@article {2479,
	title = {Simulation of Spiking Neural P Systems with Sparse Matrix-Vector Operations},
	journal = {Processes},
	volume = {9},
	year = {2021},
	abstract = {To date, parallel simulation algorithms for spiking neural P (SNP) systems are based on a matrix representation. This way, the simulation is implemented with linear algebra operations, which can be easily parallelized on high performance computing platforms such as GPUs. Although it has been convenient for the first generation of GPU-based simulators, such as CuSNP, there are some bottlenecks to sort out. For example, the proposed matrix representations of SNP systems lead to very sparse matrices, where the majority of values are zero. It is known that sparse matrices can compromise the performance of algorithms since they involve a waste of memory and time. This problem has been extensively studied in the literature of parallel computing. In this paper, we analyze some of these ideas and apply them to represent some variants of SNP systems. We also provide a new simulation algorithm based on a novel compressed representation for sparse matrices. We also conclude which SNP system variant better suits our new compressed matrix representation.},
	issn = {2227-9717},
	doi = {10.3390/pr9040690},
	url = {https://doi.org/10.3390/pr9040690},
	author = {Miguel A. Mart{\'\i}nez-del-Amor and David Orellana-Mart{\'\i}n and Ignacio P{\'e}rez-Hurtado and Francis G. C. Cabarle and Henry N. Adorna}
}
@article {2078,
	title = {CuSNP: Spiking Neural P Systems Simulators in CUDA},
	journal = {Romanian Journal of Information Science and Technology},
	volume = {20},
	year = {2017},
	pages = {57-70},
	url = {http://www.imt.ro/romjist/Volum20/Number20_1/cuprins20_1.htm},
	author = {J.P. Carandang and J.M.B. Villaflores and Francis G. C. Cabarle and Henry N. Adorna and Miguel A. Mart{\'\i}nez-del-Amor}
}
@article {2197,
	title = {Nondeterminism in Spiking Neural P Systems: Algorithms and Simulations},
	journal = {The 6th Asian Conference on Membrane Computing},
	year = {2017},
	address = {Chengdu (China)},
	url = {http://2017.asiancmc.org/?page_id=58},
	author = {J.P. Carandang and Francis G. C. Cabarle and Henry N. Adorna and Nestine H. Hernandez and Miguel A. Mart{\'\i}nez-del-Amor}
}
@article {2089,
	title = {Sparse-matrix Representation of Spiking Neural P Systems for GPU},
	journal = {Fifteenth Brainstorming Week on Membrane Computing (BWMC2017)},
	year = {2017},
	pages = {161-170},
	publisher = {F{\'e}nix Editora},
	url = {http://www.gcn.us.es/15bwmc_proceedings},
	author = {Miguel A. Mart{\'\i}nez-del-Amor and David Orellana-Mart{\'\i}n and Francis G. C. Cabarle and Mario J. P{\'e}rez-Jim{\'e}nez and Henry N. Adorna}
}
@article {1940,
	title = {Sequential spiking neural P systems with structural plasticity based on max/min spike number},
	journal = {Neural Computing and Applications},
	volume = {27},
	year = {2016},
	month = {06/2016},
	pages = {1337-1347},
	publisher = {Springer},
	address = {Amsterdam (The Netherlands)},
	abstract = {Spiking neural P systems (in short, SNP systems) are parallel, distributed, and nondeterministic computing devices inspired by biological spiking neurons. Recently, a class of SNP systems known as SNP systems with structural plasticity (in short, SNPSP systems) was introduced. SNPSP systems represent a class of SNP systems that have dynamism applied to the synapses, i.e. neurons can use plasticity rules to create or remove synapses. In this work, we impose the restriction of sequentiality on SNPSP systems, using four modes: max, min, max-pseudo-, and min-pseudo-sequentiality. We also impose a normal form for SNPSP systems as number acceptors and generators. Conditions for (non)universality are then provided. Specifically, acceptors are universal in all modes, while generators need a nondeterminism source in two modes, which in this work is provided by the plasticity rules.},
	keywords = {Membrane computing, Sequential systems, spiking neural P systems, Structural plasticity, turing universality},
	issn = {0941-0643},
	doi = {10.1007/s00521-015-1937-5},
	url = {http://link.springer.com/article/10.1007/s00521-015-1937-5},
	author = {Francis G. C. Cabarle and Henry N. Adorna and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {2070,
	title = {CuSNP: Spiking Neural P Systems Simulators in CUDA},
	journal = {Asian Conference on Membrane Computing 2016},
	year = {2016},
	url = {http://2016.asiancmc.org/},
	author = {Jym P. Carandang and John M. B. Villaflores and Francis G. C. Cabarle and Henry N. Adorna and Miguel A. Mart{\'\i}nez-del-Amor}
}
@article {1948,
	title = {Improving simulations of Spiking Neural P Systems in NVIDIA CUDA GPUs: CuSNP},
	journal = {Fourteenth Brainstorming Week on Membrane Computing (BWMC2016)},
	year = {2016},
	pages = {135-150},
	publisher = {F{\'e}nix Editora},
	url = {http://www.gcn.us.es/14bwmc_proceedings},
	author = {Jym P. Carandang and John M. B. Villaflores and Francis G. C. Cabarle and Henry N. Adorna and Miguel A. Mart{\'\i}nez-del-Amor}
}
@article {1925,
	title = {Spiking neural P systems with structural plasticity},
	journal = {Neural Computing and Applications},
	volume = {26},
	year = {2015},
	month = {11},
	pages = {1905-1917},
	publisher = {Springer},
	address = {Amsterdam (The Netherlands)},
