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An autonomous decentralized control mechanism, where the coordination of simple individual components yields non-trivial macroscopic behavior or functionalities, is a key to understanding how animals orchestrate the large degrees of freedom of their bodies in response to different situations. However, a systematic design methodology is still missing. To alleviate this problem, we focus, in this chapter, on the plasmodium of a true slime mold (Physarum polycephalum), which is a primitive multinucleate single-cell organism. Despite its primitiveness, and lacking a brain and nervous system, the plasmodium exhibits surprisingly adaptive and versatile behavior (e.g. taxis, exploration). This ability has undoubtedly been honed by evolutionary selection pressure, and there likely exists an ingenious mechanism that underlies the animals’ adaptive behavior. We successfully extracted a design scheme for decentralized control and implemented it in an amoeboid robot with many degrees of freedom. The experimental results showed that adaptive behaviors emerge even in the absence of any centralized control architecture.

The notion that brains and nervous systems are necessary for intelligence is dubbed brain chauvinism. Organisms cannot rely alone on simple reflexes in complex environments; there is insufficient room in the genome for solutions for all to be encoded. Organisms learn from experience and apply that knowledge to future challenges. Learning is central to all intelligent behaviour. If reason is profiting from experience to modify current behaviour, then most organisms are capable of reason whether it be simple or complex. Examples of current behaviour being modified in this way are described for slime moulds in its response to food sources and discrimination between food sources to provide an optimal diet. Slime moulds also learn the frequency of applied electric shocks and anticipate future ones. Observations on amoebaand parameciumindicate a capability for learning and thus profiting from experience. Stentorhas been observed to express a variety of behaviours according to the stimulus and previous experience. This single-celled organism is therefore capable of simple reasoning. Cooperative hunting was observed in amoeba. The potential inherent in large aggregated communities of bacteria is pointed out and several examples quoted. Bacterial intelligence is also claimed for signal transduction assessments. Communication in bacteria, that is meaning-based communication permitting colonial identity, intentional behaviour (e.g. pheromone based-courtship for mating), purposeful alteration of colony structure (e.g. formation of fruiting bodies), decision making (e.g. sporulation) and recognition and identification of other colonies, are credited with and resulting from a bacterial social intelligence and wisdom.

The theory that the whole universe is a computer is a bold and striking one. It is a theory of everything: the entire universe is to be understood, fundamentally, in terms of the universal computing machine that Alan Turing introduced in 1936. We distinguish between two versions of this grand-scale theory and explain what the universe would have to be like for one or both versions to be true. Spoiler: the question is in fact wide open—at the present stage of science, nobody knows whether it’s true or false that the whole universe is a computer. But the issues are as fascinating as they are important, so it’s certainly worth while discussing them. We begin right at the beginning: what exactly is a computer? To start with the obvious, your laptop is a computer. But there are also computers very different from your laptop—tiny embedded computers inside watches, and giant networked supercomputers like China’s Tianhe-2, for example. So what feature do all computers have in common? What is it that makes them all computers? Colossus was a computer, even though (as explained in Chapter 14) it did not make use of stored programs and could do very few of the things that a modern laptop can do (not even long multiplication). Turing’s ACE (see Chapters 21 and 22) was a computer, even though its design was unlike that of a laptop; for example, the ACE had no central processing unit (CPU), and moreover it stored its data and programs in the form of ‘pings’ of supersonic sound travelling along tubes of liquid. Turing’s artificial neural nets were also computers (Chapter 29), and so are the modern brain-mimicking ‘connectionist’ networks that Turing anticipated. In connectionist networks—as in a human brain, but unlike a laptop—there is no separation between memory and processing, and the very same ‘hardware’ that does the processing (the neurons and their connections) also functions as the memory. Even Babbage’s Analytical Engine (Chapter 24) was a computer, despite being built from mechanical rather than electrical parts.

Including a chapter on biological oscillators was not an easy decision. In one sense, no book on nonlinear chemical dynamics would be complete without such a chapter. Not only are the most important and most numerous examples of chemical oscillators to be found in living systems, but the lure of gaining some insight into the workings of biological oscillators and into the remarkable parallels between chemical and biological oscillators attracts many, perhaps most, new initiates to the study of “exotic” chemical systems. On the other hand, it is impossible for us to do even a minimal job of covering the ground that ought to be covered, either in breadth or in depth. To say that the subject demands a whole book is to understate the case badly. There are indeed whole books, many of them excellent, devoted to various aspects of biological oscillators. We mention here only four of our favorites, the volumes by Winfree (1980), Glass and Mackey (1988), Murray (1993) and Goldbeter (1996). Having abandoned the unreachable goal of surveying the field, even superficially, we have opted to present brief looks at a handful of oscillatory phenomena in biology. Even here, our treatment will only scratch the surface. We suspect that, for the expert, this chapter will be the least satisfying in the book. Nonetheless, we have included it because it may also prove to be the most inspiring chapter for the novice. The range of periods of biological oscillators is considerable, as shown in Table 13.1. In this chapter, we focus on three examples of biological oscillation: the activity of neurons; polymerization of microtubulcs; and certain pathological conditions, known as dynamical diseases, that arise from changes in natural biological rhythms. With the possible exception of the first topic, these are not among the best-known nor the most thoroughly studied biological oscillators; they have been chosen because we feel that they can be presented, in a few pages, at a level that will give the reader a sense of the fascinating range of problems offered by biological systems.