Marco Viola – författare

This volume brings together new papers advancing contemporary debates in foundational, conceptual, and methodological issues in cognitive neuroscience. The different perspectives presented in each chapter have previously been discussed between the authors, as the volume builds on the experience of Neural Mechanisms (NM) Online – webinar series on the philosophy of neuroscience organized by the editors of this volume. 

The contributed chapters pertain to five core areas in current philosophy of neuroscience. It surveys the novel forms of explanation (and prediction) developed in cognitive neuroscience, and looks at new concepts, methods and techniques used in the field. The book also highlights the metaphysical challenges raised by recent neuroscience and demonstrates the relation between neuroscience and mechanistic philosophy. Finally, the book dives into the issue of neural computations and representations. Assembling contributions from leading philosophers ofneuroscience, this work draws upon the expertise of both established scholars and promising early career researchers.

In recent years, non-linear optimization has had a crucial role in the development of modern techniques at the interface of machine learning and imaging. The present book is a collection of recent contributions in the field of optimization, either revisiting consolidated ideas to provide formal theoretical guarantees or providing comparative numerical studies for challenging inverse problems in imaging. The work of these papers originated in the INdAM Workshop “Advanced Techniques in Optimization for Machine learning and Imaging” held in Roma, Italy, on June 20-24, 2022.

The covered topics include non-smooth optimisation techniques for model-driven variational regularization, fixed-point continuation algorithms and their theoretical analysis for selection strategies of the regularization parameter for linear inverse problems in imaging, different perspectives on Support Vector Machines trained via Majorization-Minimization methods, generalization of Bayesian statistical frameworks to imaging problems, and creation of benchmark datasets for testing new methods and algorithms.