Filling the gap between neuronal activity and macroscopic functional brain behavior

Abstract

Two complementary approaches have been used to study brain and in general biological systems. In one of the approaches the brain-system is split into a large number of components, which are then studied in all their details. The problem of combining the data so accumulated in a working scheme able to account for the macroscopic observed functioning of the brain often is left unsolved since it is actually out of reach in this approach. Contradictory features often arise, indeed. For example, it is not clear how the high effectiveness and stability of some characterizing brain features may result from the random biomolecular activity of the brain component cells.Adilemma already pointed out by Lashley in neuroscience, and by Schrödinger in biology, but stillwaiting an answer. This first approach is the naturalistic approach. The other approach is the dynamical approach aiming to provide a comprehension of macroscopic features of the brain behavior on the basis of the data provided by the first approach. Both approaches appear thus to be necessary, although each one of them, separately considered, is not sufficient to account for the full understanding of brain functioning. A bridge between these approaches could be built following the strategy successfully used in the study of many-body condensed matter physics. In this direction moves the dissipative many-body model of brain, where the observed dynamic amplitude modulated (AM) assemblies of coherently oscillating neurons are described in the frame of the quantum field theory of spontaneously broken symmetry theories. Observations of scale free and critical phenomena in brain activity are also related to the coherent dynamics playing a crucial role in the dissipative model. A representation in terms of thermodynamic generalized Carnot-Rankine cycles is provided, which describes the process of formation of the coherent AM patterns as a transition from disordered, gas-like state of high entropy to liquid-like organized neuronal configurations of low entropy.