Asynchronous Spiking Neural P Systems with Multiple Channels and Symbols

Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computation systems, inspired from the way that the neurons process and communicate information by means of spikes. A new variant of SNP systems, which works in asynchronous mode, asynchronous spiking neural P systems with multiple channels and symbols (ASNP-MCS systems, in short), is investigated in this paper. There are two interesting features in ASNP-MCS systems: multiple channels and multiple symbols. That is, every neuron has more than one synaptic channels to connect its subsequent neurons, and every neuron can deal with more than one type of spikes. The variant works in asynchronous mode: in every step, each neuron can be free to fire or not when its rules can be applied. The computational completeness of ASNP-MCS systems is investigated. It is proved that ASNP-MCS systems as number generating and accepting devices are Turing universal. Moreover, we obtain a small universal function computing device that is an ASNP-MCS system with 67 neurons. Specially, a new idea that can solve ``block'' problems is proposed in INPUT modules

A Kernel-Based Membrane Clustering Algorithm

The existing membrane clustering algorithms may fail to handle the data sets with non-spherical cluster boundaries. To overcome the shortcoming, this paper introduces kernel methods into membrane clustering algorithms and proposes a kernel-based membrane clustering algorithm, KMCA. By using non-linear kernel function, samples in original data space are mapped to data points in a high-dimension feature space, and the data points are clustered by membrane clustering algorithms. Therefore, a data clustering problem is formalized as a kernel clustering problem. In KMCA algorithm, a tissue-like P system is designed to determine the optimal cluster centers for the kernel clustering problem. Due to the use of non-linear kernel function, the proposed KMCA algorithm can well deal with the data sets with non-spherical cluster boundaries. The proposed KMCA algorithm is evaluated on nine benchmark data sets and is compared with four existing clustering algorithms

On the Universality of Spiking Neural P Systems With Multiple Channels and Autapses

Spiking neural P systems (SN P systems) abstract the structure and function of neurons and nervous systems. By adopting some biological observations or mathematical considerations in SN P systems, many variants have been proposed. In this work, through combining two interesting variants, SN P systems with multiple channels (SNP-MC systems) and SN P systems with autapses (SNP-AU systems), we propose SN P systems with multiple channels and autapses (SNP-MCA systems) and research their universality. SNP-MCA systems are proved to be universal in the generation of numbers. For computing functions, a universal SNP-MCA system with 25 neurons is constructed, which requires fewer neurons than the two variants