This research paper presents SIMPL (Scalable Iterative Maximization of Population-coded Latents), a novel, computationally efficient algorithm designed to refine the estimation of latent variables and tuning curves from neural population activity. Latent variables in neural data represent essential low-dimensional quantities encoding behavioral or cognitive states, which neuroscientists seek to identify to understand brain computations better. Background and Motivation Traditional approaches commonly assume the observed behavioral variable as the latent neural code. However, this assumption can lead to inaccuracies because neural activity sometimes encodes internal cognitive states differing subtly from observable behavior (e.g., anticipation, mental simulation). Existing latent variable models face challenges such as high computational cost, poor scalability to large datasets, limited expressiveness of tuning models, or difficulties interpreting complex neural network-based functio...
Lineage analysis of glial cells in the intact and injured adult mouse central nervous system (CNS) involves tracking the origin, differentiation, and fate of glial cell populations under normal conditions and in response to neural injury. Here are some key points related to lineage analysis of glial cells in the intact and injured adult mouse CNS: 1. Heterogeneity of Glial Cell Populations : o Astrocytes and Oligodendrocytes : The CNS contains diverse populations of glial cells, including astrocytes and oligodendrocytes, which play crucial roles in maintaining homeostasis, supporting neuronal function, and responding to injury or disease . o Progenitor Cells : Glial progenitor cells, such as NG2 glia, represent a dynamic cell population with the capacity to differentiate into mature glial subtypes and contribute to tissue repair and regeneration in the adult CNS . 2. Lineage Tracing Techniques : o Genetic Tools : L...