Reaction–diffusion chemistry implementation of associative memory neural network

Unconventional computing paradigms are typically very difficult to program. By implementing efficient parallel control architectures such as artificial neural networks, we show that it is possible to program unconventional paradigms with relative ease. The work presented implements correlation matrix memories (a form of artificial neural network based on associative memory) in reaction–diffusion chemistry, and shows that implementations of such artificial neural networks can be trained and act in a similar way to conventional implementations

Belousov-Zhabotinsky reaction in liquid marbles

In Belousov–Zhabotinsky (BZ) type reactions, chemical oxidation waves can be exploited to produce reaction-diffusion processors. This paper reports on a new method of encapsulating BZ solution in a powder coating of either polyethylene (PE) or polytetrafluoroethylene (PTFE), to produce BZ liquid marbles (LMs). BZ LMs have solid-liquid interfaces compared to previously reported encapsulation systems, BZ emulsions and BZ vesicles. Oscillation studies on individual LMs established PE-coated LMs were easier to prepare and more robust than PTFE-coated LMs. Therefore, this coating was used to study BZ LMs positioned in ordered and disordered arrays. Sporadic transfer of excitation waves was observed between LMs in close proximity to each other. These results lay the foundations for future studies on information transmission and processing arrays of BZ LMs. Future work aims to elucidate the effect of other physical stimuli on the dynamics of chemical excitation waves withinthese systems

Chemical Wave Computing from Labware to Electrical Systems

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 detailedinvestigation 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 computingis 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

Simulating Neurons in Reaction-Diffusion Chemistry

Diffusive Computation is a method of using diffusing particles as a representation of data. The work presented attempts to show that through simulating spiking neurons, diffusive computation has at least the same computational power as spiking neural networks. We demonstrate (by simulation) that wavefronts in a Reaction-Diffusion system have a cumulative effect on concentration of reaction components when they arrive at the same point in the reactor, and that a catalyst-free region acts as a threshold on the initiation of an outgoing wave. Spiking neuron models can be mapped onto this system, and therefore RD systems can be used for computation using the same models as are applied to spiking neurons