Slime mould logic gates based on frequency changes of electrical potential oscillation

• Published in: BioSystems 2014-10, Vol.124, p.21-25
• Main Authors: Whiting, James G.H., de Lacy Costello, Ben P.J., Adamatzky, Andrew
• Format: Article
• Language: English
• Subjects: Biological computing; Boolean logic; Electric Stimulation; Physarum polycephalum; Physarum polycephalum - physiology
• ISSN: 0303-2647; 1872-8324
• DOI: 10.1016/j.biosystems.2014.08.001

•Stimuli induced change of frequency of Physarum polycephalum approximate Boolean logic.•Logic circuits XOR, half adder and full adder were 71, 65 and 59% accurate.•Frequency Physarum gates are 30 times faster than growth-based Physarum gates. Physarum polycephalum is a large single amoeba cell, which in its plasmodial phase, forages and connects nearby food sources with protoplasmic tubes. The organism forages for food by growing these tubes towards detected foodstuff, this foraging behaviour is governed by simple rules of photoavoidance and chemotaxis. The electrical activity of the tubes oscillates, creating a peristaltic like action within the tubes, forcing cytoplasm along the lumen; the frequency of this oscillation controls the speed and direction of growth. External stimuli such as light and food cause changes in the oscillation frequency. We demonstrate that using these stimuli as logical inputs we can approximate logic gates using these tubes and derive combinational logic circuits by cascading the gates, with software analysis providing the output of each gate and determining the input of the following gate. Basic gates OR, AND and NOT were correct 90%, 77.8% and 91.7% of the time respectively. Derived logic circuits XOR, half adder and full adder were 70.8%, 65% and 58.8% accurate respectively. Accuracy of the combinational logic decreases as the number of gates is increased, however they are at least as accurate as previous logic approximations using spatial growth of P. polycephalum and up to 30 times as fast at computing the logical output. The results shown here demonstrate a significant advancement in organism-based computing, providing a solid basis for hybrid computers of the future.