The new understanding

Hodgkin and Huxley in 1952 [9] enormously advanced neurophysiology by providing a series of observations on neuronal function. However, as they warned, many of the mechanisms must be fixed or replaced: ”must emphasize that the interpretation given is unlikely to provide a correct picture of the membrane”. We honor their outstanding work and want to supplement and enhance their interpretations and hypotheses rather than challenge them. However, their work became the ”Holy Bible” of physiology and a significant obstacle to development in neuroscience. ”Attempts to present a more complete picture of neuronal physiology, have met with fierce opposition from mainstream neuroscience and, as a consequence, currently remain underdeveloped and insufficiently tested. Commonly misunderstood as to their basic premises and the physical principles they are built on, and mistakenly perceived as a threat to the generally acknowledged explanatory power of the ’classical’ HH framework” [35]. The editors ’do not believe’ (see Fig.  2) that science advanced in the past seven decades, and they censor publishing new ideas in scientific journals. Likely, they did not know that science must ”have no respect whatsoever for authority; forget who said it and instead look what he starts with, where he ends up, and ask yourself, ’Is it reasonable?’ …If we suppress all discussion, all criticism, proclaiming ’This is the answer, my friends; man is saved!’ we will doom humanity for a long time to the chains of authority, confined to the limits of our present imagination.” (Richard P. Feynman) Editors of neuroscience-related journals disagree with Feynman. They do not want a ”new understanding”.

As the Human Brain Project formulated, to enter a new level, ”a new understanding of the brain” is needed. It is also correct that ”integration between data and knowledge from different disciplines, and catalysing a community effort” is requested. To form a holistic and coherent picture, one really needs several disciplines, openness, and deep knowledge of the related disciplines from the researchers; furthermore, some knowledge about the bridges to the other disciplines, as Feynman expressed. Furthermore, finding the non-ordinary laws of the underlying physics, including developing the needed mathematical handling, as Schrödinger expected. Moreover, after that, a community effort guided by the principles of the new theoretical understanding is needed to validate and revisit the interpretation of the results of previous observations in light of the ”new understanding”. The present content was written in the spirit of Feynman that we must ”have no respect whatsoever for authority; forget who said it and instead look what he starts with, where he ends up, and ask yourself, ’Is it reasonable?’”. Reading it needs the same attitude. It requires much patience; the more patience and effort, the more the reader comes to know about the subject in the spirit of ”old understanding”. The ”new understanding”, the ”non-ordinary laws” of physics, spiced with newly developed mathematics, is not easy, not quick, and not effortless. Furthermore, it requires well-controlled thinking, much above the level ”this way we used to interpret the things and reply to this question for decades”. Our discussion is a much more faithful description of neurons’ operation than the old one.

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. Research must build on classical science but be reinterpreted for living matter. There is no independent ’life science’; there is only science. It is based on the same ’first principles’ but using different abstractions and approximations for living and non-living matter, and having the appropriate relations between them. ”There is a clear need for a tighter and more carefully managed integration and realignment of the work” [36]. Without aligning the knowledge elements along the first principles, the ”integration between data and knowledge from different disciplines”, lacks ”integration”. However, even the review of the targeted HBP project summarized that ”HBP is not developing with the expected level of integration and the project controls in place are not adequate to achieve this aim.” [36]. ”More Is Different” [37].

We provide (at least, part of) “a new understanding of the brain”. Philosophically, we rebase biology on new approximations to the same first principles rather than using the old approximations used in inanimate science. We ‘integrate and realign’ seemingly distant scientific fields by applying the abstractions and approximations required for living matter. We have reached the boundaries of classical scientific disciplines, as we have many times over the past century. We are now moving through terra incognita, where we cannot navigate using the classical disciplinary science. Putting different disciplines side by side, separated according to phenomena in inanimate science, is undoubtedly not perfect and sufficient for describing living matter because of its “different construction” as Schrödinger formulated [12] decades ago. We make a fresh start by rethinking some fundamental ideas in science [38] that appear divergent across these major scientific fields. Research must build upon classical science, but be reinterpreted in light of living matter.

