1. Spiking Neural P systems with weights
- Author
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Gheorghe Paun, Jun Wang, Mario J. Pérez-Jiménez, Linqiang Pan, Hendrik Jan Hoogeboom, Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad de Sevilla. TIC193: Computación Natural, Ministerio de Ciencia e Innovación (MICIN). España, and Junta de Andalucía
- Subjects
Neurons ,Rational number ,Artificial neural network ,Quantitative Biology::Neurons and Cognition ,Cognitive Neuroscience ,Action Potentials ,Brain ,Value (computer science) ,Natural number ,Synaptic Transmission ,Combinatorics ,Nondeterministic algorithm ,Computable function ,Models of neural computation ,Arts and Humanities (miscellaneous) ,Integer ,Animals ,Humans ,Computer Simulation ,Neural Networks, Computer ,Nerve Net ,Algorithms ,Mathematics - Abstract
A variant of spiking neural P systems with positive or negative weights on synapses is introduced, where the rules of a neuron fire when the potential of that neuron equals a given value. The involved values—weights, firing thresholds, potential consumed by each rule—can be real (computable) numbers, rational numbers, integers, and natural numbers. The power of the obtained systems is investigated. For instance, it is proved that integers (very restricted: 1, −1 for weights, 1 and 2 for firing thresholds, and as parameters in the rules) suffice for computing all Turing computable sets of numbers in both the generative and the accepting modes. When only natural numbers are used, a characterization of the family of semilinear sets of numbers is obtained. It is shown that spiking neural P systems with weights can efficiently solve computationally hard problems in a nondeterministic way. Some open problems and suggestions for further research are formulated. Ministerio de Ciencia e Innovación TIN2009–13192 Junta de Andalucía P08–TIC-04200
- Published
- 2010