Representation Learning in Artificial and Biological Neural Networks

This workshop explores the interface between cognitive neuroscience and recent advances in AI fields that aim to reproduce human performance such as natural language processing and computer vision, and specifically deep learning approaches to such problems.



When studying the cognitive capabilities of the brain, scientists follow a system identification approach in which they present different stimuli to the subjects and try to model the response that different brain areas have of that stimulus. The goal is to understand the brain by trying to find the function that expresses the activity of brain areas in terms of different properties of the stimulus. Experimental stimuli are becoming increasingly complex with more and more people being interested in studying real life phenomena such as the perception of natural images or natural sentences. There is therefore a need for a rich and adequate vector representation of the properties of the stimulus, that we can obtain using advances in NLP, computer vision or other relevant ML disciplines.



In parallel, new ML approaches, many of which in deep learning, are inspired to a certain extent by human behavior or biological principles. Neural networks for example were originally inspired by biological neurons. More recently, processes such as attention are being used which have are inspired by human behavior. However, the large bulk of these methods are independent of findings about brain function, and it is unclear whether it is at all beneficial for machine learning to try to emulate brain function in order to achieve the same tasks that the brain achieves.



In order to shed some light on this difficult but exciting question, we bring together many experts from these converging fields to discuss these questions, in a new highly interactive format focused on short lectures from experts in both fields, followed by a guided discussion. 



This workshop is a continuation of a successful workshop series: Machine Learning and Interpretation in Neuroimaging (MLINI). MLINI has already had 5 iterations in which methods for analyzing and interpreting neuroimaging data were discussed in depth. In keeping with previous tradition in the workshop, we also visit the blossoming field of machine learning applied to neuroimaging data, and specifically the recent trend of utilizing neural network models to analyze brain data, which is evolving on a seemingly orthogonal plane to the use of these algorithms to represent the information content in the brain. This way we will complete the loop of studying the advances of neural networks in neuroscience both as a source of models for brain representations, and as a tool for brain image analysis.