1.1 Introduction

Nature uses an infinite variety of implementing neurons. However, they can cooperate. We base our discussion on those (sometimes ’non-ordinary’) laws, see section 2.4.3, and create an ’abstract physical neuron’ model, skipping the ’implementation details’ nature uses. Our procedure is essentially the same that John von Neumann followed when described the operating principles of computers [3]: ”we will base our considerations on a hypothetical element, which functions essentially like a vacuum tube, without going into details”: our abstract neuron works essentially like a biological neuron. Notice that at that time, it was not yet recognized that the electric signals propagate with a finite speed also in the dendrites (or, more precisely, its handling in mathematics and physics was not solved), not only on the axons; furthermore, that AIS is a separated (and, for forming an AP: vital) component of the neuron. We develop the needed ’non-ordinary’ laws in chapter 2.

We agree that ”The basic structural units of the nervous system are individual neurons” [2]. However, we also know that multiple neurons ”are linked together by dynamically changing constellations of synaptic weights” and ”cell assemblies are best understood in light of their output product” [15]. However, the classic understanding replaces the dynamic description with a perturbation-level correction to a mostly wrong (and century-old) static description. Furthermore, it lacks the laws of motion, and the modern understanding of the operation of the dynamic components the detailed operation requires. The final reason is the wrong understanding of living matter’s non-disciplinary scientific operation, so we must go back to the first principles of science. Here, we give a holistic picture of neuronal operation and provide details in the subsequent chapters.

We agree that ’the fundamental task of the nervous system is to communicate and process information’; furthermore, that ’neurons convey neural information by virtue of electrical and chemical signals’ [2]. The goal was set decades ago: ’The ultimate aim of computational neuroscience is to explain how electrical and chemical signals are used in the brain to represent and process information’ [14]. It was also confirmed two decades later: ”Information is carried within neurons and from neurons to their target cells by electrical and chemical signals. Transient electrical signals are particularly important for carrying time-sensitive information rapidly and over long distances” [41], page 126. Our goal is to describe the role of neuroscience signals in an abstract level, provide their mathematical description based on established physical processes, to understand their interdependence, to explain the physics they use, to find out in the later chapter how at physical level the information is represented and processed.

In the present chapter, we consider only the holistic picture, without details (in an abstract way) in the sense that we attempt to explain what are the principles that neurons’ functionality implement. Our point might be felt as a technical one, although it is not so. We only attempt to stay at an abstraction level, similar to the one cited in connection with communication and information, when formulating neuron’s functionality and features. We keep an eye on the discussion in other chapters and we present a purely physical model with purely physical interactions, in the spirit of the limited interaction speed. We borrow the names of components and their operations from biology. We use physical principles and laws behind them (the details are discussed in separate chapters). We build an artificial (abstract) neuron, using the ’ordinary’ and ’non-ordinary’ laws of physics. We use explicitly the finite speed of ions in electrolytes and derive the ’extraordinary’ laws from the mixing of the interaction speeds. We demonstrate that our abstract model passes the duck test ”If it looks like a duck, swims like a duck, and quacks like a duck, then it probably is a duck”.