Transformations and multi-scale optimisation in biological adaptive networks
نویسندگان
چکیده
The natural energy minimisation behaviour of a dynamical system can be interpreted as a simple optimisation process, finding a locally optimal resolution of constraints between system variables. In human problem solving, high-dimensional problems are often made much easier by inferring a low-dimensional model of the system in which search is more effective. But this is an approach that seems to require top-down domain knowledge; not one amenable to the spontaneous energy minimisation behaviour of a natural dynamical system. However, in recent work we investigated the ability of distributed dynamical systems to improve their constraint resolution ability over time by self-organisation. Using a ‘self-modelling’ Hopfield network with a particular type of associative connection we illustrated how slowly changing relationships between system components results in a transformation into a new system, a low-dimensional caricature of the original system, in which the energy minimisation behaviour is significantly more effective at globally resolving system constraints. This uses only very simple and fully-distributed positive feedback mechanisms that are relevant to other ‘active linking’ and adaptive networks. Here we overview the implications of this neural network model for understanding transformations and emergent collective behaviour in various non-neural adaptive networks such as social, genetic and in particular, ecological networks.
منابع مشابه
Transformations in the Scale of Behaviour and the Global Optimisation of Constraints in Adaptive Networks Short Title: Transformations and Optimisation in Adaptive Networks
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