Serena Di Santo — Università di Parma, Universidad de Granada #
Scale-free dynamics of brain networks: from self-organization to oscillations and neutral theory #
The brain of mammalians, including humans, have the remarkable property of being endogenously active, even in the absence of any task or stimuli.  In vivo and in vitro experiments at different resolution scales and employing diverse experimental techniques, consistently reveal the existence of intermittent outbursts of electro-chemical activity that propagate through neural networks in the form of "avalanches". The sizes and durations of neural avalanches are distributed as power laws, obey scaling, exhibit long-range correlations, and other characteristic features of critical points. These observations have elicited enthusiasm and attracted much interest among theoretitians, who took them as possible empirical evidence that some aspects of living systems (or parts or groups of them) could extract important functional benefits from operating at the edge of a continuous phase transition between two radically different phases, order and disorder. Criticality has been claimed to provide such systems with large susceptibility, huge dynamics ranges, large information processing and storing power, optimal computational capabilities, etc.

In this talk I will discuss possible alternatives and/or complementary explanations to criticality for the empirically observed scale-free avalanches of neuronal activity. In particular, I will introduce two novel concepts in the field: self-organized bistability (SOB) which naturally extends the idea of self-organized criticality (SOC) to discontinuous phase transitions, and the neutral theory of neural dynamics, that borrows from important development in molecular and population genetics.  Both of these concepts have shed light on the nature of neural dynamics and have opened new perspectives and novel research lines in this fascinating field, whose ultimate goal is to undertand how the amazingly complex behavior of the brain can possibly emerge from its underlying network of neurons and plastic synapses.