Explanatory models in neuroscience: Part 1 -- taking mechanistic abstraction seriously

Understand

Despite the recent success of neural network models in mimicking animal performance on visual perceptual tasks, critics worry that these models fail to illuminate brain function.

• We take it that a central approach to explanation in systems neuroscience is that of mechanistic modeling, where understanding the system is taken to require fleshing out the parts, organization, and activities of a system, and how those give rise to behaviors of interest.
• However, it remains somewhat controversial what it means for a model to describe a mechanism, and whether neural network models qualify as explanatory.
• We argue that certain kinds of neural network models are actually good examples of mechanistic models, when the right notion of mechanistic mapping is deployed.