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...
Polymer nanoparticles have shown great potential in biological sensing and brain tumor therapy due to their unique properties and versatility. Here are some key points regarding the use of polymer nanoparticles in these applications: 1. Biological Sensing : o Polymer nanoparticles can be engineered to serve as sensitive and selective probes for biological sensing applications. o Functionalization of polymer nanoparticles with specific ligands, antibodies, or aptamers enables targeted detection of biomarkers, pathogens, or specific molecules in biological samples. o The controlled release of signaling molecules or dyes from polymer nanoparticles can be utilized for signal amplification and real-time monitoring of biological processes. 2. Brain Tumor Therapy : o Polymer nanoparticles offer a promising platform for targeted drug delivery and imaging in brain tumor therapy. o Functionalized polymer nanoparticles can cross the blood-bra...