1. Spiking neural P systems with target indications
- Author
-
Linqiang Pan, Tingfang Wu, and Luping Zhang
- Subjects
Quantitative Biology::Neurons and Cognition ,General Computer Science ,Distribution (number theory) ,Computer science ,Computation ,0102 computer and information sciences ,02 engineering and technology ,Function (mathematics) ,01 natural sciences ,Theoretical Computer Science ,Power (physics) ,Set (abstract data type) ,Synapse ,Computer Science::Emerging Technologies ,medicine.anatomical_structure ,nervous system ,010201 computation theory & mathematics ,0202 electrical engineering, electronic engineering, information engineering ,Feature (machine learning) ,medicine ,020201 artificial intelligence & image processing ,Spike (software development) ,Neuron ,Biological system - Abstract
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 by means of spikes, where a neuron fires and distributes the spikes to all the neurons linked by synapses with the firing neuron. In this work, a new spike distribution mechanism is introduced, where the produced spikes are distributed to a set of neurons indicated by the target indications. The computation power of SNP systems with this new spike distribution mechanism is investigated. It is demonstrated that SNP systems with such a distribution mechanism are Turing universal as both number generators and function computing devices. Moreover, it is shown that 6 neurons (respectively, 15 neurons) are sufficient for constructing a universal SNP system with the proposed spike distribution mechanism as a number generator (respectively, as a function computing device). By comparing with the classical one, it can be found that the proposed spike distribution mechanism is a powerful feature in terms of the number of neurons used to construct universal SNP systems.
- Published
- 2021