Self-Organized Criticality in Developing Neuronal Networks

Abstract

Recently evidence has accumulated that many neural networks exhibit self-organized criticality. In this state, activity is similar across temporal scales and this is beneficial with respect to information flow. If subcritical, activity can die out, if supercritical epileptiform patterns may occur. Little is known about how developing networks will reach and stabilize criticality. Here we monitor the development between 13 and 95 days in vitro (DIV) of cortical cell cultures (n = 20) and find four different phases, related to their morphological maturation: An initial low-activity state (≈19 DIV) is followed by a supercritical (≈20 DIV) and then a subcritical one (≈36 DIV) until the network finally reaches stable criticality (≈58 DIV). Using network modeling and mathematical analysis we describe the dynamics of the emergent connectivity in such developing systems. Based on physiological observations, the synaptic development in the model is determined by the drive of the neurons to adjust their connectivity for reaching on average firing rate homeostasis. We predict a specific time course for the maturation of inhibition, with strong onset and delayed pruning, and that total synaptic connectivity should be strongly linked to the relative levels of excitation and inhibition. These results demonstrate that the interplay between activity and connectivity guides developing networks into criticality suggesting that this may be a generic and stable state of many networks in vivo and in vitro.

Time scales of memory, learning, and plasticity

“…As a consequence, only computational models exist [cf. Table 1 -8 and (Van Ooyen 1994;Poirazi and Mel 2001;Butz et al 2009;Tetzlaff et al 2010;Knoblauch et al 2010)] and many of them originated from developmental neuroscience (van Ooyen 2011).…”

Critical dynamics on a large human Open Connectome network

“…Theoretical and experimental research provides many signals for the brain to operate in a critical state between sustained activity and an inactive phase [1][2][3][4][5]. Critical systems exhibit optimal computational properties, suggesting why the nervous system would benefit from such mode [6].…”

Magnetoencephalography in the study of brain dynamics

“…the number of avalanches observed in the data scales inversely with the size of the avalanche (Beggs and Plenz, 2003). These dynamics characterize spontaneous neuronal activity in organotypic cultures, brain slices in vitro, and monkey and human cortex in vivo (Petermann et al, 2009;Tetzlaff et al, 2010). Additionally, the brain's dynamic properties proved to be behaviorally relevant.…”