPU Computing, Membrane computing, parallel computing},
	url = {http://www.gcn.us.es/10BWMC/10BWMCvolI/papers/ecpe-bwmc10.pdf},
	author = {Richelle A.B. Juayong and Francis Cabarle and Henry Adorna and Miguel A. Mart{\'\i}nez-del-Amor}
}
@article {826,
	title = {A Spiking Neural P System Simulator Based on CUDA},
	journal = {Lecture Notes in Computer Science},
	volume = {7184},
	year = {2012},
	pages = {87-103},
	publisher = {Springer Verlag ProBusiness Berlin},
	abstract = {In this paper we present a Spiking Neural P system (SNP system) simulator based on graphics processing units (GPUs). In particular we implement the simulator using NVIDIA CUDA enabled GPUs. The massively parallel architecture of current GPUs is very suitable for the maximally parallel computations of SNP systems. We simulate a wider variety of SNP systems, after presenting a previous work on SNP system matrix representation which led to their simulation in GPUs, and the simulation algorithm included here. Finally, we compare and present the performance speedups of the CPU-GPU based simulator over the CPU only simulator. },
	isbn = {978-3-642-28023-8},
	doi = {10.1007/978-3-642-28024-5_8},
	url = {http://www.springerlink.com/content/978-3-642-28023-8$\#$section=1017938\&page=1},
	author = {Francis Cabarle and Henry Adorna and Miguel A. Mart{\'\i}nez-del-Amor}
}
@article {802,
	title = {Spiking Neural P System Simulations on a High Performance GPU Platform},
	journal = {Lecture Notes in Computer Science},
	volume = {7017},
	year = {2011},
	note = {Algorithms and Architectures for Parallel Processing
ICA3PP Workshops, (ADCN 2011)},
	month = {10/2011},
	pages = {99-108},
	publisher = {Springer},
	edition = {ICA3PP 2011 Workshops, Part II},
	address = {Amsterdam, The Netherlands},
	abstract = {In this paper we present our results in adapting a Spiking Neural P system (SNP system) simulator to a high performance graphics
processing unit (GPU) platform. In particular, we extend our simulations to larger and more complex SNP systems using an NVIDIA Tesla C1060
GPU. The C1060 is manufactured for high performance computing and massively parallel computations, matching the maximally parallel nature of SNP systems. Using our GPU accelerated simulations we present speedups of around 200{\texttimes} for some SNP systems, compared to CPU only
simulations.
},
	isbn = {978-3-642-24668-5},
	issn = {0302-9743},
	doi = {10.1007/978-3-642-24669-2_10},
	url = {http://www.springerlink.com/content/f490qnv027884g27/},
	author = {Francis Cabarle and Henry Adorna and Miguel A. Mart{\'\i}nez-del-Amor and Mario J. P{\'e}rez-Jim{\'e}nez},
	editor = {Yang Xiang and Alfredo Cuzzocrea and Michael Hobbs and Wanlei Zhou}
}
@article {795,
	title = {An Improved GPU Simulator for Spiking Neural P Systems},
	journal = {Sixth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)},
	year = {2011},
	month = {09/2011},
	pages = {262 - 267},
	address = {Penang, Malaysia},
	abstract = {Spiking Neural P (SNP) systems, variants of Psystems (under Membrane and Natural computing), are computing models that acquire abstraction and inspiration from the way neurons {\textquoteright}compute{\textquoteright} or process information. Similar to other P system variants, SNP systems are Turing complete models that by nature compute non-deterministically and in a maximally parallel manner. P systems usually trade (often exponential) space for (polynomial to constant) time. Due to this nature, P system variants are currently limited to parallel simulations, and several variants have already been simulated in parallel devices. In this paper we present an improved SNP system simulator based on graphics processing units (GPUs). Among other reasons, current GPUs are architectured for massively parallel computations, thus making GPUs very suitable for SNP system simulation. The computing model, hardware/software considerations, and simulation algorithm are presented, as well as the comparisons of the CPU only and CPU-GPU based simulators.},
	isbn = {978-1-4577-1092-6},
	doi = {10.1109/BIC-TA.2011.37 },
	url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6046910},
	author = {Francis Cabarle and Henry Adorna and Miguel A. Mart{\'\i}nez-del-Amor}
}
@article {787,
	title = {A Spiking Neural P system simulator based on CUDA},
	journal = {12th International Conference on Membrane Computing (CMC12)},
	year = {2011},
	month = {08/2011},
	pages = {77-92},
	address = {Fontainebleau, France},
	abstract = {In this paper we present a Spiking Neural P system (SNP
system) simulator based on graphics processing units (GPUs). In particular
we implement the simulator using NVIDIA CUDA enabled GPUs.
