Fig. 1.

Latent feature spaces. (A) In the theoretical framework, the observed patterns of neural activity are explained by a set of latent features, each of which is linearly combined with an associated pattern component. The mapping between the experimental conditions (stimuli, movements, tasks, etc.) and the features (dashed line) can be non-linear. (B) Mathematically, each observed pattern (y_k,n) is a linear combination of different pattern components (u_d), each weighted by the corresponding dimension in the latent feature vector (f_k).