Theoretical Brain Studies Highlighted in Special Issue of Nature Neuroscience
On February 23, Nature Neuroscience published a special issue focusing on neural computation and theory and highlighting recent advances. The issue consists of a collection of reviews and perspectives written primarily by investigators from the Simons Collaboration on the Global Brain (SCGB). In their commentary, SCGB investigators Anne Churchland of Cold Spring Harbor Laboratory and Larry Abbott of Columbia University argue that the need for theoretical approaches in neuroscience research has never been greater. They note that this need has been driven by the “explosion of technologies” used for measuring and manipulating neurons and by developments within theoretical neuroscience itself. They review some of the challenges of data analysis and highlight the diversity of approaches to modeling brain function. Global understanding of the brain, they write, will be based on loosely stitching together highly diverse approaches.
In the related reviews and perspectives, several SCGB investigators provide more details about the advances and challenges of computation- and theory-driven approaches to modeling brain function. Abbott and his co-authors review methods for developing more realistic spiking network models. James DiCarlo of the Massachusetts Institute of Technology and his co-author describe advances in using hierarchical convolutional neural networks and outline how these networks can provide more insight into sensory cortical processing. Alexandre Pouget of the University of Geneva and his co-authors discuss the probabilistic computations of uncertainty and strategies for studying the neural codes underlying confidence and certainty, explaining how these investigations are essential to understanding the roles of cortical areas in decision-making. Brent Doiron of the University of Pittsburgh and his co-authors examine recent theoretical results related to the analysis of simultaneous recordings from large neural populations, findings that they say are important for statistical analyses of high-dimensional neural data. Ila Fiete of the University of Texas at Austin and her co-author review the computational principles of memory. Two other papers, written by investigators outside of SCGB, round out the issue by describing experimental and theoretical studies of cortical inhibition and computational psychiatry.
The special issue is available here.
