Graphing the Brain’s Dark Energy: Network Models and Neural Mechanisms
A lecture by Carl Craver, Professor of Philosophy and Philosophy-Neuroscience-Psychology at Washington University in St. Louis.
Abstract: In a recent paper on network analysis, Philipe Hunneman conjectures that topological explanations represent a style of explanation distinct from mechanistic explanation. I discuss these claims in the context of recent work using resting state functional correlations to infer cortical structure. Graph theory and network analysis are central research tools in the Human Connectome Project (Sporns 2011). I argue (in agreement with Hunneman) that that network models (often coupled with facts about localization) can be used to describe features of the organization of complex mechanisms that other representational systems are ill-equipped to describe. I catalogue some of the most promising uses of network theory in contemporary connectomics. I argue, however, that network theory can be used to construct accurate, complete, and well-verified mathematical descriptions of both brain activity and brain structure that explain nothing at all. The explanatory force of the model comes not from the fact that it is a network model but from the fact that network analysis reveals something useful about the organization of a mechanism. Network models that fail in this regard would not be explanatorily interesting. Philosophical emphasis on the explanatory value of network models distracts attention from more interesting questions raised by network theory concerning the organization of complex systems and the methods by which that organization might efficiently be discovered.