Delayed Spiking Neural P Systems with Scheduled Rules

Abstract

Due to the inevitable delay phenomenon in the process of signal conversion and transmission, time delay is bound to occur between neurons. Therefore, it is necessary to introduce the concept of time delay into the membrane computing models. Spiking neural P systems (SN P systems), as an attractive type of neural-like P systems in membrane computing, are widely followed. Inspired by the phenomenon of time delay, in our work, a new variant of spiking neural P systems called delayed spiking neural P systems (DSN P systems) is proposed. Compared with normal spiking neural P systems, the proposed systems achieve time control by setting the schedule on spiking rules and forgetting rules, and the schedule is also used to realize the system delay. A schedule indicates the time difference between receiving and outputting spikes, and it also makes the system work in a certain time, which means that a rule can only be used within a specified time range. We specify that each rule is performed only in the continuous schedule, during which the neuron is locked and cannot send or receive spikes. If the neuron is not available at a given time, it will not receive or send spikes due to the lack of a schedule for this period of time. Moreover, the universality of DSN P systems in both generating and accepting modes is proved. And a universal DSN P system having 81 neurons for computing functions is also proved.

Evolutionary game theory in a cell: A membrane computing approach

“…From the starting model of SNPS, many other features have been added and explored. Among the most recent, we can cite Homogeneous spiking neural P systems with structural plasticity [22], Delayed Spiking Neural P Systems with Scheduled Rules [23] or Spiking neural P systems with autapses, [24]. Among the recent contributions we can cite the SN P systems with communication on requests [25,26] or SN P systems variant used in optimization and for building an arithmetic calculator [27][28][29].…”

Spiking Neural P Systems With Enzymes

“…Therefore, the statement of the Theorem 3 is obviously true. □ [23] 109 3 NSNP [32] 117 3 DeP [48] 115 2 SNP-IR [33] 100 3 IR-SSNP [49] 89 3 DSNP [50] 81 4 SNPA [51] 75 3…”