The Next Phase for Tracking and Predicting the Navigational Behavior Using Machine Learning

摘要

Animals navigate through their ways in various scales ranging from centimeters to thousands of kilometers. How does the brain realize such spatial navigation? Several lines of evidence have suggested that the hippocampal place cell activity in the brain has a potential to answer the question. Although statistical machine learning plays critical roles on precisely deciphering the nature of the place cell activity, some technical issues remain unsolved since the ground truth is missing. By virtue of progressive efforts together with the advance on machine learning, methodologies for tracking and predicting navigational behaviors have been improved. Here we review the popular methodologies based on statistical machine learning that track the animal behavior from images and predict it from place cell activity, and discuss about what the next phase of the analysis tools is to deeply understand the neuronal underpinning of spatial navigation.