Foreword

The dynamic operation of individual neurons, their connections, higher-level organizations, connections, the brain with its information processing capability, and finally, the mind with its conscience and behavior, are still among the big mysteries of science: at which point the non-living matter becomes a living one, at which point the living matter becomes intelligent and conscious; whether and how science can handle all this stuff. We really need a new understanding that we provide here. The hype in the newly launched projects is excessive and seems to lose the hope to build brain research on a firm science base. For example, the newly (at the end of 2025) launched “Brain/MINDS 2.0” program in Japan was launched using only mathematical models, without targeting the understanding of the underlying physical processes. In neuron models, ’dynamics’ refers to how the state variables of the neuron (primarily membrane potential and ion channel properties) change and evolve over time in response to internal processes and external inputs. However, unlike in our approach, the usual processes simply use a time parameter in empirical or mathematical functions, see for an example [1]. We use a real dynamics, based on physics, in the sense as Newton introduced his laws of motion to describe response of neurons.

Nature uses an infinite variety of implementing neurons. In the Central Nervous System (CNS) they can cooperate with each other, ’despite the extraordinary diversity and complexity of neuronal morphology and synaptic connectivity, the nervous systems adopt a number of basic principles for all neurons and synapses’, [2] independently from the infinitely complex molecular, biochemical and physiological details. We will base our holistic discussion on those general basic principles and create an ’abstract physical neuron’; a hypothetical element, which functions essentially like a neuron, skipping the ’implementation details’ nature uses. We essentially follow von Neumann’s method in describing the foundations of computing science [3]: ”we will base our considerations on a hypothetical element, which functions essentially like a neuron”. We need different abstractions and approximations (furthermore, ’non-ordinary’ laws of science) for describing biological processes. However, an abstraction is usable in practice only when paired with a generalization: the more abstract the assumption, the more general and widely applicable the concept or conclusion. By abstractions, we can reduce the unbelievably detailed world into manageable pieces, and we can learn anything general. We must show which approximations are oversimplifications and which phenomena are misunderstood; measured, or interpreted in a wrong approach.

For the same reasons, we follow von Neumann’s method when he described principles of technical computing [3]. ”The ideal procedure would be, to take up the specific parts in some definite order, to treat each one of them exhaustively, and go on to the next one only after the predecessor is completely disposed of. However, this seems hardly feasible. The desirable features of the various parts, and the decisions based on them, emerge only after a somewhat zigzagging discussion. It is, therefore, necessary to take up one part first, pass after an incomplete discussion to a second part, return after an equally incomplete discussion of the latter with the combined results to the first part, extend the discussion of the first part without yet concluding it, then possibly go on to a third part, etc. Furthermore, these discussions of specific parts will be mixed with discussions of general principles, of the elements to be used, etc.” For example, basic concepts such as the membrane potential and its regulation in resting and transiens states, belong to chapter Physics, and their detailed scientific, physics-based, discussion is given there, but their corresponding aspects must be discussed in chapters ’Abstract neuron’ and less abstract ’Physiology’ as well. The contents are inherently intertwinned, and the form must follow the contents. We make this zigzag reading more accessible by using hyperlinks and cross-references throughout the document.

The site is not exclusively about theory: we also give a programmed implementation of the ideas we describe. Our simulator has a direct scientific base instead of ad-hoc mathematical formulas; and the only one which can reproduce the true biological time course of neurons, from the first science principles, without arbitrary ad-hoc assumptions and limited validity formulas. Our methods enable discussing the major aspects of phenomena of the natural operation of neurons to analyze the effects of invasive investigation methods on neuroscience. We offer demos, class implementations, performance benchmarks, and test cases to demonstrate simulating capabilities. We intend to develop full-value educational, demonstration, and research tools.