Optimal Decision-Making in Brains and Social Insect Colonies

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By James Marshall, Bristol.

The problem of how to compromise between speed and accuracy in decision-making faces organisms at many levels of biological complexity. Recent models of decision-making in the vertebrate brain have shown how simple neural models can implement statistically optimal decision-making processes that are able to minimise decision time for any desired error rate. Such models consider mutually inhibitory populations of neurons. The activation threshold required of these populations can be varied to emphasise speed, or accuracy, of decision-making. There are some striking similarities between these models and models of the decision-making processes in social insect colonies when searching for a new nest site. In this talk I will present stochastic models of collective behaviour in social insects that can be directly compared with corresponding neural models, and show how they may also implement statistically optimal decision-making in a similar manner to neural circuits.

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