Understanding Neural Responses Through PCA

This chapter explores the application of principal component analysis (PCA) in analyzing neural responses in the visual cortex, emphasizing the importance of cross-validation in sorting genuine stimuli responses from background noise. The discussion includes advanced techniques like Fourier transforms, differentiable neural codes, and machine learning kernels to illustrate the complexities of population coding and dimensionality in neural data. Throughout, findings reveal surprising high-dimensional structures in neural responses, challenging previous assumptions and shedding light on the brain's encoding mechanisms.