Topic: behavioral subspaces

Neural recordings are often high-dimensional while latent dynamics related to behavior are low-dimensional. Similarly, ethological behaviors are described by large numbers of dynamic features and it is important to know both the dimensionality and composition of the neurally relevant behavioral subspace. However, methods for data-driven identification of behavioral subspaces relevant for neural recordings is nascent. Here, we introduce external Dynamics Components Analysis (eDCA), a linear dimensionality reduction method that finds subspaces of past neural data that have maximal mutual informa