On Applications of Spiking Neural P Systems

Abstract

Over the years, spiking neural P systems (SNPS) have grown into a popular model in membrane computing because of their diverse range of applications. In this paper, we give a comprehensive summary of applications of SNPS and its variants, especially highlighting power systems fault diagnoses with fuzzy reasoning SNPS. We also study the structure and workings of these models, their comparisons along with their advantages and disadvantages. We also study the implementation of these models in hardware. Finally, we discuss some new ideas which can further expand the scope of applications of SNPS models as well as their implementations.

Snapse: A Visual Tool for Spiking Neural P Systems

“…While this work is a preliminary one to introduce the visual tool Snapse for SN P systems, the Snapse version as of now can be used, at least in part, in some applications. Aside from the examples in Section 5 and their extensions or generalization, Snapse could be used in (parts of) the systems for skeletonizing images as in [27,28], aiding in the design of some systems in [30,[32][33][34], computational biology in [37], and other applications in [26,38].…”

“…Much theoretical work has been done on SN P systems, e.g., their normal forms [14][15][16], formal representations [17][18][19], and their relations to classical models of computation [20][21][22][23][24][25] with a short and recent survey in [26]. After much theoretical work, more recently the work to apply SN P systems to real-world problems becomes even more active, with some early works on image processing e.g., [27] and more recently in [28], use for cryptography [29][30][31], use of evolutionary algorithms to design SN P systems [32][33][34], in pattern recognition [35,36], computational biology [37], with a recent survey in [38].…”

Simulation of Spiking Neural P Systems with Sparse Matrix-Vector Operations

“…More general ways to provide the input or receive the output include the use of spike trains, i.e., a stream or sequence of spikes entering or leaving the system. Further results and details on computability, complexity, and applications of spiking neural P systems are detailed in [5][6][7], a dedicated chapter in the Handbook in [8], and an extensive bibliography until February 2016 in [9]. Moreover, there is a wide range of SNP system variants: with delays, with weights [10], with astrocytes [11], with anti-spikes [12], dendrites [13], rules on synapses [14], scheduled synapses [15], stochastic firing [16], numerical [17], etc.…”

“…This have been harnessed already by introducing CuSNP, a set of simulators for SNP systems implemented with CUDA [21][22][23][24]. Simulators for specific solutions have been also defined in the literature [5,25]. Moreover, this is not unique for SNP systems, many simulators for other P system variants have been accelerated on GPUs [26][27][28].…”

“…Neuron 1 is being divided, and its content is copied to an empty column (5). Columns 0 and 3 represent neurons with a synapse to the neuron being divided (1), so we need to update them as well with the synapse to the created neuron (5). Neuron 3 has reached its limit of maximum out degree, therefore we need to expand the matrix with a new row, or use a COO-like system to store these exceeded elements.…”