To survive, organisms must respond appropriately to a variety of challenges posed by a dynamic and uncertain environment. The mechanisms underlying such responses can in general be framed as input-output devices which map environment states (inputs) to associated responses (output. In this light, it is appealing to attempt to model these systems using information theory, a well developed mathematical framework to describe input-output systems.
Under the information theoretical perspective, an organism’s behavior is fully characterized by the repertoire of its outputs under different environmental conditions. Due to natural selection, it is reasonable to assume this input-output mapping has been fine tuned in such a way as to maximize the organism’s fitness. If that is the case, it should be possible to abstract away the mechanistic implementation details and obtain the general principles that lead to fitness under a certain environment. These can then be used inferentially to both generate hypotheses about the underlying implementation as well as predict novel responses under external perturbations.
In this work I use information theory to address the question of how biological systems generate complex outputs using relatively simple mechanisms in a robust manner. In particular, I will examine how communication and distributed processing can lead to emergent phenomena which allow collective systems to respond in a much richer way than a single organism could
