Liquid brains, solid brains

Abstract

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? To answer this question requires a new, integrative framework. This article is part of the theme issue 'Liquid brains, solid brains: How distributed cognitive architectures process information'.

Keywords: ants; cognition; evolution; immunology; neurons; swarms.

Figure 1.

Example cognitive networks. The figure illustrates four classes of cognitive networks, based on whether or not actual neurons are present or absent and on the physical organization of the network. Standard neuronal networks (a) involve specialized, spatially localized cells connected through synaptic weights. Simpler organisms, such as planarians (flatworms, b) contain neural structures that differ from the more complex brains in panel (a). Inspired by real neurons, artificial arrays of in silico units (c) imitate some of the generic features of their biological counterparts by sensing and responding to environmental signals, but these rigid spatial structures lack neural units. Placozoans (d) lack neurons altogether and have very simple anatomical complexity, but exhibit active behaviours. Using a different architecture, plants also lack neurons but some of their modular parts, including roots (e) and stomata in leaves (f), belong to the ‘solid’ sub-class. Liquid brains include those formed by agents equipped with their own neural or neural-like components such as (g) ant or (h) termite societies and their artificial counterparts in robot swarms (i). In these liquid brains, each component has its own solid brain. A second major class of liquid brains includes mobile components that lack an internal brain such as (j) Physarum, (k) immune networks and (l) microbiome communities. Here there are no neural-like elements and yet in many ways these systems solve complex problems, exhibit learning and memory, and make decisions in response to enivornmental conditions. Finally, there is evidence that both the immune system and the microbiome interact at some level with the brain of the host organism.

Figure 2.

The diversity of computations in liquid and solid networks. Ant colonies (a, courtesy of Guy Theraulaz) can be understood as solving a least-action problem where the shortest path is discovered through preferential choice of paths with the highest pheromone concentration. The single-celled plasmodium of Physarum polycephalum (b) also uses least-action dynamics to solve logic, geometrical, and graph theory problems, including finding the shortest path through a maze. It is less clear how to classify problems solved by planarians (c), a class of flatworms whose nervous system is organized bilaterally with a solid ‘brain’ and two eyes. Planarians feature distributed (versus brain-centric) memory of past events: their morphology is reprogrammable through bioelectric signals which may play a central role in both cognition and development. Finally, single cells contain diverse information-processing phenomena, including complex cascades, genetic–metabolic interactions, and vesicle-associated computations, such as the machinery for storing insulin displayed in (d).