Neural architectures and their connectivity

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One focus is on effective connectivity algorithms and how they can be applied to understanding human brain function. An aim is to measure how effective connectivity changes with different task demands to study information flow in the brain. To investigate neural architectures, one route will utilise genetic algorithms to create biologically plausible architectures for defined computational tasks. A related route will investigate how the network design relates to the invariances in the input, using for example memory trace learning rules or geometric approaches to deep learning. Understanding the relationship between environmental statistics and neural network design may lead real world applications in the fields of unsupervised learning and neural architecture search. Finally, knowledge of both the connectivity and architecture of the brain is not only important for computational neuroscience, but may lead to applications in medical science.