	abstract = {Spiking neural P (SNP) systems are a class of parallel, distributed, and nondeterministic computing models inspired by the spiking of biological neurons. In this work, the biological feature known as structural plasticity is introduced in the framework of SNP systems. Structural plasticity refers to synapse creation and deletion, thus changing the synapse graph. The {\textquotedblleft}programming{\textquotedblright} therefore of a brain-like model, the SNP system with structural plasticity (SNPSP system), is based on how neurons connect to each other. SNPSP systems are also a partial answer to an open question on SNP systems with dynamism only for synapses. For both the accepting and generative modes, we prove that SNPSP systems are universal. Modifying SNPSP systems semantics, we introduce the spike saving mode and prove that universality is maintained. In saving mode, however, a deadlock state can arise, and we prove that reaching such a state is undecidable. Lastly, we provide one technique in order to use structural plasticity to solve a hard problem: a constant time, nondeterministic, and semi-uniform solution to the NP-complete problem Subset Sum.},
	keywords = {computational universality, Deadlock, Membrane computing, spiking neural P systems, Structural plasticity, Subset Sum, Undecidability},
	issn = {0941-0643},
	doi = {doi: 10.1007/s00521-015-1857-4},
	url = {http://link.springer.com/article/10.1007\%2Fs00521-015-1857-4},
	author = {Francis G. C. Cabarle and Henry N. Adorna and Mario J. P{\'e}rez-Jim{\'e}nez and Tao Song}
}
@article {1922,
	title = {Asynchronous Spiking neural P systems with structural plasticity},
	journal = {Lecture Notes in Computer Science},
	volume = {9252},
	year = {2015},
	month = {07/2015},
	pages = {132-143},
	publisher = {Springer},
	address = {Amsterdam, The Netherlands},
	issn = {0302-9743},
	url = {http://dx.doi.org/10.1007/978-3-319-21819-9_9},
	author = {Francis G. C. Cabarle and Henry N. Adorna and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {1931,
	title = {Sequential spiking neural P systems with structural plasticity based on max/min spike number},
	journal = {Neural Computing and Applications},
	year = {2015},
	month = {06/2015},
	pages = {1-11},
	publisher = {Springer},
	address = {Amsterdam (The Netherlands)},
	abstract = {Spiking neural P systems (in short, SNP systems) are parallel, distributed, and nondeterministic computing devices inspired by biological spiking neurons. Recently, a class of SNP systems known as SNP systems with structural plasticity (in short, SNPSP systems) was introduced. SNPSP systems represent a class of SNP systems that have dynamism applied to the synapses, i.e. neurons can use plasticity rules to create or remove synapses. In this work, we impose the restriction of sequentiality on SNPSP systems, using four modes: max, min, max-pseudo-, and min-pseudo-sequentiality. We also impose a normal form for SNPSP systems as number acceptors and generators. Conditions for (non)universality are then provided. Specifically, acceptors are universal in all modes, while generators need a nondeterminism source in two modes, which in this work is provided by the plasticity rules},
	keywords = {Membrane computing, Sequential systems, spiking neural P systems, Structural plasticity, turing universality},
	issn = {0941-0643},
	doi = {10.1007/s00521-015-1937-5},
	url = {http://link.springer.com/article/10.1007/s00521-015-1937-5},
	author = {Francis G. C. Cabarle and Henry N. Adorna and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {1914,
	title = {Notes on Spiking Neural P systems and finite automata},
	journal = {Thirteenth Brainstorming Week on Membrane Computing},
	year = {2015},
	month = {02/2015},
	pages = {77-89},
	publisher = {F{\'e}nix Editora},
	address = {Sevilla, Espa{\~n}a},
	abstract = {Spiking neural P systems (in short, SNP systems) are membrane computing
models inspired by the pulse coding of information in biological neurons. SNP systems
with standard rules have neurons that emit at most one spike (the pulse) each step, and
have either an input or output neuron connected to the environment. SNP transducers
were introduced, where both input and output neurons were used. More recently, SNP
modules were introduced which generalize SNP transducers: extended rules are used
(more than one spike can be emitted each step) and a set of input and output neurons can
be used. In this work we continue relating SNP modules and finite automata: (i) we amend
previous constructions for DFA and DFST simulations, (ii) improve the construction
from three neurons down to one neuron, (iii) DFA with output are simulated, and (iv)
we generate automatic sequences using results from (iii).},
	keywords = {Automatic sequences, finite automata, Membrane computing, spiking neural P systems},
	url = {http://www.cs.us.es/~marper/investigacion/proceedings-13th-BWMC.pdf},
	author = {Francis G. C. Cabarle and Henry N. Adorna and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {2071,
	title = {Spiking Neural P Systems with Structural Plasticity: Attacking the Subset Sum Problem},
	journal = {Lecture Notes in Computer Science},
	volume = {9504},
	year = {2015},
	pages = {106-116},
	publisher = {Springer},
	isbn = {978-3-319-28474-3},
	doi = {10.1007/978-3-319-28475-0_8},
	url = {http://dx.doi.org/10.1007/978-3-319-28475-0_8},
	author = {Francis G. C. Cabarle and Nestine H. S. Hernandez and Miguel A. Mart{\'\i}nez-del-Amor}
}
