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...
The development of the human brain is supported by various lines of evidence, including neuroimaging studies, histological analyses, genetic research, and clinical observations. These different approaches provide valuable insights into the structural, functional, and molecular changes that occur during brain development. Here are some key pieces of evidence supporting the development of the human brain: 1. Neuroimaging Studies: Techniques such as magnetic resonance imaging (MRI) and functional MRI (fMRI) allow researchers to visualize the structural and functional changes in the human brain across different developmental stages. These studies provide detailed information about brain maturation, connectivity patterns, and regional changes over time. 2. Histological Analyses: Histological studies involve examining brain tissue samples under a microscope to observe cellular structures, neuronal connections, and developmental changes. These analyses help...