Brains are powerful prediction machines. They evolved to process information and have become a dominant computation paradigm both in nature and now also in the development of artificial intelligence, but they are but one of many different kinds of information-processing strategies and architectures based on nonlinear networks of interacting agents. Some more solid, some more liquid.
Cognitive networks have evolved a broad range of solutions to the problem of gathering, storing and responding to information. Some of these networks are describable as static sets of neurons linked in an adaptive web of connections. These are ‘solid’ networks, with a well-defined and physically persistent architecture. Other systems are formed by sets of agents that exchange, store and process information but without persistent connections or move relative to each other in physical space. We refer to these networks that lack stable connections and static elements as ‘liquid’ brains, a category that includes ant and termite colonies, immune systems and some microbiomes and slime moulds. What are the key differences between solid and liquid brains, particularly in their cognitive potential, ability to solve particular problems and environments, and information-processing strategies?Solé Ricard, et al. ‘Liquid Brains, Solid Brains’. Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 374, no. 1774, June 2019, p. 20190040. royalsocietypublishing.org (Atypon), doi:10.1098/rstb.2019.0040.
Understanding them would allow us to think of systems that have not yet been discovered by evolution.