Abstract: Example embodiments for efficient neuromorphic population coding are described. In one case, individual instances of input stimuli are evaluated using a set of feature encoding units to generate a population of encoded feature values. The population of encoded values for each of the individual input stimuli are arranged into a population code matrix. The population code matrix is factorized into a basis element matrix and a contribution coefficient matrix based on a number of basis vectors, where the number of basis vectors is selected to balance sparseness in the basis element matrix and reconstruction error of the population code matrix from the basis element matrix and the contribution coefficient matrix. The embodiments are compatible with neuromorphic hardware and can achieve compact representation of high-dimensional data, infer latent variables in the data, and defer processing to an off-line training phase to save time during real-time data capture and evaluation.