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...
Neural patterning in the embryonic period is a complex process that involves the establishment of regional identities and the differentiation of neural progenitor cells into specific cell types. Here are key points regarding neural patterning in the embryonic period: 1. Regional Specification : o During the embryonic period, regional specification of the neural tube occurs, leading to the formation of distinct brain regions with unique identities. o The neural tube gives rise to the forebrain (prosencephalon), midbrain (mesencephalon), and hindbrain (rhombencephalon), which further differentiate into specific structures within each region. o Graded patterns of molecular signaling in the neocortical proliferative zone contribute to the regional elaboration of the brain, establishing primitive patterning of sensorimotor regions by the end of the embryonic period. 2. Genetic...