29,887 research outputs found
On spiking neural P systems
This work deals with several aspects concerning the formal verification of SN P
systems and the computing power of some variants. A methodology based on the
information given by the transition diagram associated with an SN P system is presented.
The analysis of the diagram cycles codifies invariants formulae which enable us to establish
the soundness and completeness of the system with respect to the problem it tries to resolve.
We also study the universality of asynchronous and sequential SN P systems and the
capability these models have to generate certain classes of languages. Further, by making a
slight modification to the standard SN P systems, we introduce a new variant of SN P
systems with a special I/O mode, called SN P modules, and study their computing power. It
is demonstrated that, as string language acceptors and transducers, SN P modules can
simulate several types of computing devices such as finite automata, a-finite transducers,
and systolic trellis automata.Ministerio de Educación y Ciencia TIN2006-13425Junta de Andalucía TIC-58
On The Delays In Spiking Neural P Systems
In this work we extend and improve the results done in a previous work on
simulating Spiking Neural P systems (SNP systems in short) with delays using
SNP systems without delays. We simulate the former with the latter over
sequential, iteration, join, and split routing. Our results provide
constructions so that both systems halt at exactly the same time, start with
only one spike, and produce the same number of spikes to the environment after
halting.Comment: Presented at the 6th Symposium on the Mathematical Aspects of
Computer Science (SMACS2012), Boracay, Philippines. 6 figures, 6 pages, 2
column
Spiking Neural P Systems with Addition/Subtraction Computing on Synapses
Spiking neural P systems (SN P systems, for short) are a class of distributed
and parallel computing models inspired from biological spiking neurons. In this paper,
we introduce a variant called SN P systems with addition/subtraction computing on
synapses (CSSN P systems). CSSN P systems are inspired and motivated by the shunting
inhibition of biological synapses, while incorporating ideas from dynamic graphs and
networks. We consider addition and subtraction operations on synapses, and prove that
CSSN P systems are computationally universal as number generators, under a normal
form (i.e. a simplifying set of restrictions)
Simulating FRSN P Systems with Real Numbers in P-Lingua on sequential and CUDA platforms
Fuzzy Reasoning Spiking Neural P systems (FRSN P systems,
for short) is a variant of Spiking Neural P systems incorporating
fuzzy logic elements that make it suitable to model fuzzy diagnosis knowledge
and reasoning required for fault diagnosis applications. In this sense,
several FRSN P system variants have been proposed, dealing with real
numbers, trapezoidal numbers, weights, etc. The model incorporating
real numbers was the first introduced [13], presenting promising applications
in the field of fault diagnosis of electrical systems. For this variant,
a matrix-based algorithm was provided which, when executed on parallel
computing platforms, fully exploits the model maximally parallel
capacities. In this paper we introduce a P-Lingua framework extension
to parse and simulate FRSN P systems with real numbers. Two simulators,
implementing a variant of the original matrix-based simulation
algorithm, are provided: a sequential one (written in Java), intended to
run on traditional CPUs, and a parallel one, intended to run on CUDAenabled
devices.Ministerio de Economía y Competitividad TIN2012-3743
Spiking Neural P Systems with Communication on Request
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Spiking Neural P Systems are Neural System models characterised by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural P systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these P systems, a speci ed number of spikes are consumed and a speci ed number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron.
In the present work, a novel communication strategy among neurons of Spiking Neural P Systems is proposed. In the resulting models, called Spiking Neural P Systems with Communication on Request, the spikes are requested from neighbouring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural P systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron).
The Spiking Neural P Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems
Extended Spiking Neural P systems with Excitatory and Inhibitory Astrocytes
We investigate an extended model of spiking neural P systems incorporating
astrocytes and their excitatory or inhibitory influence on axons between neurons.
Using very restricted variants of extended spiking neural P systems with excitatory and
inhibitory astrocytes we can easily model Boolean gates like NAND-gates as well as discrete
amplifiers
Time After Time: Notes on Delays In Spiking Neural P Systems
Spiking Neural P systems, SNP systems for short, are biologically inspired
computing devices based on how neurons perform computations. SNP systems use
only one type of symbol, the spike, in the computations. Information is encoded
in the time differences of spikes or the multiplicity of spikes produced at
certain times. SNP systems with delays (associated with rules) and those
without delays are two of several Turing complete SNP system variants in
literature. In this work we investigate how restricted forms of SNP systems
with delays can be simulated by SNP systems without delays. We show the
simulations for the following spike routing constructs: sequential, iteration,
join, and split.Comment: 11 pages, 9 figures, 4 lemmas, 1 theorem, preprint of Workshop on
Computation: Theory and Practice 2012 at DLSU, Manila together with UP
Diliman, DLSU, Tokyo Institute of Technology, and Osaka universit
Memory about the 14 th BWMC: SN P systems vs. ESN P systems with Transmittable States
The objectives of this memory are, on one hand, to provide a general
overview about the topic of Membrane Computing, answering basic questions as what
is it, which are its basic elements, which problems it allows to solve, its current limita-
tions... and, on the other hand, to provide a more speci c information about the model of
Spiking Neural P systems in both its original formulation and on the variant of the model
suggested on the 14th edition of the Brainstorming Week on Membrane Computing, the
Extended Spiking Neural P systems with Transmittable States.
The motivation to further explore the topic of Spiking Neural P systems (SN P
systems for short) comes from the idea that they could be a really suitable framework
in order to model chain-reaction processes as the ssion of 235U taking place inside a
nuclear reactor
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