Biology represents a complex case where phenomena cannot be reasonably reduced to a single discipline, as classic inanimate science does. Cell biochemistry sufficiently well describes the lipid bilayers that constitute the membrane [39, 40]. The negative and positive ends of the lipids that constitute the membrane, in an electrolyte solution, attract and bind ions, forming a charged layer on the membrane surfaces. In this way, the membrane in electrolytes acts as a condenser, comprising two charged sheets on its surfaces, with ill-defined parameters. Their boundaries, thicknesses, and compositions depend on the operating state (unlike conducting plates with well-defined sizes and boundaries). Cell science does not establish its electrical phenomena; instead, it attempts to elucidate complex protein activities that explain charge transfer, resting and transient potential, and the entire biological operation using mathematical formulas without a realistic physical background. Electrophysiology combines currents of electrons and ions, and uses Ohm’s Law for its admittedly non-Ohmic systems. It introduces foreign (clamping) currents into biological systems, attributing their effects to a change in membrane conductance without explaining how voltage and current can be independent of charge carriers. By introducing current feedback (which, by definition, compensates for any gradient in the system), it applies an opposite-phase control unit against the neuron’s native control unit and concludes that, after compensation, there is no gradient in the system; in other words, that life exists without needing a driving force. It is not aware that inside the neuron, there is a condenser with a voltage difference between its plates; instead, it believes that a ‘leakage current’ flows through resistors distributed over the membrane. That current is special in that, among other things, it has neither a chemical composition nor charge carriers; furthermore, it can not only emit but also absorb heat. Neuroscience observes thermodynamic and mechanical changes but cannot connect them to electrical phenomena. Lacking the required physical background, biology claims that physical laws cannot describe processes in living matter; without providing a plausible and self-consistent theoretical model. We show that, upon closer inspection, the concepts with the same name have different interpretations. We must understand that physical laws are about ’net’ cases, while biology is about mixed cases, on the boundary of the classical disciplines, or even on the ’nobody’s land. We must build a bridge (or maybe a new sub-discipline), concepts and measurement methods for that ’different contruction’, instead of abusing concepts and test methods of inanimate science on living matter.

We proceed in a zigzag way when discussing different levels of understanding. Our discussion is closely related to physics, discussed in chapter 2. When discussing the underlying physical laws, we assume a knowledge of classical physics above college-level and go back to the fundamental physical concepts and principles instead of taking over the approximations and abstractions (in this context: ordinary laws of physics) used in the classical physics for non-biological matter and less complex (strictly pair-wise, single type, finite interaction speed in homogeneous isotropic medium) interactions. We provide a holistic picture, from a physical point of view, by explaining which physical/physiological components cooperate. We derive the laws of motion for thermodynamical/physiological processes of biology in section 2.4.5 (in this context, ’non-ordinary laws of physics’), and introduce a component which implements them. In chapter 3 we go to a less abstract level (”abstract physiology”). We summarize how the components are put together to form, conceptually, the dynamic operation of neural systems, including that why the action potential is evoked; futhermore, in general, how the processes happen. This abstract discussion serves as a basis for explaining what computing for biology means, see chapter 4; how the idea of computing can be generalized to include the biological implementation; furthermore, how biology implements those general computing principles. Similarly, we use these abstract concepts when we go one step closer to the mystery of how biological information is represented, encoded and decoded, transmitted and processed, in chapter 5. After reviewing the operation of individual neurons, we study the cooperation of their ”constellations” in chapter 6. We separate the operation of single neurons from the cooperation of neurons, a vital point for understanding the brain’s elementary operation. We show how those elementary operations constrain the functionality of vast populations of neurons, in the context of the notion of intelligence in chapter 7. We also provide hints on how those operations affect intelligence.