University of Birmingham > Talks@bham > Artificial Intelligence and Natural Computation seminars > Untangling the tangled bank: A connectionist view of biological complexity

Untangling the tangled bank: A connectionist view of biological complexity

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For Darwin the network of relationships between species in an ecosystem is splendidly complex but ‘tangled’. Each species may have numerous well-adapted interdependencies with other species, but because each species is independently or ‘selfishly’ motivated by natural selection, and the ecosystem as a whole is not a unit of selection, ecosystem structure cannot be holistically adapted. The work we present challenges this doctrine. We show that evolved changes to inter-species relationships ‘wire together’ species the commonly co-occur. This simple observation has an intuitive explanation but significant consequences for ecosystem behaviour, resilience and functional structure. This kind of change causes an ecosystem as a whole to develop an associative memory that can ‘recall’ past configurations, and under general conditions arrive at configurations of species that are globally adaptive even though each species is acting selfishly. We can understand how these results follow from this observation using associative learning theory from computational neuroscience. This implies that inter-species relationships are not merely tangled, but exhibit adaptive organisational principles in common with connectionist models of organismic learning. Whereas prior evolutionary theory treats ecological relationships and dynamics merely as the backdrop to the adaptation of the entities therein, we suggest a connectionist view of evolutionary adaptation where the relationships between entities and their dynamical interactions take the foreground. Understanding how system-level adaptation is possible in systems of selfish components, and how subsets of synergistic agents mutually reinforce conditions that are self-sustaining, sheds light on vital evolutionary questions such as the evolution of individuality, the major evolutionary transitions and the evolution of biological complexity. We also show how these insights lead to novel optimisation methods that are provably superior to conventional evolutionary algorithms in problems with a nearly-decomposable or modular structure.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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