ePoster · Computational Neuroscience
Assessing Neural Manifold Properties With Adapted Normalizing Flows
Peter Bouss, Sandra Nestler, Kirsten Fischer, Claudia Merger, Alexandre René, Moritz Helias · Bernstein Conference 2024
Despite the large number of active neurons in the cortex, the activity of neuronal populations is expected to lie on a low-dimensional manifold for different brain regions [1]. Variants of principal component analysis (PCA) are commonly used to assess this manifold. However, these methods are limited by the assumption that the data follows a Gaussian distribution and neglect additional features such as the curvature of the manifold. Hence, their performance as generative models tends to be subpar. To construct a generative model that entirely learns the statistics of neural activity with no a