Integrating Levels of Analysis in Systems and Cognitive Neurosciences

Abstract

Neuroscience is inherently interdisciplinary, rapidly expanding beyond its roots in biological sciences to many areas of the social and physical sciences. This expansion has led to more sophisticated ways of thinking about the links between brains and behavior and has inspired the development of increasingly advanced tools to characterize the activity of large populations of neurons. However, along with these advances comes a heightened risk of fostering confusion unless efforts are made to better integrate findings across different model systems and to develop a better understanding about how different measurement techniques provide mutually constraining information. Here we use selective visuospatial attention as a case study to highlight the importance of these issues, and we suggest that exploiting multiple measures can better constrain models that relate neural activity to animal behavior.