The massively parallel architecture of current GPUs is very suitable for
the maximally parallel computations of SNP systems. We simulate a
wider variety of SNP systems, after presenting a previous work on SNP
system matrix representation which led to their simulation in GPUs, and
the simulation algorithm included here. Finally, we compare and present
the performance speedups of the CPU-GPU based simulator over the
CPU only simulator.},
	keywords = {CUDA, GPU Computing, Membrane computing, parallel computing, spiking neural P systems},
	url = {http://cmc12.lacl.fr/cmc12proceedings.pdf},
	author = {Francis Cabarle and Henry Adorna and Miguel A. Mart{\'\i}nez-del-Amor}
}
@article {738,
	title = {Spiking neural P system without delay simulator implementation using GPGPUs},
	journal = {Eleventh Philippine Computing Science Congress},
	year = {2011},
	month = {03/2011},
	pages = {35-43},
	publisher = {Computing Society of the Philippines},
	address = {Naga, Philippines},
	issn = {1908-1146},
	url = {https://sites.google.com/a/dcs.upd.edu.ph/csp-proceedings/pcsc2011},
	author = {Francis Cabarle and Henry Adorna and Miguel A. Mart{\'\i}nez-del-Amor}
}
@article {762,
	title = {Simulating Spiking Neural P Systems Without Delays Using GPUs},
	journal = {Ninth Brainstorming Week on Membrane Computing},
	year = {2011},
	month = {02/2011},
	pages = {23-42},
	publisher = {F{\'e}nix Editora},
	address = {Seville, Spain},
	abstract = {We present in this paper our work regarding simulating a type of P sys-
tem known as a spiking neural P system (SNP system) using graphics processing units
(GPUs). GPUs, because of their architectural optimization for parallel computations,
are well-suited for highly parallelizable problems. Due to the advent of general purpose
GPU computing in recent years, GPUs are not limited to graphics and video processing
alone, but include computationally intensive scientic and mathematical applications as
well. Moreover P systems, including SNP systems, are inherently and maximally parallel
computing models whose inspirations are taken from the functioning and dynamics of a
living cell. In particular, SNP systems try to give a modest but formal representation of
a special type of cell known as the neuron and their interactions with one another. The
nature of SNP systems allowed their representation as matrices, which is a crucial step
in simulating them on highly parallel devices such as GPUs. The highly parallel nature
of SNP systems necessitate the use of hardware intended for parallel computations. The
simulation algorithms, design considerations, and implementation are presented. Finally,
simulation results, observations, and analyses using an SNP system that generates all
numbers in N - 1 are discussed, as well as recommendations for future work.},
	url = {http://www.gcn.us.es/9BWMC/volume/02snp-gpu.pdf},
	author = {Francis Cabarle and Henry Adorna and Miguel A. Mart{\'\i}nez-del-Amor}
}
@article {727,
	title = {Matrix representation of Spiking Neural P systems},
	journal = {Lecture Notes in Computer Science},
	volume = {6501},
	year = {2011},
	pages = {377-392},
	publisher = {Springer},
	address = {Amsterdam, The Netherlands},
	abstract = {Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes. In this work, a discrete structure representation of SN P systems with extended rules and without delay is proposed. Specifically, matrices are used to represent SN P systems. In order to represent the computations of SN P systems by matrices, configuration vectors are defined to monitor the number of spikes in each neuron at any given configuration; transition net gain vectors are also introduced to quantify the total amount of spikes consumed and produced after the chosen rules are applied. Nondeterminism of the systems is assured by a set of spiking transition vectors that could be used at any given time during the computation. With such matrix representation, it is quite convenient to determine the next configuration from a given configuration, since it involves only multiplication and addition of matrices after deciding the spiking transition vector. },
	isbn = {978-84-9887-518-8},
	issn = {0302-9743},
	doi = {10.1007/978-3-642-18123-8_29},
	url = {http://link.springer.com/chapter/10.1007/978-3-642-18123-8_29},
	author = {XiangXiang Zeng and Henry Adorna and Miguel A. Mart{\'\i}nez-del-Amor and Linqiang Pan and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {779,
	title = {Simulating Spiking Neural P Systems Without Delays Using GPUs},
	journal = {International Journal of Natural Computing Research},
	volume = {2},
	year = {2011},
	pages = {19-31},
	publisher = {IGI Global},
	address = {Hershey, Pennsylvania (USA)},
	abstract = {In this paper, the authors discuss the simulation of a P system variant known as Spiking Neural P systems (SNP systems), using Graphics Processing Units (GPUs). GPUs are well suited for highly parallel computations because of their intentional and massively parallel architecture. General purpose GPU computing has seen the use of GPUs for computationally intensive applications, not just in graphics and video processing. P systems, including SNP systems, are maximally parallel computing models taking inspiration from the functioning and dynamics of a living cell. In particular, SNP systems take inspiration from a type of cell known as a neuron. The nature of SNP systems allowed for their representation as matrices, which is an elegant step toward their simulation on GPUs. In this paper, the simulation algorithms, design considerations, and implementation are presented. Finally, simulation results, observations, and analyses using a simple but non-trivial SNP system as an example are discussed, including recommendations for future work.},
	issn = {1947-928X},
	doi = {10.4018/jncr.2011040102},
	url = {http://www.igi-global.com/bookstore/article.aspx?titleid=57968},
	author = {Francis Cabarle and Henry Adorna and Miguel A. Mart{\'\i}nez-del-Amor}
}
@article {673,
	title = {On Communication Complexity in Evolution-Communication P Systems},
	journal = {Romanian Journal of Information Science and Technology},
	volume = {13},
	year = {2010},
	month = {09/2010},
	pages = {113-130},
	publisher = {EDITURA ACADEMIEI ROM{\^A}NE},
	address = {Bucure{\c s}ti, Rom{\^a}nia},
	abstract = {Looking for a theory of communication complexity for P systems, we consider here so-called evolution-communication (EC for short) P systems, where objects evolve by multiset rewriting rules without target commands and pass through membranes by means of symport/antiport rules. We first propose a way to measure the communication costs by means of {\textquotedblleft}quanta of energy{\textquotedblright} (produced by evolution rules and) consumed by communication rules. EC P systems with such costs are proved to be Turing complete in all three cases with respect to the relation between evolution and communication operations: priority of communication, mixing the rules without priority for any type, priority of evolution (with the cost of communication increasing in this ordering in the universality proofs).
