4. Selforganization in the Brain

4.1 Introduction

Can the brain be considered as a dynamic system?

The requirements mentioned in part 2 are met: the brain does consist of a large number of more or less similar units, the neurons, interacting with each other. Neurons interact by 'exciting' and 'inhibiting' one another, thereby changing each other’s functional state, as reflected for instance by the membrane potential or the mean firing rate. Neurons therefore exchange 'levels of excitation'. Unlike energy and momentum, this property is not strictly conserved.  But the maintenance of the excited states and changes therein caused by synaptic interaction lean heavily on metabolic energy. This strong coupling of function and energy consumption is clearly demonstrated by the experiments with 14C-deoxyglucose uptake (Sokoloff, 1977). On a larger timescale, neurons are capable of changing each other’s biochemistry by the process of 'long-term potentiation' (Bliss and Lomo, 1973). This interaction between units gives rise to the emergence of macroscopic properties which do not depend on the state of individual neurons, but only on the average state of a large set of neurons. One such a macroscopic parameter could be the 'neuronal activity' defined as: the number of neurons firing per unit time in a small volume. The rates of change in these macroscopic parameters are equivalent to the 'flows' mentioned in Part 2. A constant supply of energy (glucose) keeps the system in a dynamic state, sufficiently far from thermodynamic equilibrium.

Are processes of selforganization to be expected in the brain?

Positive answers to this question were given by Katchalski et al. (1974) and Freeman (1975). According to Katchalski, dynamic patterns can be expected in free energy dissipating systems containing a large number of nonlinear interacting elements.  The dissipation (= conversion to heat) of free energy in the brain is high and special mechanisms exist to guarantee a sufficient supply of oxygen and glucose, in order to maintain the high level of dissipation. The interaction between the neurons is strongly nonlinear, due to threshold and saturation effects. Furthermore, the different inputs to a neuron are not simply summated or integrated: coincident inputs are much more effective than asynchronous inputs (Abeles, 1982), an effect which adds to the non-linearity. Moreover, the neurons are diffusely coupled, as a consequence of the large degree of divergence and convergence, and therefore interactive in a continuum. According to Freeman (1975), macroscopic entities can be expected when a large number of highly nonlinear elements such as neurons mutually influence each other diffusely over an extended range in time and space. The macroscopic form of neuronal activity is reflected by the electroencephalogram (EEG) and Freeman stresses the importance of EEG measurements for the understanding of higher brain functions. Experimental evidence for the existence of macroscopic forms of neural activity is presented (Freeman and Skarda, 1985; Freeman, 1986).