Disentangling signal and noise in neural responses through generative modeling.

ABSTRACT: Measurements of neural responses to identically repeated experimental events often exhibit large amounts of variability. This noise is distinct from signal, operationally defined as the average expected response across repeated trials for each given event. Accurately distinguishing signal from noise is important, as each is a target that is worthy of study (many believe noise reflects important aspects of brain function) and it is important not to confuse one for the other. Here, we describe a principled modeling approach in which response measurements are explicitly modeled as the sum of samples from multivariate signal and noise distributions. In our proposed method-termed Generative Modeling of Signal and Noise (GSN)-the signal distribution is estimated by subtracting the estimated nois

SUBMITTER: Kay K 

PROVIDER: S-EPMC12289057 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature