Computational power of sequential spiking neural P systems with multiple channels

2021 ◽  
Author(s):  
Zeqiong Lv ◽  
Qian Yang ◽  
Hong Peng ◽  
Xiaoxiao Song ◽  
Jun Wang
Keyword(s):  
P Systems ◽  
Multiple Channels ◽  
Computational Power ◽  
Spiking Neural P Systems

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.

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.

Spiking neural P systems with multiple channels

2017 ◽  
Vol 95 ◽  
pp. 66-71 ◽  
Author(s):  
Hong Peng ◽  
Jinyu Yang ◽  
Jun Wang ◽  
Tao Wang ◽  
Zhang Sun ◽  
...  
Keyword(s):  
P Systems ◽  
Multiple Channels ◽  
Spiking Neural P Systems

Spiking neural P systems with multiple channels and polarizations

Biosystems ◽  
2019 ◽  
Vol 185 ◽  
pp. 104020
Author(s):  
Qian Yang ◽  
Zeqiong Lv ◽  
Liucheng Liu ◽  
Hong Peng ◽  
Xiaoxiao Song ◽  
...  
Keyword(s):  
P Systems ◽  
Multiple Channels ◽  
Spiking Neural P Systems

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 Extended Channel Rules

2020 ◽  
Vol 31 (01) ◽  
pp. 2050049 ◽  
Author(s):  
Zeqiong Lv ◽  
Tingting Bao ◽  
Nan Zhou ◽  
Hong Peng ◽  
Xiangnian Huang ◽  
...  
Keyword(s):  
Parallel Computing ◽  
Control Mechanism ◽  
P Systems ◽  
Multiple Channels ◽  
Spiking Neural P Systems ◽  
Distributed Parallel Computing ◽  
New Variant ◽  
New Type ◽  
Turing Universality ◽  
Computing Models

This paper discusses a new variant of spiking neural P systems (in short, SNP systems), spiking neural P systems with extended channel rules (in short, SNP–ECR systems). SNP–ECR systems are a class of distributed parallel computing models. In SNP–ECR systems, a new type of spiking rule is introduced, called ECR. With an ECR, a neuron can send the different numbers of spikes to its subsequent neurons. Therefore, SNP–ECR systems can provide a stronger firing control mechanism compared with SNP systems and the variant with multiple channels. We discuss the Turing universality of SNP–ECR systems. It is proven that SNP–ECR systems as number generating/accepting devices are Turing universal. Moreover, we provide a small universal SNP–ECR system as function computing devices.

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.

Small universal asynchronous spiking neural P systems with multiple channels

2020 ◽  
Vol 378 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaoxiao Song ◽  
Hong Peng ◽  
Jun Wang ◽  
Guimin Ning ◽  
Zhang Sun
Keyword(s):  
P Systems ◽  
Multiple Channels ◽  
Spiking Neural P Systems
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