Patterned photostimulation optogenetics enables unprecedented precision in perturbing neural circuits, yet often yields weak or counterintuitive effects due to the complexity of neural code. I will present modeling approaches that leverage low-dimensional structure in neural population activity to design effective and informative stimulation patterns. First, I will show that recurrent neural networks (RNNs) trained to replicate neural recordings are not sufficiently constrained by low-dimensional task-driven activity, undermining their ability to reliably predict perturbation effects. Instead, low-dimensional neural response data uniquely constrain a latent low-rank connectivity in RNNs, enabling accurate prediction of perturbation effects within the task-relevant subspace. Second, I will show how we combined optogenetic activation of glomerular patterns in the mouse olfactory bulb with low-dimensional models of glomerular odor responses to relate glomerular activity to olfactory percepts. We discovered that the olfactory code is low-dimensional and inherently structured for efficient odor representations, enabling the design of novel activation patterns with desired perceptual properties.