11,437 research outputs found
Matrix Representation of Spiking Neural P Systems
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.Ministerio de Ciencia e Innovación TIN2009-13192Junta de Andalucía P08-TIC0420
Sparse-matrix Representation of Spiking Neural P Systems for GPUs
Current parallel simulation algorithms for Spiking Neural P (SNP) systems
are based on a matrix representation. This helps to harness the inherent parallelism
in algebraic operations, such as vector-matrix multiplication. Although it has been
convenient for the rst parallel simulators running on Graphics Processing Units
(GPUs), such as CuSNP, there are some bottlenecks to cope with. For example, matrix
representation of SNP systems with a low-connectivity-degree graph lead to sparse
matrices, i.e. containing more zeros than actual values. Having to deal with sparse
matrices downgrades the performance of the simulators because of wasting memory and
time.
However, sparse matrices is a known problem on parallel computing with GPUs, and
several solutions and algorithms are available in the literature. In this paper, we brie
y
analyse some of these ideas and apply them to represent some variants of SNP systems.
We also conclude which variant better suit a sparse-matrix representation
When Matrices Meet Brains
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 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 multiplying vectors to a matrix and
adding vectors
CuSNP: Spiking Neural P Systems Simulators in CUDA
Spiking neural P systems (in short, SN P systems) are models
of computation inspired by biological neurons. CuSNP is a project involving
sequential (CPU) and parallel (GPU) simulators for SN P systems. In this
work, we report the following results: a P-Lingua le parser is included, for
ease of use when performing simulations; extension of the matrix representation
of SN P systems to include delay; comparison and analysis of our simulators
by simulating two types (bitonic and generalized) of parallel sorting networks;
extension of supported types of regular expressions in SN P systems. Our GPU
simulator is better suited for generalized sorting as compared to bitonic sorting
networks, and the GPU simulators run up to 50 faster than our CPU simulator.
Finally, we discuss our experiments and provide directions for further work
Matrix representation and simulation algorithm of spiking neural P systems with structural plasticity
Abstract(#br)In this paper, we create a matrix representation for spiking neural P systems with structural plasticity (SNPSP, for short), taking inspiration from existing algorithms and representations for related variants. Using our matrix representation, we provide a simulation algorithm for SNPSP systems. We prove that the algorithm correctly simulates an SNPSP system: our representation and algorithm are able to capture the syntax and semantics of SNPSP systems, e.g. plasticity rules, dynamism in the synapse set. Analyses of the time and space complexity of our algorithm show that its implementation can benefit using parallel computers. Our representation and simulation algorithm can be useful when implementing SNPSP systems and related variants with a dynamic topology, in software or..
A Spiking Neural P System Simulator Based on CUDA
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.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08-TIC-0420
Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis
This paper discusses the application of fuzzy reasoning spiking neural P systems with trapezoidal fuzzy numbers (tFRSN P systems) to fault diagnosis of power systems, where a matrix-based fuzzy reasoning algorithm based on the dynamic firing mechanism of neurons is used to develop the inference ability of tFRSN P systems from classical reasoning to fuzzy reasoning. Some case studies show the effectiveness of the presented method. We also briefly draw comparisons between the presented method and several main fault diagnosis approaches from the perspectives of knowledge representation and inference process
Simulating Spiking Neural P systems without delays using GPUs
We present in this paper our work regarding simulating a type of P system
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
scientific 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
- {1} are discussed, as well as recommendations for future work.Comment: 19 pages in total, 4 figures, listings/algorithms, submitted at the
9th Brainstorming Week in Membrane Computing, University of Seville, Spai
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