Keynote Presentation: Towards Multi-system Neural Network Models of Brain Functions with Live Q&A

Artificial neural networks can be useful for studying brain functions. In cognitive neuroscience, recurrent neural networks are often used to model cognitive functions. I will first offer my opinion on what is missing in the classical use of recurrent neural networks. Then I will discuss several lines of ongoing efforts in our group to move beyond the classical recurrent neural networks by studying multi-system neural networks (the talk will focus on two-system networks). These are networks that combine modules for several neural systems, such as vision, audition, prefrontal, hippocampal systems. I will showcase how multi-system networks can potentially be constrained by experimental data in fundamental ways and at scale. I will demonstrate how we can gain new neuroscientific insights by studying these multi-system models.

Learning Objectives:

1. Discuss the challenge of classical recurrent neural network approach to cognitive neuroscience.

2. Discuss multi-system neural network models and their applications to neuroscience.

3. Discuss how multi-system models can shed light on fundamental questions such as the nature of working memory limit.