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.

On Some Classes of Sequential Spiking Neural P Systems

2014 ◽  
Vol 26 (5) ◽  
pp. 974-997 ◽  
Author(s):  
Xingyi Zhang ◽  
Xiangxiang Zeng ◽  
Bin Luo ◽  
Linqiang Pan
Keyword(s):  
P Systems ◽  
Active Neuron ◽  
Spiking Neural P Systems ◽  
Biological Inspiration ◽  
Distributed Parallel Computing ◽  
Spike Number ◽  
Minimum Number ◽  
Sequential Mode ◽  
Maximum Minimum ◽  
Active Can

Spiking neural P systems (SN P systems) are a class of distributed parallel computing devices inspired by the way neurons communicate by means of spikes; neurons work in parallel in the sense that each neuron that can fire should fire, but the work in each neuron is sequential in the sense that at most one rule can be applied at each computation step. In this work, with biological inspiration, we consider SN P systems with the restriction that at each step, one of the neurons (i.e., sequential mode) or all neurons (i.e., pseudo-sequential mode) with the maximum (or minimum) number of spikes among the neurons that are active (can spike) will fire. If an active neuron has more than one enabled rule, it nondeterministically chooses one of the enabled rules to be applied, and the chosen rule is applied in an exhaustive manner (a kind of local parallelism): the rule is used as many times as possible. This strategy makes the system sequential or pseudo-sequential from the global view of the whole network and locally parallel at the level of neurons. We obtain four types of SN P systems: maximum/minimum spike number induced sequential/pseudo-sequential SN P systems with exhaustive use of rules. We prove that SN P systems of these four types are all Turing universal as number-generating computation devices. These results illustrate that the restriction of sequentiality may have little effect on the computation power of SN P systems.

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.

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.

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.

P SYSTEMS WORKING IN THE SEQUENTIAL MODE ON ARRAYS AND STRINGS

2005 ◽  
Vol 16 (04) ◽  
pp. 663-682 ◽  
Author(s):  
RUDOLF FREUND
Keyword(s):  
P Systems ◽  
General Definition ◽  
Computational Power ◽  
Generative Power ◽  
Sequential Mode ◽  
Definition Of

Based on a quite general definition of P systems where the rules are applied in a sequential way (and not in the maximally parallel way as it usually happens in most models of P systems considered so far in the literature), we investigate the generative power of various models of such P systems working in the sequential mode on arrays and strings, respectively. P systems working in the sequential mode on arrays/strings without priority relations for the rules reveal the same computational power as the corresponding matrix grammars without appearance checking working on arrays/strings. For obtaining the computational power of matrix grammars with appearance checking, priority relations for the rules (as one of many other possible additional features) are needed.

ON THE POWER OF DETERMINISTIC AND SEQUENTIAL COMMUNICATING P SYSTEMS

2007 ◽  
Vol 18 (02) ◽  
pp. 415-431 ◽  
Author(s):  
LUDĚK CIENCIALA ◽  
LUCIE CIENCIALOVÁ ◽  
PIERLUIGI FRISCO ◽  
PETR SOSÍK
Keyword(s):  
The Other ◽  
P Systems ◽  
Computational Power ◽  
Other Hand ◽  
Mode 2 ◽  
Parallel Mode ◽  
Sequential Mode

We characterize the computational power of several restricted variants of communicating P systems. We show that 2-deterministic communicating P systems with 2 membranes, working in either minimally or maximally parallel mode, are computationally universal. Considering the sequential mode, 2 membranes are shown to characterize the power of partially blind multicounter machines. Next, a characterization of the power of 1-deterministic communicating P systems is given. Finally, we show that the nondeterministic variant in maximally parallel mode is universal already with 1 membrane. These results demonstrate differences in computational power between nondeterminism, 2-determinism and 1-determinism, on one hand, and between sequential, minimally and maximally parallel modes, on the other hand.

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.

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