Universality of SNQ P Systems Using One Type of Spikes and Restrictive Rule Application

2020 ◽  
Vol 31 (01) ◽  
pp. 117-132
Author(s):  
Andrei Păun ◽  
Florin-Daniel Bîlbîe
Keyword(s):  
P Systems ◽  
Current Work ◽  
Turing Machines ◽  
Computational Power ◽  
Process Information ◽  
Spiking Neural P Systems ◽  
Rule Application ◽  
The Way

We investigate the spiking neural P systems with communication on request (SNQ P systems) that are devices in the area of neural like P systems abstracting the way in which neurons work and process information. Here we discuss the SNQ P systems using the rule application strategy as defined by Linqiang Pan and collaborators and we are able to improve their result of universality of such systems using two types of spikes. In the current work, we prove that only one type of spikes is sufficient for reaching the computational power of Turing Machines for these devices, bringing closer to implementation such a device. The result holds both in maximum parallel manner application of the rules as well as the maximum-sequentiality application of rules.

ON THE POWER OF FAMILIES OF RECOGNIZER SPIKING NEURAL P SYSTEMS

2011 ◽  
Vol 22 (01) ◽  
pp. 75-88
Author(s):  
PETR SOSÍK ◽  
ALFONSO RODRÍGUEZ-PATÓN ◽  
LUDĚK CIENCIALA
Keyword(s):  
P Systems ◽  
Complexity Classes ◽  
Computational Power ◽  
Spiking Neural P Systems ◽  
Formal Framework

The paper summarizes recent knowledge about computational power of spiking neural P systems and presents a sequence of new more general results. The concepts of recognizer SN P systems and of uniform families of SN P systems provide a formal framework for this study. We establish the relation of computational power of spiking neural P systems with various limitations to standard complexity classes like P , NP, PSPACE and P /poly.

Evolution-Communication Spiking Neural P Systems

2020 ◽  
pp. 2050064
Author(s):  
Tingfang Wu ◽  
Qiang Lyu ◽  
Linqiang Pan
Keyword(s):  
Parallel Computation ◽  
Fire Behavior ◽  
Information Storage ◽  
P Systems ◽  
Computational Power ◽  
Spiking Neural P Systems ◽  
Integrate And Fire ◽  
Restricted Form ◽  
The Family ◽  
Novel Variant

Spiking neural P systems (SNP systems) are a class of distributed and parallel computation models, which are inspired by the way in which neurons process information through spikes, where the integrate-and-fire behavior of neurons and the distribution of produced spikes are achieved by spiking rules. In this work, a novel mechanism for separately describing the integrate-and-fire behavior of neurons and the distribution of produced spikes, and a novel variant of the SNP systems, named evolution-communication SNP (ECSNP) systems, is proposed. More precisely, the integrate-and-fire behavior of neurons is achieved by spike-evolution rules, and the distribution of produced spikes is achieved by spike-communication rules. Then, the computational power of ECSNP systems is examined. It is demonstrated that ECSNP systems are Turing universal as number-generating devices. Furthermore, the computational power of ECSNP systems with a restricted form, i.e. the quantity of spikes in each neuron throughout a computation does not exceed some constant, is also investigated, and it is shown that such restricted ECSNP systems can only characterize the family of semilinear number sets. These results manifest that the capacity of neurons for information storage (i.e. the quantity of spikes) has a critical impact on the ECSNP systems to achieve a desired computational power.

Nonlinear Spiking Neural P Systems

2020 ◽  
Vol 30 (10) ◽  
pp. 2050008 ◽  
Author(s):  
Hong Peng ◽  
Zeqiong Lv ◽  
Bo Li ◽  
Xiaohui Luo ◽  
Jun Wang ◽  
...  
Keyword(s):  
The State ◽  
P Systems ◽  
Number Generator ◽  
Computational Power ◽  
Nonlinear Functions ◽  
Computing Systems ◽  
Spiking Neural P Systems ◽  
System A ◽  
New Variant ◽  
New Type

This paper proposes a new variant of spiking neural P systems (in short, SNP systems), nonlinear spiking neural P systems (in short, NSNP systems). In NSNP systems, the state of each neuron is denoted by a real number, and a real configuration vector is used to characterize the state of the whole system. A new type of spiking rules, nonlinear spiking rules, is introduced to handle the neuron’s firing, where the consumed and generated amounts of spikes are often expressed by the nonlinear functions of the state of the neuron. NSNP systems are a class of distributed parallel and nondeterministic computing systems. The computational power of NSNP systems is discussed. Specifically, it is proved that NSNP systems as number-generating/accepting devices are Turing-universal. Moreover, we establish two small universal NSNP systems for function computing and number generator, containing 117 neurons and 164 neurons, respectively.

scholarly journals Sequential Spiking Neural P Systems with Local Scheduled Synapses without Delay

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Alia Bibi ◽  
Fei Xu ◽  
Henry N. Adorna ◽  
Francis George C. Cabarle
Keyword(s):  
Computational Models ◽  
P Systems ◽  
Dynamic Graphs ◽  
Computational Power ◽  
Spiking Neural P Systems ◽  
Graphs And Networks ◽  
The Family ◽  
Minimum Number ◽  
Sequential Mode ◽  
Maximum Minimum

Spiking neural P systems with scheduled synapses are a class of distributed and parallel computational models motivated by the structural dynamism of biological synapses by incorporating ideas from nonstatic (i.e., dynamic) graphs and networks. In this work, we consider the family of spiking neural P systems with scheduled synapses working in the sequential mode: at each step the neuron(s) with the maximum/minimum number of spikes among the neurons that can spike will fire. The computational power of spiking neural P systems with scheduled synapses working in the sequential mode is investigated. Specifically, the universality (Turing equivalence) of such systems is obtained.

