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What are neural network models, what kind of cognitive processes can they perform, and what do they teach us about representations and consciousness? First, this chapter explains the functioning of reduced neuron models. We construct neural networks using these building blocks and explore how they accomplish memory, categorization and other tasks. Computational advantages of parallel-distributed networks are considered, and we explore their emergent properties, such as in pattern completion. Artificial neural networks appear instructive for understanding consciousness, as they illustrate how stable representations can be achieved in dynamic systems. More importantly, they show how low-level processes result in high-level phenomena such as memory retrieval. However, an essential remaining problem is that neural networks do not possess a mechanism specifying what kind of information (e.g. sensory modality) they process. Going back to the classic labeled-lines hypothesis, it is argued that this hypothesis does not offer a solution to the question how the brain differentiates the various sensory inputs it receives into distinct modalities. The brain is observed to live in a "Cuneiform room" by which it only receives and emits spike messages: these are the only source materials by which it can construct modally differentiated experiences.

Organisms that developed independent existence had to rely on external sources of energy and, in plants, photosynthesis was probably the end of an evolutionary process that saw cells free living in sunlight. How did this process evolve? The earliest organisms thought to be bacteria are potentially detectable 3.5 billion years ago. Photosynthesis commenced about 3 billion years ago or so with oxygen-producing organisms in abundance 2.7 billion years ago. Fossil stromatolites, 2.2 billion years old, and containing blue-green algae and bacteria are well established, and, astonishingly, are very similar to present-day stromatolites. What provided the impetus for early molecular primordia to eventually generate a living cell? A robust energy supply is undoubtedly essential. Early chemical reactions would have to be coupled directly or indirectly to it. Providing the energy supply is sustained, Prigogine’s dissipative mechanism, seeing order derive increasingly from continued energy flow, is the crucial underpin. The early molecular components would have to be connected to form an integrated, holistic system of low entropy and information flow between them. With increasing experimentation, a stabilizing hierarchical structure would come to dominate, initially, between molecules, then groups of molecules as modules. This early system had to become teleonomic; that is being purposive in maintenance and replication. Negative feedback would have helped stabilize the early structure by keeping the internal environment constant, but may have evolved to counteract destabilizing noise. Each of these criteria is discussed in this chapter to try and provide understanding of this vital event.

People have always been fascinated by cooperation and altruism in animals, in part to shed light on our own propensity or reluctance to help others. Darwin’s theory added a certain urgency to the subject because the principle of “nature red in tooth and claw” superficially seems to deny the possibility of altruism and cooperation altogether. Some evolutionary biologists have accepted and even reveled in this vision of nature, giving rise to statements such as “the economy of nature is competitive from beginning to end . . . scratch an ‘altruist’ and watch a hypocrite bleed”. Others have gone so far in the opposite direction as to proclaim the entire earth a unit that cooperatively regulates its own atmosphere (Lovelock 1979). The truth is somewhere between these two extremes; cooperation and altruism can evolve but only if special conditions are met. As might be expected from the polarized views outlined above, achieving this middle ground has been a difficult process. Science is often portrayed as a heroic march to the truth, but in this case, it is more like the Three Stooges trying to move a piano. I don’t mean to underestimate the progress that been made—the piano has been moved—but we need to appreciate the twists, turns, and reversals in addition to the final location. To see why cooperation and altruism pose a problem for evolutionary theory, consider the evolution of a nonsocial adaptation, such as cryptic coloration. Imagine a population of moths that vary in the degree to which they match their background. Every generation, the most conspicuous moths are detected and eaten by predators while the most cryptic moths survive and reproduce. If offspring resemble their parents, then the average moth will become more cryptic with every generation. Anyone who has beheld a moth that looks exactly like a leaf, right down to the veins and simulated herbivore damage, cannot fail to be impressed by the power of natural selection to evolve breathtaking adaptations at the individual level. Now consider the same process for a social adaptation, such as members of a group warning each other about approaching predators.

Simple or ‘complicated’ systems that display linear, predictable behaviours are contrasted with complex systems characterised by ‘emergent properties’ arising from inter-relationships between parts, feedback loops and sometimes unexpected effects arising from changes in other parts of the system. Particular characteristics of complex social systems and learning from change processes in complex social systems are described. The relevance of systems thinking to epidemiology, public health research and ‘evidence’ is noted including the importance of recognising wider context, the relationship between knowledge and uncertainty and development of new methodologies. It is noted that approaches to public health such as socio-ecological, community development and policy, regulation and ‘whole organisation’ approaches show some features of systemic approaches but that this is not always explicit or consistent. Opportunities and barriers to further development of systemic approaches to public health in the new organisational arrangements for public health in England are highlighted.

At first blush, computing and biology seem an odd couple, yet they formed a liaison of sorts from the very first years of the electronic digital computer. Following a seminal paper published in 1943 by neurophysiologist Warren McCulloch and mathematical logician Warren Pitts on a mathematical model of neuronal activity, John von Neumann of the Institute of Advanced Study, Princeton, presented at a symposium in 1948 a paper that compared the behaviors of computer circuits and neuronal circuits in the brain. The resulting publication was the fountainhead of what came to be called cellular automata in the 1960s. Von Neumann’s insight was the parallel between the abstraction of biological neurons (nerve cells) as natural binary (on–off) switches and the abstraction of physical computer circuit elements (at the time, relays and vacuum tubes) as artificial binary switches. His ambition was to unify the two and construct a formal universal theory. One remarkable aspect of von Neumann’s program was inspired by the biology: His universal automata must be able to self-reproduce. So his neuron-like automata must be both computational and constructive. In 1955, invited by Yale University to deliver the Silliman Lectures for 1956, von Neumann chose as his topic the relationship between the computer and the brain. He died before being able to deliver the lectures, but the unfinished manuscript was published by Yale University Press under the title The Computer and the Brain (1958). Von Neumann’s definitive writings on self-reproducing cellular automata, edited by his one-time collaborator Arthur Burks of the University of Michigan, was eventually published in 1966 as the book Theory of Self-Reproducing Automata. A possible structure of a von Neumann–style cellular automaton is depicted in Figure 7.1. It comprises a (finite or infinite) configuration of cells in which a cell can be in one of a finite set of states. The state of a cell at any time t is determined by its own state and those of its immediate neighbors in the preceding point of time t – 1, according to a state transition rule.

In this dialogue, the Harvard neuroscientist, Alvaro Pascual-Leone initially reflects on the importance of ‘unlearning’ and forgetting. He then gives a detailed explanation of, and how he carries out, transcraneal magnetic stimulation (TMS) and how he uses this technology to fight diseases, as well as explaining his experiments on inattentional blindness. He then discusses how the brain acts as a hypothesis generator and whether the brain, the mind and the soul are different things or not. Later reflect on the questions: Is the mind and what we are a consequence of the brain’s structure?  Do changes in the brain change our reality? And why are a person’s dreams important? Then he explains how freewill and decision-making work from the brain, and relates his vision of intelligence and where it may be generated from, explaining the differences between the mind and the brain. He finally reflects on what is known so far about the brain’s “dark energy” and the way we are continuously being surprised by the wonders of the brain's plasticity.