More appropriate measures of communication complexity are then defined, as dynamical parameters, counting the communication steps or the number (and the weight) of communication rules used during a computation. Such parameters can be used in three ways: as properties of P systems (considering the families of sets of numbers generated by systems with a given communication complexity), as conditions to be imposed on computations (accepting only those computations with a communication complexity bounded by a given threshold), and as standard complexity measures (defining the class of problems which can be solved by P systems with a bounded complexity). Because we ignore the evolution steps, in all three cases it makes sense to consider hierarchies starting with finite complexity thresholds. We only give some preliminary results about these hierarchies (for instance, proving that already their lower levels contain complex {\textendash} e.g., non-semilinear {\textendash} sets), and we leave open many problems and research issues.},
	issn = {1453-8245},
	url = {http://www.imt.ro/romjist/Volum13/Number13_2/abstracts.htm$\#$2},
	author = {Henry Adorna and Gheorghe Paun and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {699,
	title = {Matrix representation of Spiking Neural P Systems},
	journal = {Eleventh International Conference on Membrane Computing (CMC11)},
	year = {2010},
	month = {08/2010},
	pages = {425-439},
	publisher = {Verlag ProBusiness Berlin},
	address = {Jena, Germany},
	abstract = {Spiking neural P systems (SN P systems, for short) are a
class of distributed parallel computing devices inspired from the way neurons
communicate by means of spikes. In this work, a discrete structure
representation of SN P systems with extended rules and without delay is
proposed. Specifically, matrices are used to represent SN P systems. In
order to represent the computations of SN P systems by matrices, configuration
vectors are defined to monitor the number of spikes in each
neuron at any given configuration; transition net gain vectors are also
introduced to quantify the total amount of spikes consumed and produced
after the chosen rules are applied. Nondeterminism of the systems
is assured by a set of spiking transition vectors that could be used at
any given time during the computation. With such matrix representation,
it is quite convenient to determine the next configuration from a
given configuration, since it involves only multiplication and addition of
matrices after deciding the spiking transition vector.},
	isbn = {978-3-86805-721-8},
	url = {http://cmc11.uni-jena.de/proceedings.html},
	author = {XiangXiang Zeng and Henry Adorna and Miguel A. Mart{\'\i}nez-del-Amor and Linqiang Pan and Mario J. P{\'e}rez-Jim{\'e}nez},
	editor = {Marian Gheorghe and Thomas Hinze and Gheorghe Paun}
}
@article {640,
	title = {On Communication Complexity in Evolution-Communication P Systems},
	journal = {Eighth Brainstorming  Week on Membrane Computing},
	year = {2010},
	month = {02/2010},
	pages = {1-22},
	publisher = {F{\'e}nix Editora},
	address = {Sevilla, Spain},
	isbn = {978-84-614-2357-6},
	url = {http://www.gcn.us.es/8BWMC/volume/01commcomplexity.pdf},
	attachments = {http://www.gcn.us.es/biblio/author/files/01commcomplexity.pdf},
	author = {Henry Adorna and Gheorghe Paun and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {644,
	title = {When Matrices Meet Brains},
	journal = {Eighth Brainstorming  Week on Membrane Computing},
	year = {2010},
	month = {02/2010},
	pages = {255-266},
	publisher = {F{\'e}nix Editora},
	address = {Sevilla, Spain},
	isbn = {978-84-614-2357-6},
	url = {http://www.gcn.us.es/8BWMC/volume/24SNP_Matrix2b.pdf},
	attachments = {http://www.gcn.us.es/biblio/author/files/24SNP_Matrix2b.pdf},
	author = {XiangXiang Zeng and Henry Adorna and Miguel A. Mart{\'\i}nez-del-Amor and Linqiang Pan}
}
