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)

Spiking Neural P Systems. Recent Results, Research Topics

After a quick introduction of spiking neural P systems (a class of P systems inspired from the way neurons communicate by means of spikes, electrical impulses of identical shape), and presentation of typical results (in general equivalence with Turing machines as number computing devices, but also other issues, such as the possibility of handling strings or infinite sequences), we present a long list of open problems and research topics in this area, also mentioning recent attempts to address some of them. The bibliography completes the information offered to the reader interested in this research area.Ministerio de Educación y Ciencia TIN2006-13425Junta de Andalucía TIC-58

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

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

Extended Spiking Neural P Systems with White Hole Rules

We consider extended spiking neural P systems with the additional possibility of so-called \white hole rules", which send the complete contents of a neuron to other neurons, and we show how this extension of the original model allow for easy proofs of the computational completeness of this variant of extended spiking neural P systems using only one actor neuron. Using only such white hole rules, we can easily simulate special variants of Lindenmayer systems

Open Problems, Research Topics, Recent Results on Numerical and Spiking Neural P Systems (The "Curtea de Arge s 2015 Series")

A series of open problems and research topics are formulated, about numer- ical and spiking neural P systems, initially prepared as a working material for a three months research stage of the second and the third co-author in Curtea de Arge s, Roma- nia, in the fall of 2015. Further problems were added during this period, while certain problems were addressed in this time; some details and references are provided for such cases

Spiking Neural P Systems: A Short Introduction and New Normal Forms

Spiking neural P systems are a class of P systems inspired from the way the neurons communicate with each other by means of electrical impulses (called \spikes"). In the few years since this model was introduced, many results related to the computing power and e ciency of these computing devices were reported. The present paper quickly surveys the basic ideas of this research area and the basic results, then, as typical proofs about the universality of spiking neural P systems, we present some new normal forms for them. Speci cally, we consider a natural restriction in the architecture of a spiking neural P system, to have neurons of a small number of types (i.e., using a small number of sets of rules). We prove that three types of neurons are su cient in order to generate each recursively enumerable set of numbers as the distance between the rst two spikes emitted by the system; the problem remains open for accepting SN P systems. The paper ends with the complete bibliography of this domain, at the level of April 2009.Ministerio de Educación y Ciencia TIN2006-13452Junta de Andalucía P08-TIC-0420

Implementation of Arithmetic Operations by SN P Systems with Communication on Request

Spiking neural P systems (SN P systems, for short) are a class of distributed and parallel computing devices inspired from the way neurons communicate by means of spikes. In most of the SN P systems investigated so far, the system communicates on command, and the application of evolution rules depends on the contents of a neuron. However, inspired from the parallel-cooperating grammar systems, it is natural to consider the opposite strategy: the system communicates on request, which means spikes are requested from neighboring neurons, depending on the contents of the neuron. Therefore, SN P systems with communication on request were proposed, where the spikes should be moved from a neuron to another one when the receiving neuron requests that. In this paper, we consider implementing arithmetical operations by means of SN P systems with communication on request. Specifically, adder, subtracter and multiplier are constructed by using SN P systems with communication on request