Towards Programmable Smart Materials: Dynamical Reconfiguration of Emergent Transport Networks

We recommend you cite the published version. Disclaimer UWE has obtained warranties from all depositors as to their title in the material deposited and as to their right to deposit such material. UWE makes no representation or warranties of commercial utility, title, or fitness for a particular purpose or any other warranty, express or implied in respect of any material deposited. UWE makes no representation that the use of the materials will not infringe any patent, copyright, trademark or other property or proprietary rights. UWE accepts no liability for any infringement of intellectual property rights in any material deposited but will remove such material from public view pending investigation in the event of an allegation of any such infringement. Smart materials promise adaptive morphology and functionality of materials , however, controlling the desired pattern formation using simple and local bottom-up interactions is a difficult task, but one which living organisms appear to manage effortlessly. We have previously demonstrated a virtual material inspired by the slime mould Physarum polycephalum which, from simple interactions within a swarm based particle collective, forms complex emergent transport networks. One desired characteristic of smart materials is that they should be programmable, adapting their structure in response to external stimuli. As a step towards this aim we suggest a prototype method to dynamically reconfigure emergent transport networks, based on real-time network analysis of the current configuration and feedback via dynamic adjustment of network node weights. The analysis method utilises a novel collective memory of previous network history which is used to provide connectivity information to control a feedback method to the network nodes. Although simple in operation, the feedback method utilises complex neural network-like control including excitation, inhibition and refractory dynamics. The transitions of the reconfiguration method are analysed and high level motifs and transitions are described. We suggest how the dynamical reconfiguration method may be used as a spatially represented unconventional computing method for combinatorial optimisation problems including the Euclidean Travelling Salesman Problem. We conclude by discussing limitations and possible improvements to the dynamical reconfiguration method and exploring the potential advantages of exploring low-level and indirect methods of influence on smart materials.