Research On Computational Properties And Applications Of Spiking Neural P Systems

Bio-computing is a inter-discipline of computer science and bio-science. Bio-computing models have several advantages, such as storing information costing less space,performing computation highly parallel, doing computation in self-assemble and self-adaption manners, and having high fault tolerance in the computation. Membrane com-puting is a new and hot branch of bio-computing. The computing models investigated inmembrane computing are called P systems. In this work, we deal with spiking neural Psystems, which are inspired from the way of biological neuron processing information andcommunicating with each other by means of electrical impulses (spikes).Homogeneity is a particular property of human brain in sense that each neuron hasthe similar function and structure. We investigate homogeneous spiking neural P systemswith anti-spikes, where each neuron has the same set of rules. Such systems with pure formof spiking rules and without forgetting rules are proved to be universal. In case of usinginhibitory synapse, the equivalence between homogeneous spiking neural P systems withanti-spikes and Turing machine can be achieved. These results have an important sense:the structure of a neural system is crucial for the functioning of the system. Although theindividual neuron is homogeneous, by cooperating with each other, a network of neuronscan be powerful–“complete (Turing) creativity”.We investigate some normal forms of spiking neural P systems with anti-spikes. Specif-ically, in case of using two categories of pure spiking rules, spiking P systems with anti-spikes can achieve Turing completeness. This result improves a corresponding result pro-posed by Pan, which use three categories of non-pure spiking rules. Note that for spikingneural P systems with anti-spike using non-pure spiking rules, determining whether a rulecan be applied is a potential computational hard problem. So, constructing spiking neu-ral P systems with anti-spike using only pure form of spiking rules will provide feasiblecomputing models in practice.Reversible computational models are elementary computing devices in Quantum com- puting. The most important advantage of reversible models is that the cost of energy inthe computing processes is low. We construct reversible spiking neural P systems, and it isproved that reversible spiking neural P systems are universal. These results will constructa connection between quantum theory and bio-computing theory, as well as provide sometheoretical low energy costing bio-computing models.There are some typical motifs and communities in biological neural networks. Neuronsfrom the same motif or community can cooperate with each other to achieve some biologicalfunctions. Inspired from this fact, we construct asynchronous spiking neural P systems withlocal synchronization. It is proved that the systems with general neurons can achieve Turingcompleteness, and the systems with limited neurons can only generate semi-linear sets ofnatural numbers.In classical spiking neural P systems, each operation, such as application of the spikingrule, forgetting rule, cell division, neuron budding, will cost one time unit. However, thisrestriction that each rule has a precise execution time does not coincide with the biologicalfact, since the execution time of bio-chemical reactions can vary because of external uncon-trollable conditions. To avoid this defect, we construct timed and time-free spiking neuralP systems. We investigate the efficiency of the systems by solving a specific computationalhard problem–SAT problem.The most important function of human brain is maintaining human’s cognitive ability.Spiking neural P systems are neural-like computing models inspired from biological neuralsystems, so it is interesting to investigate the “cognitive ability” the systems. In thiswork, spiking neural P system with learning mechanism is constructed to recognizing handwritten English letters. Simulation results show that spiking neural P systems with learningmechanism performs well in recognizing hand written English characters.