Pulsed neural networks
Journal of Computer and System Sciences
Handbook of Formal Languages
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Membrane Computing: An Introduction
Membrane Computing: An Introduction
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Theoretical Computer Science - Natural computing
Theoretical Computer Science
Computation: finite and infinite machines
Computation: finite and infinite machines
Fundamenta Informaticae
Uniform solutions to SAT and Subset Sum by spiking neural P systems
Natural Computing: an international journal
The Oxford Handbook of Membrane Computing
The Oxford Handbook of Membrane Computing
Matrix representation of spiking neural P systems
CMC'10 Proceedings of the 11th international conference on Membrane computing
Spiking Neural P Systems with Weighted Synapses
Neural Processing Letters
Spiking neural p systems with functional astrocytes
CMC'12 Proceedings of the 13th international conference on Membrane Computing
International Journal of Computing Science and Mathematics
Reversible spiking neural P systems
Frontiers of Computer Science: Selected Publications from Chinese Universities
Universality of sequential spiking neural P systems based on minimum spike number
Theoretical Computer Science
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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.