Spontaneous synchronization in recurrent neural networks: From mathematical analysis to flexible information processing in spiking networks

The brain is a highly complex distributed system that not only continuously performs demanding computational tasks but also flexibly adapts its processing to changing requirements on short time scales. A prime example of this flexibility is provided by selective visual attention. Neurons in the visual cortex have been shown to respond to simultaneous presentation of multiple stimuli in their receptive field as if only the stimulus receiving attention was present. This constitutes an attention mediated change of the functional configuration of information processing resulting in selective routing of the behaviorally relevant signals. It is not fully understood which neural mechanisms enable flexible information processing in the brain. This thesis uses theoretical analysis, model building and experimental investigations to generate novel insights into the key aspects coordination, configuration and control of flexible information processing.
On a fundamental level, neural activity consists of spatio-temporal spike patterns which can contain transient bursts of synchronous activity of an assembly of neurons in form of spike avalanches. Theoretical studies suggest that a critical dynamics exhibiting power law distributed avalanche sizes maximizes computational capabilities, but usually assume homogeneously coupled populations in the large network size limit. Neural populations in the brain are instead finite, highly structured and strongly driven. To help bridge this gap, we develop a theory of spike pattern formation for finite and arbitrarily coupled networks of spiking neurons resulting in closed form avalanche assembly distributions. This closed form contains the networks graph Laplacian and thus provides explicit insights into how the network structure coordinates spontaneously synchronized activity.