Int J Neural Syst - Understanding Networks of Computing Chemical Droplet Neurons Based on Information Flow.

Tópicos

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Resumo

In this paper, we present general methods that can be used to explore the information processing potential of a medium composed of oscillating (self-exciting) droplets. Networks of Belousov-Zhabotinsky (BZ) droplets seem especially interesting as chemical reaction-diffusion computers because their time evolution is qualitatively similar to neural network activity. Moreover, such networks can be self-generated in microfluidic reactors. However, it is hard to track and to understand the function performed by a medium composed of droplets due to its complex dynamics. Corresponding to recurrent neural networks, the flow of excitations in a network of droplets is not limited to a single direction and spreads throughout the whole medium. In this work, we analyze the operation performed by droplet systems by monitoring the information flow. This is achieved by measuring mutual information and time delayed mutual information of the discretized time evolution of individual droplets. To link the model with reality, we use experimental results to estimate the parameters of droplet interactions. We exemplarily investigate an evolutionary generated droplet structure that operates as a NOR gate. The presented methods can be applied to networks composed of at least hundreds of droplets.

Resumo Limpo

paper present general method can use explor inform process potenti medium compos oscil selfexcit droplet network belousovzhabotinski bz droplet seem especi interest chemic reactiondiffus comput time evolut qualit similar neural network activ moreov network can selfgener microfluid reactor howev hard track understand function perform medium compos droplet due complex dynam correspond recurr neural network flow excit network droplet limit singl direct spread throughout whole medium work analyz oper perform droplet system monitor inform flow achiev measur mutual inform time delay mutual inform discret time evolut individu droplet link model realiti use experiment result estim paramet droplet interact exemplarili investig evolutionari generat droplet structur oper gate present method can appli network compos least hundr droplet

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