Explanatory models in neuroscience: Part 2 -- constraint-based intelligibility
This addresses the philosophical and practical issue of model intelligibility in neuroscience, offering a framework to bridge computational models and biological understanding, though it appears incremental by combining existing explanatory modes.
The paper tackles the problem of how computational models explain brain function by proposing that intelligibility arises from understanding dependencies between behavior and causal factors, particularly top-down constraints from ethological goals and evolutionary pressures. It argues that neural network models, when optimized for ecologically-relevant goals, constrain mechanisms similarly to the brain, thus illuminating brain function.
Computational modeling plays an increasingly important role in neuroscience, highlighting the philosophical question of how computational models explain. In the context of neural network models for neuroscience, concerns have been raised about model intelligibility, and how they relate (if at all) to what is found in the brain. We claim that what makes a system intelligible is an understanding of the dependencies between its behavior and the factors that are causally responsible for that behavior. In biological systems, many of these dependencies are naturally "top-down": ethological imperatives interact with evolutionary and developmental constraints under natural selection. We describe how the optimization techniques used to construct NN models capture some key aspects of these dependencies, and thus help explain why brain systems are as they are -- because when a challenging ecologically-relevant goal is shared by a NN and the brain, it places tight constraints on the possible mechanisms exhibited in both kinds of systems. By combining two familiar modes of explanation -- one based on bottom-up mechanism (whose relation to neural network models we address in a companion paper) and the other on top-down constraints, these models illuminate brain function.