Statistical physics of liquid brains

摘要

Liquid neural networks (or 'liquid brains') are a widespread class of cognitive living networks characterized by a common feature: the agents (ants or immune cells, for example) move in space. Thus, no fixed, long-term agentagent connections are maintained, in contrast with standard neural systems. Howis this class of systems capable of displaying cognitive abilities, fromlearning to decision-making? In this paper, the collective dynamics, memory and learningproperties of liquid brains is explored under the perspective of statistical physics. Usingacomparative approach, we reviewthegenericproperties of three large classes of systems, namely: standard neural networks (solid brains), ant colonies and the immune system. It is shown that, despite their intrinsic physical differences, these systems share key properties with standard neural systems in terms of formal descriptions, but strongly depart in otherways. On one hand, the attractors found in liquid brains are not always based on connectionweights but instead on population abundances. However, some liquid systems use fluctuations in ways similar to those found in cortical networks, suggesting a relevant role for criticality as a way of rapidly reacting to external signals.