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...
Selecting a random sample is a crucial aspect of research methodology to ensure the representativeness and generalizability of study findings. Here are some common methods and considerations for selecting a random sample: 1. Simple Random Sampling : o In simple random sampling, each element in the population has an equal chance of being selected for the sample. o One method is to assign a unique identifier (e.g., numbers) to each element in the population and then use a random number generator to select sample units. o Another approach is to use random sampling techniques such as lottery methods or random number tables to choose sample units. 2. Systematic Sampling : o In systematic sampling, researchers select every nth element from a list of the population after randomly determining a starting point. o This method is efficient and easy to implement, espe...