Spiking Neural P Systems with a Generalized Use of Rules

2014 ◽  
Vol 26 (12) ◽  
pp. 2925-2943 ◽  
Author(s):  
Xingyi Zhang ◽  
Bangju Wang ◽  
Linqiang Pan
Keyword(s):  
Parallel Computing ◽  
Spiking Neurons ◽  
P Systems ◽  
Computational Power ◽  
Finite Sets ◽  
Spiking Neural P Systems ◽  
Semilinear Sets ◽  
Distributed Parallel Computing

Spiking neural P systems (SN P systems) are a class of distributed parallel computing devices inspired by spiking neurons, where the spiking rules are usually used in a sequential way (an applicable rule is applied one time at a step) or an exhaustive way (an applicable rule is applied as many times as possible at a step). In this letter, we consider a generalized way of using spiking rules by “combining” the sequential way and the exhaustive way: if a rule is used at some step, then at that step, it can be applied any possible number of times, nondeterministically chosen. The computational power of SN P systems with a generalized use of rules is investigated. Specifically, we prove that SN P systems with a generalized use of rules consisting of one neuron can characterize finite sets of numbers. If the systems consist of two neurons, then the computational power of such systems can be greatly improved, but not beyond generating semilinear sets of numbers. SN P systems with a generalized use of rules consisting of three neurons are proved to generate at least a non-semilinear set of numbers. In the case of allowing enough neurons, SN P systems with a generalized use of rules are computationally complete. These results show that the number of neurons is crucial for SN P systems with a generalized use of rules to achieve a desired computational power.

scholarly journals Snapse: A Visual Tool for Spiking Neural P Systems

Processes ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 72
Author(s):  
Aleksei Dominic C. Fernandez ◽  
Reyster M. Fresco ◽  
Francis George C. Cabarle ◽  
Ren Tristan A. de la Cruz ◽  
Ivan Cedric H. Macababayao ◽  
...  
Keyword(s):  
Theoretical Computer Science ◽  
Spiking Neurons ◽  
P Systems ◽  
Turing Machines ◽  
Neuron Models ◽  
Theoretical Computer ◽  
Spiking Neural P Systems ◽  
Visual Tool ◽  
Real World Applications ◽  
Hard Problems

Spiking neural P (SN P) systems are models of computation inspired by spiking neurons and part of the third generation of neuron models. SN P systems are equivalent to Turing machines and are able to solve computationally hard problems using a space-time trade-off. Research in SN P systems theory is especially active, more so in recent years as more efforts are directed towards their real-world applications. Usually, SN P systems are represented visually as a directed graph and simulated through mainly text-based simulations or tables. Thus, there is a need for tools that can simulate and create SN P Systems in a visual and easy-to-use manner. Snapse is such a tool which aims to hasten the speed and ease at which researchers may create and experiment with SN P systems. Furthermore, visual tools such as Snapse can help further bring SN P systems outside of theoretical computer science.

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

2018 ◽  
Vol 13 (3) ◽  
pp. 353-364
Author(s):  
Yun Jiang ◽  
Yuan Kong ◽  
Chaoping Zhu
Keyword(s):  
Parallel Computing ◽  
P Systems ◽  
Arithmetic Operations ◽  
Spiking Neural P Systems ◽  
Grammar Systems ◽  
Distributed And Parallel Computing ◽  
The Way

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.

Spiking Neural P Systems with Astrocytes Producing Calcium

2020 ◽  
Vol 30 (12) ◽  
pp. 2050066
Author(s):  
Bogdan Aman ◽  
Gabriel Ciobanu
Keyword(s):  
Directed Graph ◽  
Calcium Ions ◽  
Topological Structure ◽  
Essential Role ◽  
Normal Forms ◽  
P Systems ◽  
Firing Condition ◽  
Computational Power ◽  
Spiking Neural P Systems ◽  
New Variant

The astrocytes are cells which play an essential role in the functioning and interaction of neurons by feeding the respective neurons with calcium ions. Drawing inspiration from this two-way relationship in which the astrocytes influence and are influenced by the neurons by means of calcium ions, in this paper, we define and study spiking neural P systems with astrocytes producing calcium. Distinct from the usual firing rules in spiking neural P systems, the firing condition not only depends on the spikes collected in a neuron but also on the calcium units received from astrocytes. From the perspective of topological structure, the new variant is shown as a directed graph in which synapses link either astrocytes or neurons, as well as astrocytes to neurons and conversely. The computational power of spiking neural P systems with astrocytes producing calcium is investigated; it is proved that these systems using a limited number of rules are Turing universal as both number generating and number accepting devices. It is also presented how to obtain normal forms by removing forgetting rules and delays while preserving the computational power.

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