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 orientation to an EEG record involves understanding the key components and information present in an EEG recording. Here are some important aspects of orienting to an EEG record: 1. Electrode Placement : EEG recordings are obtained by placing electrodes on specific locations on the scalp according to standardized systems such as the "10-20" electrode placement system. Understanding the electrode locations and their corresponding brain regions is essential for interpreting the EEG data accurately. 2. Montage Selection : EEG recordings can be displayed in different montages, such as bipolar and referential montages. Each montage provides a different perspective on the brain activity, and selecting the appropriate montage is crucial for analyzing specific aspects of the EEG data. 3. Interpretation of Waveforms : EEG recordings display electrical waveforms that represent the brain's electrical activity. Understanding the characteristics of different waveforms, such as fre...