An Improvement of Small Universal Spiking Neural P Systems with Anti-Spikes

2016 ◽  
pp. 226-236 ◽  
Author(s):  
Shuo Liu ◽  
Kang Zhou ◽  
Shan Zeng ◽  
Huaqing Qi ◽  
Xing Chen
Keyword(s):  
P Systems ◽  
Spiking Neural P Systems

Small universal spiking neural P systems with dendritic/axonal delays and dendritic trunk/feedback

2021 ◽  
Vol 138 ◽  
pp. 126-139
Author(s):  
Luis Garcia ◽  
Giovanny Sanchez ◽  
Eduardo Vazquez ◽  
Gerardo Avalos ◽  
Esteban Anides ◽  
...  
Keyword(s):  
P Systems ◽  
Spiking Neural P Systems

scholarly journals Novel Numerical Spiking Neural P Systems with a Variable Consumption Strategy

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 549
Author(s):  
Xiu Yin ◽  
Xiyu Liu ◽  
Minghe Sun ◽  
Qianqian Ren
Keyword(s):  
Production Functions ◽  
P Systems ◽  
P System ◽  
Computing Device ◽  
Spiking Neural P Systems ◽  
Distributed Parallel Computing ◽  
Production Values ◽  
Novel Variant ◽  
Turing Universality ◽  
Variable Consumption

A novel variant of NSN P systems, called numerical spiking neural P systems with a variable consumption strategy (NSNVC P systems), is proposed. Like the spiking rules consuming spikes in spiking neural P systems, NSNVC P systems introduce a variable consumption strategy by modifying the form of the production functions used in NSN P systems. Similar to the delay feature of the spiking rules, NSNVC P systems introduce a postponement feature into the production functions. The execution of the production functions in NSNVC P systems is controlled by two, i.e., polarization and threshold, conditions. Multiple synaptic channels are used to transmit the charges and the production values in NSNVC P systems. The proposed NSNVC P systems are a type of distributed parallel computing models with a directed graphical structure. The Turing universality of the proposed NSNVC P systems is proved as number generating/accepting devices. Detailed descriptions are provided for NSNVC P systems as number generating/accepting devices. In addition, a universal NSNVC P system with 66 neurons is constructed as a function computing device.

An Adaptive e-Learning Environment Using Distributed Spiking Neural P Systems

2011 ◽  
Author(s):  
Susan Elias ◽  
A. P. Sarath Chandar ◽  
Kamala Krithivasan ◽  
S. V. Raghavan
Keyword(s):  
Learning Environment ◽  
P Systems ◽  
Spiking Neural P Systems ◽  
E Learning

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

A Parallel Image Skeletonizing Method Using Spiking Neural P Systems with Weights

2018 ◽  
Vol 50 (2) ◽  
pp. 1485-1502 ◽  
Author(s):  
Tao Song ◽  
Shanchen Pang ◽  
Shaohua Hao ◽  
Alfonso Rodríguez-Patón ◽  
Pan Zheng
Keyword(s):  
P Systems ◽  
Spiking Neural P Systems ◽  
Parallel Image

Small Universal Spiking Neural P Systems with Exhaustive Use of Rules

2010 ◽  
Vol 7 (5) ◽  
pp. 890-899 ◽  
Author(s):  
Xingyi Zhang ◽  
Yun Jiang ◽  
Linqiang Pan
Keyword(s):  
P Systems ◽  
Spiking Neural P Systems

Asynchronous spiking neural P systems with rules on synapses

2015 ◽  
Vol 151 ◽  
pp. 1439-1445 ◽  
Author(s):  
Tao Song ◽  
Quan Zou ◽  
Xiangrong Liu ◽  
Xiangxiang Zeng
Keyword(s):  
P Systems ◽  
Spiking Neural P Systems

scholarly journals Spiking neural P systems: An improved normal form

2010 ◽  
Vol 411 (6) ◽  
pp. 906-918 ◽  
Author(s):  
Linqiang Pan ◽  
Gheorghe Păun
Keyword(s):  
Normal Form ◽  
P Systems ◽  
Spiking Neural P Systems
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