sliceTCA

…In addition, I will present sliceTCA, a new t

…In addition, I will present sliceTCA, a new t

Learning induces changes in neural activity over slow timescales. These changes can be summarized by restructuring neural population data into a three-dimensional array or tensor, of size neurons by time points by trials. Classic dimensionality reduction methods often assume that neural representations are constrained to a fixed low-dimensional latent subspace. Consequently, this view does not capture how the latent subspace could evolve over learning, nor how high-dimensional neural activity could emerge over learning. Furthermore, the link between these empirically-observed changes in neural

A fundamental question in systems neuroscience is how populations of neurons represent sensory, motor, and cognitive variables. Yet how these neural representations evolve over slow timescales is not well understood. Recent work has proposed using tensor decomposition methods to identify low-dimensional latent dynamics without requiring trial-averaging (tensor component analysis; TCA). This approach assumes that the data tensor can be described as a sum of components with fixed neural weights and temporal dynamics that vary over trials only in amplitude. However, recent evidence suggests that