Distributed decision making in mycelial fungi

Abstract:
Woodland basidiomycete fungi have to explore to find new resources that are usually distributed patchily in space and time. As they lack the ability to move, they have to establish a network to allow transport of resources throughout the system between sources and sinks. Within the network there is a Darwinian-like developmental pattern with over-production of links, selection of a sub-set and regression and recycling of the rest. Although there have been hints of long-range communication through electrical signalling, the current working assumption is that the structure and function of these networks emerges from iteration of local rules running in parallel that lead to global behaviour. We are currently exploring a new class of agent-based models (network automata arXiv:physics/0701307v1) that capture the essence of networked fungi to understand the minimum amount on information required that might lead to distributed decision making. As a separate strand, the macroscopic behaviour of these systems has been likened to a foraging strategy, with each strategy presumably representing a balance between cost, coverage, persistance and exposure to damage or predation. Each species appears to switch between a subset of strategies depending on the internal nutrient state and the external nutrient cues. At one level, we can characterise the network using concepts borrowed from graph theory and statistical mechanics to allow comparison with other biological networks including ant-trails, ant-colonies, slime moulds, vascular development or neuronal network formation. At a higher level we can try to model the ecological significance of different foraging behaviours both in silico and experimentally. We are just beginning to think in terms of the foraging models developed in the animal field, and particularly how such networked organisms may optimise behaviour and whether behaviour and decision-making in such systems can be modified by the fungal equivalent of memory and prior experience.