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

June 11, 2021


4:30 p.m. - 6:30 p.m.

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



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.