Implementation and Optimization of Chemical Logic Gates Using Memristive Cellular Automata

By utilizing biologically inspired approaches, a wide range of complex and computationally intensive problems can be transformed to simpler and more appropriate forms to be easily solved by unconventional computing systems. A well-known computing platform with such characteristics is the Cellular Automata paradigm, where a spatial-extended network of nodes, with local interactions, exhibit emerging computations. In such CA networks, the application of nanodevices, like memristors, with inherent novel abilities, like memory storing and computing capabilities, together with nonlinear interactions is promising for the advancement of computation. In this work, a memristor-based Cellular Automaton (MemCA) is developed for the implementation and optimization of topological chemical logic gates. The proposed MemCA is inspired by the behaviour of the biological organism Physarum Polycephalum that firstly spreads to reach nutrients in its environment and afterwards shrinks to optimize its energy requirements, while performing biochemical oscillations to accomplish these tasks. In a similar way, the MemCA simulates Physarum's spreading to perform the spatial operation of the chemical logic gate, while Physarum's shrinking was utilised to further optimise the required area of the gate.