  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 machine learning.
  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.
