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 Jupyter Notebook is an open-source, interactive web application that allows you to create and share documents containing live code, equations, visualizations, and narrative text. It is widely used in data science, machine learning, and scientific computing because it supports the entire workflow of data exploration, analysis, and communication. Key Features and Benefits: 1. Interactive Code Execution You can write and execute code in a cell-by-cell manner. This enables you to run small blocks of code incrementally, see their output immediately, and modify them as needed. This is especially useful in data analysis and machine learning where iterative exploration is common. 2. Supports Multiple Languages Although most commonly used with Python, Jupyter Notebook supports over 40 programming languages including R, Julia, and Scala. For the context of machine learning with Python, the Python kernel is predominantly us...