Status : Verified
| Personal Name | Macababayao, Ivan Cedric H. |
|---|---|
| Resource Title | Normal forms for spiking neural P systems and some of its variants |
| Date Issued | 5 July 2021 |
| Abstract | Spiking Neural P Systems (SN P Systems) are membrane computing systems that are abstracted from the behavior of spiking neurons, or brain cells. These systems take advantage of various mechanisms, such as the ability of neurons to forget, the ability of neurons to create and remove synapses, and many others. Some variants of SN P Systems are (1) SN P Systems with Structural Plasticity, which include the ability to create and delete synapses, and (2) SN P Systems with Rules on Synapses, which associates rules with synapses instead of with neurons. The main goal of this work is to investigate on how simple the neuron in SN P Systems can be. Specifically, we want to see which features of the neuron can be removed or limited while maintaining the computational completeness of the system. We also want to explore some trade-offs between these features. This work formulates normal forms for SN P Systems and two of its variants, the SNPSP and RSSNP Systems. We show, among other results, that computational completeness is possible even when using only one type of regular expression in the whole system, a maximum of two rules per neuron for SN P Systems, one rule per neuron for SNPSP Systems, and one rule per synapse for RSSNP Systems. We also show that SNPSP Systems are universal even when using only plasticity rules, and without synapses in the beginning and end of computation. Lastly, we explore the idea of bounded-optimized normal forms. These are normal forms where the number of unbounded rules in the system are minimized. |
| Degree Course | MS Computer Science |
| Language | English |
| Keyword | natural computing; membrane computing; Spiking Neural P Systems; normal forms |
| Material Type | Thesis/Dissertation |
Preliminary Pages
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Category : F - Regular work, i.e., it has no patentable invention or creation, the author does not wish for personal publication, there is no confidential information.
Access Permission : Open Access