Criticality in the brain

Abstract. Spontaneous brain activity has been recently characterized by avalanche dynamics with critical features for systems in vitro and in vivo. In this contribution we overview the experimental results and discuss a modelling approach implementing several physiological properties of living neurons which is able to reproduce experimental results for avalanche statistics. The temporal organization of avalanches can be characterized by the distribution of waiting times between successive avalanches. Experimental measurements exhibit a non-monotonic behaviour, not usually found in other natural processes. Numerical simulations provide evidence that this behaviour is a consequence of the alternation between states of high and low activity, leading to a balance between excitation and inhibition controlled by a single parameter. During these periods both the single neuron state and the network excitability level, keeping memory of past activity, are tuned by homeostatic mechanisms. Interestingly, the same homeostatic balance is detected for neuronal activity at the scale of the whole brain. We finally discuss the learning abilities of this neuronal network. Learning occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. The system is able to learn all the tested rules and the learning dynamics exhibits universal features as function of the strength of plastic adaptation. Any rule could be learned provided that the plastic adaptation is sufficiently slow