Topological insights into the neural basis of flexible behavior

Abstract

It is widely accepted that there is an inextricable link between neural computations, biological mechanisms, and behavior, but it is challenging to simultaneously relate all three. Here, we show that topological data analysis (TDA) provides an important bridge between these approaches to studying how brains mediate behavior. We demonstrate that cognitive processes change the topological description of the shared activity of populations of visual neurons. These topological changes constrain and distinguish between competing mechanistic models, are connected to subjects performance on a visual change detection task, and, via a link with network control theory, reveal a tradeoff between improving sensitivity to subtle visual stimulus changes and increasing the chance that the subject will stray off task. These connections provide a blueprint for using TDA to uncover the biological and computational mechanisms by which cognition affects behavior in health and disease. Significance StatementAs the fields of systems, computational, and cognitive neuroscience strive to establish links between computations, biology, and behavior, there is an increasing need for an analysis framework to bridge levels of analysis. We demonstrate that topological data analysis (TDA) of the shared activity of populations of neurons provides that link. TDA allows us to distinguish between competing mechanistic models and to answer longstanding questions in cognitive neuroscience, such as why there is a tradeoff between visual sensitivity and staying on task. These results and analysis framework have applications to many systems within neuroscience and beyond.