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...
A connectome is a comprehensive map of neural connections in the brain, representing the intricate network of structural and functional pathways that facilitate communication between different brain regions. 1. Definition: - A connectome is a detailed representation of the wiring diagram of the brain, illustrating the complex network of axonal projections, synaptic connections, and communication pathways between neurons and brain regions. - The connectome encompasses both the structural connectivity, which refers to the physical links between neurons and brain areas, and the functional connectivity, which reflects the patterns of neural activity and information flow within the brain. 2. Structural Connectome: - The structural connectome provides a map of the anatomical connections in the brain, showing how neurons are physically linked through axonal projections, white matter pathways, and synaptic contacts. - Techniques ...