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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.
Criticality meets learning : criticality signatures in a self-organizing recurrent neural network
(2017)
Many experiments have suggested that the brain operates close to a critical state, based on signatures of criticality such as power-law distributed neuronal avalanches. In neural network models, criticality is a dynamical state that maximizes information processing capacities, e.g. sensitivity to input, dynamical range and storage capacity, which makes it a favorable candidate state for brain function. Although models that self-organize towards a critical state have been proposed, the relation between criticality signatures and learning is still unclear. Here, we investigate signatures of criticality in a self-organizing recurrent neural network (SORN). Investigating criticality in the SORN is of particular interest because it has not been developed to show criticality. Instead, the SORN has been shown to exhibit spatio-temporal pattern learning through a combination of neural plasticity mechanisms and it reproduces a number of biological findings on neural variability and the statistics and fluctuations of synaptic efficacies. We show that, after a transient, the SORN spontaneously self-organizes into a dynamical state that shows criticality signatures comparable to those found in experiments. The plasticity mechanisms are necessary to attain that dynamical state, but not to maintain it. Furthermore, onset of external input transiently changes the slope of the avalanche distributions – matching recent experimental findings. Interestingly, the membrane noise level necessary for the occurrence of the criticality signatures reduces the model’s performance in simple learning tasks. Overall, our work shows that the biologically inspired plasticity and homeostasis mechanisms responsible for the SORN’s spatio-temporal learning abilities can give rise to criticality signatures in its activity when driven by random input, but these break down under the structured input of short repeating sequences.