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...
Hypersynchronous slowing in EEG recordings can be observed in various neurological conditions, indicating altered brain function and underlying pathologies. Some examples of neurological conditions where hypersynchronous slowing may be present: 1. Encephalopathy : o Hypersynchronous slowing is commonly seen in encephalopathy, a condition characterized by diffuse brain dysfunction. o In encephalopathy, generalized hypersynchronous slowing may reflect the nonspecific state of cerebral dysfunction associated with metabolic disturbances, toxic exposures, or systemic illnesses. 2. Seizure Disorders : o Hypersynchronous slowing can be associated with seizure disorders, including epilepsy. o In patients with epilepsy, hypersynchronous slowing may indicate abnormal neuronal excitability and predisposition to seizures. 3. Brain Injury : o Following traumatic brain injury or stroke, hypersy...