Abstract
In this talk, I will provide a brief introduction to deep neural networks (DNN) and discuss their usefulness with respect to modeling and understanding visual processing in the brain. To assess the potential benefits of DNN models, it is important to step back and consider generally the purpose of computational modeling and how computational models and experimental data should be integrated. Is the only goal to match experimental data? Or should we derive understanding from computational models? What kinds of information can be derived from a computational model that cannot be derived through simpler analyses? Given that DNN models can be quite complex, it is also important to consider how to interpret these models. Is it possible to identify the key feature of a DNN model that is responsible for a specific experimental effect? Is it useful to perform in silico experiments with a DNN model? Should we should strive to perform meta-modeling, that is, developing a (simple) model of a (complex DNN) model in order to help understand the latter? I will discuss these and related issues in the context of DNN models and compare DNN modeling to an alternative modeling approach that I have pursued in past research.
Meeting abstract presented at VSS 2016