Reaction–diffusion (RD) chemical systems are known to realize sensible computation when both data and results of the computation are encoded in concentration profiles of chemical species; the computation is implemented via spreading and interaction of either diffusive or phase waves, while a silicon RD chip is an electronic analog of the chemical RD systems where the concentration profiles of chemicals are represented by voltage distributions on the chip's surface. In this paper, we present a prototype RD chip implementing a chemical RD processor for a well-known NP-complete problem of computational geometry — computation of a Voronoi diagram. We offer experimental results for fabricated RD chips and compare the accuracy of information processing in silicon analogs of RD processors and their experimental "wetware" prototypes.

/pdf/silicon-implementation-of-a-chemical-reaction-diffusion-2wfsss6ebf.pdf

Silicon implementation of a chemical reaction-diffusion processor for computation of voronoi diagram

A cellular non-linear network (CNN) is a uniform regular array of locally connected continuous-state machines, or nodes, which update their states simultaneously in discrete time. A microbial fuel cell (MFC) is an electro-chemical reactor using the metabolism of bacteria to drive an electrical current. In a CNN model of the MFC, each node takes a vector of states which represent geometrical characteristics of the cell, like the electrodes or impermeable borders, and quantify measurable properties like bacterial population, charges produced and hydrogen ion concentrations. The model allows the study of integral reaction of the MFC, including temporal outputs, to spatial disturbances of the bacterial population and supply of nutrients. The model can also be used to evaluate inhomogeneous configurations of bacterial populations attached on the electrode biofilms.

https://uwe-repository.worktribe.com/preview/883828/BIO_2017_61_source.pdf

Cellular non-linear network model of microbial fuel cell.

Unconventional and, specifically, wave computing has been repeatedly studied in laboratory based experiments by utilizing chemical systems like a thin film of Belousov–Zhabotinsky (BZ) reactions. Nonetheless, the principles demonstrated by this chemical computer were mimicked by mathematical models to enhance the understanding of these systems and enable a more detailed investigation of their capacity. As expected, the computerized counterparts of the laboratory based experiments are faster and less expensive. A further step of acceleration in wave-based computing is the development of electrical circuits that imitate the dynamics of chemical computers. A key component of the electrical circuits is the memristor which facilitates the non-linear behavior of the chemical systems. As part of this concept, the road-map of the inspiration from wave-based computing on chemical media towards the implementation of equivalent systems on oscillating memristive circuits was studied here. For illustration reasons, the most straightforward example was demonstrated, namely the approximation of Boolean gates.

/pdf/chemical-wave-computing-from-labware-to-electrical-systems-cg5r00hg.pdf

Chemical Wave Computing from Labware to Electrical Systems

The possibility to exploit emergent computation in a naturally inspired complex network, namely the reaction-diffusion cellular nonlinear network (RD-CNN), is investigated. The particular application under focus is image processing. It is shown that by implementing a simplified discrete-time model and by using the local activity theory to locate potentially useful regions in the huge parameter space, many useful image processing tasks may be performed in reasonable execution time. Such tasks may include but are not limited to: feature extraction, image enhancement, noise removal, pattern formation, etc. A framework is provided for a systematic design allowing the identification of useful genes (sets of parameters) associated with meaningful image processing tasks.

Applications of Emergent Computation in Reaction-Diffusion CNNs for Image Processing

We numerically investigated the dynamics of a new reaction-diffusion-type excitable medium where the diffusion coefficient is represented by memristive dynamics. This type of a medium consists of an array of excitable Oregonators, and each Oregonator is locally coupled with other Oregonators via memristors, which were claimed to be the fourth circuit element exhibiting a relationship between flux φ and charge q. Through extensive numerical simulations, we found that the memristor conductances were modulated by the excitable waves and controlled the velocity of the waves, depending on the memristor's polarity. Further, different nonuniform spatial patterns were generated depending on the initial condition of Oregonator's state, memristor polarity and stimulation.

/pdf/reaction-diffusion-media-with-excitable-oregonators-coupled-1lekinze8u.pdf

Reaction-diffusion media with excitable oregonators coupled by memristors

We designed a special CMOS circuit for reaction-diffusion computers that accepts optical inputs in parallel and generates excitable spatial waves on a chip surface. We demonstrated the spatiotemporal properties of the proposed circuits by fabricated LSIs.

Reaction-diffusion chip implementing excitable lattices with multiple-valued cellular automata

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Reaction-diffusion chip implementing excitable lattices with multiple-valued cellular automata