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...
Polysialylation, the addition of polysialic acid chains to glycoproteins like the Neural Cell Adhesion Molecule (NCAM), plays a crucial role in brain development. Here are key points outlining the significance of polysialylation in brain development: 1. Neuronal Migration : o Polysialylation of NCAM is essential for neuronal migration during brain development. o Polysialic acid chains on NCAM reduce cell adhesion, allowing migrating neurons to detach from neighboring cells and move to their appropriate locations in the developing brain. 2. Axon Guidance : o Polysialylation of NCAM is involved in axon guidance, the process by which growing axons navigate to their target regions to establish neural circuits. o Polysialic acid on NCAM modulates axon growth cone behavior, facilitating the extension of axons and their pathfinding to specific target areas. 3. Synaptic Plasticity : o Polysialylation of NCAM contri...