Graph Alignment Exploiting the Spatial Organisation Improves the Similarity of Brain Networks

International audienceEvery brain is unique, having its structural and functional organisation shaped by both genetic and environmental factors over the course of its development. Brain image studies tend to produce results by averaging across a group of subjects, under a common assumption that it is possible to subdivide the cortex into homogeneous areas while maintaining a correspondence across subjects. This paper questions such assumption: can the structural and functional properties of a specific region of an atlas be assumed to be the same across subjects? This question is addressed by looking at the network representation of the brain, with nodes corresponding to brain regions and edges to their structural relationships. We perform graph matching on a set of control patients and on parcellations of different granularity to understand which is the connectivity misalignment between regions. The graph matching is unsupervised andreveals interesting insight on local misalignment of brain regions across subjects

Machine Learning As Tool And Theory For Computational Neuroscience

Computational neuroscience is in the midst of constructing a new framework for understanding the brain based on the ideas and methods of machine learning. This is effort has been encouraged, in part, by recent advances in neural network models. It is also driven by a recognition of the complexity of neural computation and the challenges that this poses for neuroscience’s methods. In this dissertation, I first work to describe these problems of complexity that have prompted a shift in focus. In particular, I develop machine learning tools for neurophysiology that help test whether tuning curves and other statistical models in fact capture the meaning of neural activity. Then, taking up a machine learning framework for understanding, I consider theories about how neural computation emerges from experience. Specifically, I develop hypotheses about the potential learning objectives of sensory plasticity, the potential learning algorithms in the brain, and finally the consequences for sensory representations of learning with such algorithms. These hypotheses pull from advances in several areas of machine learning, including optimization, representation learning, and deep learning theory. Each of these subfields has insights for neuroscience, offering up links for a chain of knowledge about how we learn and think. Together, this dissertation helps to further an understanding of the brain in the lens of machine learning