M.Sc. C.S. Thesis Defense: Prometheus Peter L. Lazo (Spiking Neural P Systems with Stochastic Application of Rules) (rescheduled)

Zoom Link https://up-edu.zoom.us/j/84169561527

Meeting ID: 841 6956 1527

Passcode: 15969932

Panel Members

Francis George C. Cabarle, PhD, Adviser

Henry N. Adorna, Panel, PhD, Chair

Richelle Ann B. Juayong, PhD, Reader

ABSTRACT

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This work continues the investigations of introducing probabilities to spiking neural P systems, SN P systems in short – membrane computing models inspired from biological spiking neurons. A particular interest for SN P systems in this work is the nondeterministic selection of applicable firing rules. Rules represent the possible reactions of a neuron to the number of electrical impulses, or spikes, present. Intuitively, having nondeterministic selection can be interpreted as having a random choice with equal probabilities for all options. This seems unnatural in some biological sense since some reactions are more active than others in general as emphasized in Obtulowicz, A., & Păun, G. (2003). (In search of ) probabilistic P systems. BioSystems, 70(2), 107-121. This work introduces stochasticity a priori to rule application. As a prerequisite to proposing new a stochastic SN P system, this study proposes an SN P variant featuring rules that can be desynchronized. This study then shows that the proposed SN P system with desynchronized rules is computationally universal when using standard or extended rules. Then an SN P system with stochastic application of rules is proposed. This study also shows how the stochasticity affects SN P systems as well as its computational completeness. Lastly, this study compares and contrast the proposed stochastic SN P system with the classic SN P system, stochastic SN P variants, and SN P variants that use extended rules.