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’.

Ecological memory and relocation decisions in fungal mycelial networks: responses to quantity and location of new resources

“…However, the design allowed us to confirm that mycelia had no preference in growth direction before addition of bait, and thus we can say that there was memory of the predominant direction in which the mycelium developed. Previous studies have categorised the biotic mechanisms of memory in organisms (or swarms) without (central) nervous systems into two types [36]: (1) external memory, which detects signals deposited in the environment; (2) somatic memory, achieved by storage of both epigenetic and/or non-genetic changes of cell physiology. An example of (1) is foraging plasmodia of slime moulds which avoid areas that have previously been explored by detecting deposited extracellular slime [17].…”

“…It is also valid to consider this to be a part of a memory mechanism because the mechanisms of memory in the human brain includes this kind of non-physiological, nonepigenetic phenotypic level change in neuronal network structure [44]. However, although we appreciate that there may be semantic conflicts in the concept of non-neuronal memory among scientists as it is a novel and developing study field [36], we believe that recognizing such a carryover effect as a kind of memory is a first step in the study of non-neuronal intelligence (in the words of Solé et al [36] 'liquid brain') in a broader sense. Determining which of the above-mentioned mechanisms are involved in a mycelial memory in our system is the next experimental challenge.…”

Fate of Duplicated Neural Structures

“…Perhaps it is an unavoidable language. The task is big, but efforts are building up [ 5 , 35 , 36 , 37 , 39 , 48 , 49 , 56 , 60 , 61 , 62 ]. We hope to see important developments soon, hopefully along empirical records of neural circuitry falling within the resulting phase diagrams.…”

“…Cognition is possible in organisms without neurons [ 39 , 40 , 41 , 42 , 43 ]; however, nerve cells are the cornerstone of cognitive complexity. Within the previous framework we wonder how to elaborate a biologically grounded statistical physics of neural circuits ( Figure 1 c).…”

“…Such a theory would dictate the abundance of different kinds of circuits; or which kinds of brains to expect under fixed thermodynamic, computational, and Darwinian conditions. For example, it could tell whether “brains” are likely to have a solid substrate (such as the cortex or laptops [ 39 ]) or a liquid substrate (such as ants, termites, or the immune system [ 39 , 48 , 49 ]). We could also derive phase diagrams saying when computing units behaving as reservoirs (as in Reservoir Computing [ 50 , 51 , 52 , 53 , 54 , 55 ]) are more efficient [ 56 ] or, in a different context, whether redundancy or regeneration is a favored strategy to overcome neural damage [ 37 ] ( Figure 1 d).…”