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...
Hypnopompic, hypnagogic, and hedonic hypersynchrony are normal pediatric phenomena that are typically not associated with specific neurological conditions. However, in certain cases, these patterns may be observed in individuals with neurological disorders or conditions. Here is a brief overview of how these hypersynchronous patterns may manifest in different neurological contexts: 1. Epilepsy : o While hypnopompic, hypnagogic, and hedonic hypersynchrony are considered normal phenomena, they may resemble certain epileptiform discharges seen in epilepsy. o In individuals with epilepsy, distinguishing between normal hypersynchrony and epileptiform activity is crucial for accurate diagnosis and treatment. 2. Developmental Disorders : o Children with developmental disorders may exhibit atypical EEG patterns, including variations in hypersynchrony. o The presence of hypnopompic, hypnagogic, or hedonic hypersynchrony in in...