Spiking neural P systems with inhibitory rules

Motivated by the mechanism of inhibitory synapses, a new kind of spiking neural P (SNP) system rules, called inhibitory rules, is introduced in this paper. Based on this, a new variant of SNP systems is proposed, called spiking neural P systems with inhibitory rules (SNP-IR systems). Different from...

Descripción completa

Detalles Bibliográficos
Autores: Peng, Hong, Li, Bo, Wang, Jun, Song, Xiaoxiao, Wang, Tao, Valencia Cabrera, Luis, Pérez Hurtado de Mendoza, Ignacio, Riscos Núñez, Agustín, Pérez Jiménez, Mario de Jesús
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2020
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/104514
Acceso en línea:https://hdl.handle.net/11441/104514
https://doi.org/10.1016/j.knosys.2019.105064
Access Level:acceso abierto
Palabra clave:Membrane Computing
Spiking neural P Systems
Spiking neural P systems with inhibitory rules
Inhibitory synapse
Descripción
Sumario:Motivated by the mechanism of inhibitory synapses, a new kind of spiking neural P (SNP) system rules, called inhibitory rules, is introduced in this paper. Based on this, a new variant of SNP systems is proposed, called spiking neural P systems with inhibitory rules (SNP-IR systems). Different from the usual firing rules in SNP systems, the firing condition of an inhibitory rule not only depends on the state of the neuron associated with the rule but also is related to the states of other neurons. Moreover, from the perspective of topological structure, the new variant is shown as a directed graph with inhibitory arcs, and therefore seems to have more powerful control. The computational completeness of SNPIR systems is discussed. In particular, it is proved that SNP-IR systems are Turing universal number accepting/generating devices. Moreover, we obtain a small universal function-computing device for SNP-IR systems consisting of 100 neurons.