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...
Non-Invasive Brain-Computer Interfaces (BCIs) are systems that facilitate direct communication between the brain and external devices without the need for surgical procedures. They primarily rely on techniques that measure brain activity externally, such as electroencephalography (EEG). Principles of Non-Invasive BCIs 1. Signal Acquisition : Non-invasive BCIs capture brain signals using external sensors placed on the scalp. The most common method employed is: Electroencephalography (EEG) : This method detects electrical activity produced by neuronal firing via electrodes attached to the scalp. 2. Signal Processing : Once the brain signals are acquired, they undergo signal processing, which includes filtering, amplification, and feature extraction. The aim is to enhance signal quality and isolate relevant neural signatures associated with specific thoughts or commands. 3. ...