Poisson Noise

Neuronal cultures and human stem-cell-derived organoids are fundamental building blocks of neuroscientific research and future personalized medicine. However, in-vitro networks show considerably more synchronous bursting activity than networks in-vivo [1]. One leading theory proposes that under a lack of external input, maladapted network’s plasticity leads to a pathological collective activity pattern: long periods of quiescence are interspersed with bursts of strong, synchronous activity [2, 3]. In theoretical simulations, weak external input can reduce this bursting behavior. However, expe

To address the question of how to compare DNN model activations to brain data, we investigate what transforms best describe similarity in neural activity in the same brain area between conspecifics. We expect neural responses to be functionally highly similar within a species (since we expect findings to generalize across animals). What kind of transform will make such similarity most evident? That is, under what kind of transform are conspecifics’ neural responses highly similar to each other? Researchers often default to linear regression as a reasonable transform class for measuring neural

Low dimensional neural manifolds provide a compact characterization of population activity and shared co-variability across brain areas. Nonetheless technical limitations restrict the applicability of these tools to experimental designs without explicit trial structure, and thus cannot be used to understand neural responses during naturalistic behavior. Here we propose a novel probabilistic framework that allows for interpretable partitioning of population variability within and across areas in the context of naturalistic behavior. Our approach for task aligned manifold estimation (TAME-GP) ex