Embedding dimension of neural manifolds and the structure of mixed selectivity
Christopher Langdon, Tatiana Engel · COSYNE 2025
Single neurons show complex responses during cognitive tasks and dimensionality reduction has been a successful approach for finding structure in heterogeneous neural activity. When applied to neural response patterns in a population state space, where each axis represents activity of one neuron, dimensionality reduction methods often reveal low-dimensional manifolds encoding behaviorally relevant variables. Alternatively, when the same responses are viewed in a selectivity space, where each axis represents a response feature (e.g., task condition or time), dimensionality reduction methods can
ePoster · Neuroscience