From criticality to learning: a study of self-organization in recurrent neural networks

The brain is a large complex system which is remarkably good at maintaining stability under a wide range of input patterns and intensities. In addition, such a stable dynamical state is able to sustain essential functions, including the encoding of information about the external environment and storing memories. In order to succeed in these challenging tasks, neural circuits rely on a variety of plasticity mechanisms that act as self-organizational rules and regulate their dynamics. Based on toy models of self-organized criticality, this stable state has been proposed to be a phase transition point, poised between distinct types of unhealthy dynamics, in what has become known as the critical brain hypothesis. It is not yet known, however, if and how self-organization could drive biological neural networks towards a critical state while maintaining or improving their learning and memory functions. Here, we investigate the emergence of criticality signatures in the form of neuronal avalanches due to self-organizational plasticity rules in a recurrent neural network. We show that power-law distributions of events, widely observed in experiments, arise from a combination of biologically inspired synaptic and homeostatic plasticity but are highly dependent on the external drive. Additionally, we describe how learning abilities and fading memory emerge and are improved by the same self-organizational processes. We finally propose an application of these enhanced functions, focusing on sequence and simple language learning tasks. Taken together, our results suggest that the same self-organizational processes can be responsible for improving the brain’s spatio-temporal learning abilities and memory capacity while also giving rise to criticality signatures under particular input conditions, thus proposing a novel link between such abilities and neuronal avalanches. Although criticality was not verified, the detailed study of self-organization towards critical dynamics further elucidates its potential emergence and functions in the brain