”Understanding the relative computational advantages of liquid versus solid systems is important scientifically, and in the future these understandings are likely to suggest important new ways of thinking about and configuring traditional computations. As computation continues to move off the desktop and into the physical world, we expect that liquid systems characterized by networks of distributed, mobile, and largely autonomous components will become even more important than they are today.” [April 22, 2019] https://royalsocietypublishing.org/doi/10.1098/rstb.2019.0040
Liquid cognitive systems, in addition to the ‘solid’ brain model, includes:
“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… The moving hypothesis posits that active exploration of an organism’s spatial environment was a key step in the evolutionary trajectory that produced brains… Predation was likely the most important selective pressure to create learning networks . This powerful evolutionary innovation was not limited to neural networks, and we suggest here that the many kinds of networks of interacting agents that evolved to process information have characteristic similarities and differences.
“Information processing networks can be found in microbial communities, inside cells (as gene regulatory webs), and in immune systems. The diversity of architectures and information-processing strategies of these networks is stunning. Fluid webs of information exchanges among thousands or even millions of ants or termites unfold in most of the biosphere [7,8]. Simple life forms known as slime moulds, made of a single macroscopic (multinucleated) cell, can solve complex problems. Plants seem to occupy a very different region of the space of cognitive networks, lacking neural-like structures and physical movement, yet defining a tremendously successful and ecologically important group. Liquid computers and chemical reactions provide a rather different set of case studies, where computation and informational processes are not clearly defined. In this context, developmental programmes and pattern formation are also considered to be forms of cognition…
“Are there strategies that have been discovered by natural evolution that could lead to new forms of computation, perhaps using synthetic biology? Answering these and other fundamental questions was the goal of a small workshop held at the Santa Fe Institute in December 2017. The meeting convened a group of researchers from diverse fields of science and engineering, including social insect behaviour, microbiology, synthetic biology, developmental and systems biology, neuroscience, computer science and statistical physics. Over several days, the participants took the initial steps towards formulating a theory of liquid versus solid brains with the long-term goal of establishing the basis of a general theory of cognitive networks.
…”nonlinear character requires (in most cases) an appeal to extended views of computation beyond standard definitions . In addition, the collective dynamics exhibited by large populations of agents interacting nonlinearly depends critically on whether or not the basic network components are mobile. We identified two key dimensions to characterize different categories of cognitive networks (figure 1): the physical characteristics of the system, and the presence or absence of neurons.
…”Even before complex neuronal networks evolved, microorganisms discovered collective structures that could respond to stressful environments, especially those that posed threats to individual cells. Survival was thus tied to cooperation, and cooperation required novel forms of communication within collectives. To quote James Shapiro: ‘bacteria are small but not stupid’ . A well-known example of this level of collective behaviour is quorum sensing (QS), a process that involves populations of cells working cooperatively . QS allows groups of bacteria to monitor the presence of other bacteria at a population-wide scale, leading in some cases to the emergence of colony-level coordinated responses. This illustrates how microbial colonies can make collective decisions. In another vein, the collective behaviour of biofilms is illustrated by recent work on long-range electrical communication in bacterial communities . Martinez-Corral et al.  investigate how similar chemical signalling might exist in both cortical brain activity and biofilm dynamics.
“Slime moulds Physarum polycephalum are a particularly fascinating example of collective behaviour by aggregates of single cells. Although the organism is single-celled (but including multiple nuclei), in groups it displays highly complex spatial morphological patterns… they also display habituation, i.e. a common adaptive response (displayed by neural organisms) to an unpleasant persistent stimulus. This finding supports the idea that brainless systems can under the right conditions learn from experience to discriminate diverse sources of information.
“The boundaries of cognition space can be delineated by considering the simplest ‘solid’ brains and asking how they do their jobs compared with similarly simple liquid examples . Planarians (flatworms) are a candidate for the first true (i.e. centralized) brains [2,13]. Of particular interest is the tight integration of developmental and cognitive phenomena. As pointed out in , remarkable information processing tasks were evolved long before solid brains emerged. Planarians can regenerate every part of their bodies  and experimental studies show that memories survive decapitation (see Shomrat & Levin  and references therein). These results point to a deep connection between neural-based phenomena and somatic memory. Importantly, many developmental responses to perturbations can be mapped into an attractor diagram that represents morphological end states as attractors. The dynamics leading to these attractor-based responses can be implemented in very different types of non-neural hardware, although we still lack a common theoretical framework for describing these systems, as discussed in .
“The solid, aneural region of cognitive space is shared with other groups of living organisms with different organizations, life styles, and life cycles. Plants, in particular, define a limiting case [28,29]. The cognitive potential of plants was recognized as early as Darwin in a monograph , where he pointed to the interesting responses displayed by plants to external signals and environmental cues. Plants exhibit responses that suggest interesting computational abilities , and the concept of ‘plant intelligence’  has also been developed (with some degree of controversy) in recent decades. Communication at multiple scales, in particular, has been of interest, ranging from networks of stomata in leaves to signals sent through root systems. These examples point to the need for better understanding of information processing in plants , including genetic switches and analogue computations that take place within the process of seed dispersal and germination . Intriguingly, these processes involve the ‘movable’ part of the plant’s life cycle.
…”Vining et al.  develop liquid cellular automata to demonstrate how liquid systems compute without sophisticated physical network structures. Mobility is shown to increase information flow among moving agents, which encounter and communicate with new agents over time…
…”Understanding the relative computational advantages of liquid versus solid systems is important scientifically, and in the future these understandings are likely to suggest important new ways of thinking about and configuring traditional computations. As computation continues to move off the desktop and into the physical world, we expect that liquid systems characterized by networks of distributed, mobile, and largely autonomous components will become even more important than they are today.” https://royalsocietypublishing.org/doi/10.1098/rstb.2019.0040
[same issue: “This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’]…” We hypothesize that a cognitive-level information-processing view of the functions of living systems can complement reductive perspectives, improving efficient top-down control of organism-level outcomes. Exploration of the deep parallels across diverse quantitative paradigms will drive integrative advances in evolutionary biology, regenerative medicine, synthetic bioengineering, cognitive neuroscience and artificial intelligence.” https://royalsocietypublishing.org/doi/10.1098/rstb.2018.0369