AUTHOR=Xu Zishen , Wu Wei , Winter Shawn S. , Mehlman Max L. , Butler William N. , Simmons Christine M. , Harvey Ryan E. , Berkowitz Laura E. , Chen Yang , Taube Jeffrey S. , Wilber Aaron A. , Clark Benjamin J. 

TITLE=A Comparison of Neural Decoding Methods and Population Coding Across Thalamo-Cortical Head Direction Cells

JOURNAL=Frontiers in Neural Circuits

VOLUME=Volume 13 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/neural-circuits/articles/10.3389/fncir.2019.00075

DOI=10.3389/fncir.2019.00075

ISSN=1662-5110

ABSTRACT=Animals can navigate by monitoring an online record of their spatial orientation in an environment and using this information to produce direct trajectories to hidden goals. Head direction (HD) cells, which fire action potentials whenever an animal points its head in a particular direction, are thought to subserve the animal’s sense of spatial orientation. HD cells are found prominently in several thalamo-cortical regions including anterior thalamic nuclei (ATN), postsubiculum (PoS), medial entorhinal cortex (MEC), parasubiculum (PaS), and the parietal cortex (PC). While a number of methods in neural decoding have been developed to assess the dynamics of spatial signals within thalamo-cortical regions, studies conducting a quantitative comparison of machine learning and statistical model-based decoding methods on HD cell activity are currently lacking. Here, we compare statistical model-based and machine learning approaches by assessing decoding accuracy and evaluate variables that contribute to population coding across thalamo-cortical HD cells.