All that we derive here needs ’zigzag reading’. As emphasized many times, we confine our discussion to some abstract non-disciplinary level, with pointers to special topics.
We interpret known physiological evidence on top of the correct ’non-ordinary’ laws of physics and derive the needed mathematical handling separately but in parallel with that physics. The physiological conclusions should be understood even if one does not understand the underlying physical and mathematical details. For this reason, we repeat the explanations in greater detail in different chapters. If you understand why the non-ordinary physical laws for living matter are more or less different from the ordinary ones for non-living matter, you may leave the details for your expert colleagues (physicists and mathematicians). Mathematical handling also involves the fundamental principles of using the abstractions and approximations of constructing laws for physics reqires a thorough knowledge of both fields. Anyhow, you will need to be aware that college-level physics is usually not sufficient to understand the physics is usually not sufficient to understand the depth of the material, and you will need to re-read the concepts; the more you know it (and especially the more false biophysics you learned), the more carefully. A half-understanding of the physical base hinders your learning and the development of brain science.
Nature is overly complex: science fields must use different approximations and abstractions. When setting up a holistic model of a neuron, we attempt to see the forest for the trees. We must pass between ’Scylla’ and ’Charybdis’: being still sufficiently accurate and detailed in describing phenomena while keeping the mathematical complexity (and computational need) still manageable. Without understanding that living matter needs different approaches and testing methods from science, really, ”no single researcher or discovery [and we add: even no ’vibrant ecosystem for rigorous and ethical research with human research participants as partners’ or ’a community effort’] will solve the brain’s mysteries” [5]. We must express the same fundamental principles in the form of laws that differ from those valid for classical physics for non-living material, and validate them. To do so, we must revisit whether we made the appropriate simplifications and approximations, and whether we correctly mapped those phenomena to the corresponding mathematical descriptions. We must dispel some important misconceptions, present the correct conceptions, and explain why the wrong ones misguide research.
We aim to proceed along the lines (but fixing its conceptual mistakes, mainly the rigid disciplinarity) that was formulated @2012 as “The HBP should lay the technical foundation for a new model of Info-Communication Technology (ICT) -based brain research, driving integration between data and knowledge from different disciplines, and to achieve a new understanding of the brain…and new brain-like computing technologies.” [31]. Moreover, in the Brain Initiative (BI) , ”There is a clear need for a tighter and more carefully managed integration and realignment of the work” [5]. However, ”HBP is not developing with the expected level of integration and the project controls in place are not adequate to achieve this aim.” [31]. The ”great journey into the unknown” [5] must begin at a much lower level: revisiting the fundamental phenomena, disciplines, laws, interactions, abstractions, omissions, and testing methods of science.
You may have arrived with different backgrounds and goals. Consequently, you may have different spots of interest and paths through this material. The site is about neuron-based computing, which, for today, may mean very different topics. Of course, one must first understand the physical operations. The primary goal is to deal with biologically implemented neurons’ operation (they are created ’as is’; maybe not entirely understood, but attempting to discover it) and also artificially manufactured neurons which attempt to imitate the biological ones, grasping one or more of their features; furthermore, their networks, operations, features, and fallacies. A further goal is to understand the features of their larger assemblies and how they implement advanced computations.