Frontiers in Systems Neuroscience, 8:112 (2014).
Behavioural studies have shown that human cognition is characterized by properties such as temporal scale invariance, heavy-tailed non-Gaussian distributions, and long-range correlations at long time scales, suggesting models of how (non observable) components of cognition interact. On the other hand, results from functional neuroimaging studies show that complex scaling and intermittency may be generic spatio-temporal properties of the brain at rest. Somehow surprisingly, though, hardly ever have the neural correlates of cognition been studied at time scales comparable to those at which cognition shows scaling properties. Here, we analyze the meanings of scaling properties and the significance of their task-related modulations for cognitive neuroscience. It is proposed that cognitive processes can be framed in terms of complex generic properties of brain activity at rest and, ultimately, of functional equations, limiting distributions, symmetries, and possibly universality classes characterizing them.
[Read more in Frontiers in Systems Neuroscience]
The effects of temperature on various aspects of neural activity from single cell to neural circuit level have long been known. However, how temperature affects the system-level of activity typical of experiments using non-invasive imaging techniques, such as magnetic brain imaging of electroencephalography, where neither its direct measurement nor its manipulation are possible, is essentially unknown. Starting from its basic physical definition, we discuss
possible ways in which temperature may be used both as a parameter controlling the evolution of other variables through which brain activity is observed, and as a collective variable describing brain activity. On the one hand, temperature represents a key control parameter of brain phase space navigation. On the other hand, temperature is a quantitative measure of the relationship between spontaneous and evoked brain activity, which can be used to describe how brain activity deviates from thermodynamic equilibrium. These two aspects are further illustrated in the case of learning-related brain activity, which is shown to be reducible to a purely thermally guided phenomenon. The phenomenological similarity between brain activity and amorphous materials suggests a characterization of plasticity of the former in terms of the well-studied temperature and thermal history dependence of the latter, and of individual differences in learning capabilities as material-specific properties. Finally, methods to extract a temperature from experimental data are reviewed, from which the whole brain’s thermodynamics can then be reconstructed.
[Read more in ArXiv]