“…Nor do more complicated GLM-based paradigms scale in a tractable manner to capture high-dimensional structures such as the inter-neuronal relationships described by population-level models, or nonlinear and dynamic representation of stimulus space described by the modulatory or time-varying models. Although augmenting GLMs with regularization or dimensionality reduction procedures, such as sparse or low-dimensional regression models, have shown to be promising for tackling the dimensionality problem ( Gerwinn et al, 2010 ; Sheikhattar et al, 2016 ; Aoi and Pillow, 2018 ; Zoltowski and Pillow, 2018 ; Niknam et al, 2019 ; Semedo et al, 2019 ), identifying behaviorally relevant dimensions ( Akbarian et al, 2021 ; Sani et al, 2021 ; Valente et al, 2021 ; Weng et al, 2023 ) which can directly link representation to readout is another important direction for current and future research.…”