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Unveiling Hidden Neural Codes: SIMPL – A Scalable and Fast Approach for Optimizing Latent Variables and Tuning Curves in Neural Population Data

Dr. Rishabh Pathak
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...
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What is Laplace Dirichlet-Rule-Based-Methods (LDRBMs)?

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Dr. Rishabh Pathak


 

Laplace-Dirichlet-Rule-Based-Methods (LDRBMs) are a class of Rule-Based-Methods (RBMs) used to generate cardiac muscle fiber orientations in electromechanical models. These methods define the orientation of myocardial fibers within the heart by solving Laplace boundary-value problems. Specifically, LDRBMs determine the transmural, apico-basal, and inter-ventricular distances as solutions to Laplace equations within the computational domain. By prescribing boundary conditions on the epicardial, endocardial, and base surfaces of the heart, LDRBMs provide a mathematical framework for generating realistic fiber architectures that play a crucial role in electric signal propagation and myocardial contraction. These methods are essential for accurately modeling the biomechanics of the heart and are widely used in computational cardiac electromechanics research.

 

Piersanti, R., Regazzoni, F., Salvador, M., Corno, A. F., Dede', L., Vergara, C., & Quarteroni, A. (2021). 3D-0D closed-loop model for the simulation of cardiac biventricular electromechanics. *arXiv preprint arXiv:2108.01907*.


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