Skip to main content

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...
Post a Comment

The Mathematical Models used in 3D-0D closed-loop model for the simulation of cardiac biventricular electromechanics.

Image
Dr. Rishabh Pathak


 

The mathematical models to the reconstruction of cardiac muscle fiber architecture in biventricular geometries and the development of a 3D cardiac electromechanical (EM) model coupled with a 0D closed-loop model for the cardiovascular system. Here is an overview of the mathematical models discussed in the document:

 1. Fiber Generation Methods: The document outlines the methods used to reconstruct the cardiac muscle fiber architecture in biventricular geometries. Specifically, Laplace-Dirichlet-Rule-Based-Methods (LDRBMs) are employed to generate realistic fiber orientations within the heart. These methods involve solving Laplace boundary-value problems to determine the orientation of myocardial fibers based on boundary conditions on the heart's surfaces.

 2. 3D Cardiac EM Model: The document presents a detailed 3D cardiac electromechanical model that captures the biophysical processes involved in heart function. This model integrates aspects of electrophysiology, active contraction of cardiomyocytes, tissue mechanics, and blood circulation within the heart chambers. By considering these components, the model can simulate the electromechanical behavior of the heart in a comprehensive manner.

 3. 0D Closed-Loop Model: In addition to the 3D cardiac EM model, the document discusses the incorporation of a 0D closed-loop model for the cardiovascular system. This model represents the hemodynamics of the entire circulatory system using lumped parameters to simulate blood flow dynamics, pressure-volume relationships, and systemic interactions. The coupling of the 3D EM model with the 0D closed-loop model enables a holistic simulation of the heart's electromechanical activity in the context of circulatory dynamics.

 4. Numerical Approximation: The document also covers the numerical discretization strategies employed to solve the coupled 3D-0D model. This includes space and time discretizations using the Finite Element Method (FEM) with different mesh resolutions to handle the varying scales of electromechanical and hemodynamic processes. The Segregated-Intergrid-Staggered (SIS) approach is utilized to sequentially solve the core models contributing to cardiac EM and blood circulation.

 Overall, the mathematical models presented in the document provide a framework for simulating biventricular electromechanics and studying the complex interactions between the heart and the circulatory system. These models enable researchers to investigate cardiac function, electromechanical behavior, and hemodynamic responses in a comprehensive and integrated manner.


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*.

Categories:

Comments

Post a Comment

Popular posts from this blog

Distinguishing Features of Electrode Artifacts

Dr. Rishabh Pathak
Electrode artifacts in EEG recordings can present with distinct features that differentiate them from genuine brain activity.  1.      Types of Electrode Artifacts : o Variety : Electrode artifacts encompass several types, including electrode pop, electrode contact, electrode/lead movement, perspiration artifacts, salt bridge artifacts, and movement artifacts. o Characteristics : Each type of electrode artifact exhibits specific waveform patterns and spatial distributions that aid in their identification and differentiation from true EEG signals. 2.    Electrode Pop : o Description : Electrode pop artifacts are characterized by paroxysmal, sharply contoured transients that interrupt the background EEG activity. o Localization : These artifacts typically involve only one electrode and lack a field indicating a gradual decrease in potential amplitude across the scalp. o Waveform : Electrode pop waveforms have a rapid rise and a slower fall compared to in...
Post a Comment
Read more

Non-probability Sampling

Dr. Rishabh Pathak
Non-probability sampling is a sampling technique where the selection of sample units is based on the judgment of the researcher rather than random selection. In non-probability sampling, each element in the population does not have a known or equal chance of being included in the sample. Here are some key points about non-probability sampling: 1.     Definition : o     Non-probability sampling is a sampling method where the selection of sample units is not based on randomization or known probabilities. o     Researchers use their judgment or convenience to select sample units that they believe are representative of the population. 2.     Characteristics : o     Non-probability sampling methods do not allow for the calculation of sampling error or the generalizability of results to the population. o    Sample units are selected based on the researcher's subjective criteria, convenience, or accessibility....
Post a Comment
Read more

How Brain Computer Interface is working in the Neurosurgery ?

Dr. Rishabh Pathak
Brain-Computer Interfaces (BCIs) have profound implications in the field of neurosurgery, providing innovative tools for monitoring brain activity, aiding surgical procedures, and facilitating rehabilitation. 1. Overview of BCIs in Neurosurgery BCIs in neurosurgery aim to create a direct communication pathway between the brain and external devices, which can be utilized for various surgical applications. These interfaces can aid in precise surgery, enhance patient outcomes, and provide feedback on brain function during operations. 2. Mechanisms of BCIs in Neurosurgery 2.1 Types of BCIs Invasive BCIs : These involve implanting devices directly into the brain tissue, providing high-resolution data. Invasive BCIs, such as electrocorticography (ECoG) grids, are often used intraoperatively for detailed monitoring of brain activity. Non-invasive BCIs : Primarily utilize EEG and fNIRS. They are helpful for pre-operative assessments and monitoring post-operati...
Post a Comment
Read more

Research Methods

Dr. Rishabh Pathak
Research methods refer to the specific techniques, procedures, and tools that researchers use to collect, analyze, and interpret data in a systematic and organized manner. The choice of research methods depends on the research questions, objectives, and the nature of the study. Here are some common research methods used in social sciences, business, and other fields: 1.      Quantitative Research Methods : §   Surveys : Surveys involve collecting data from a sample of individuals through questionnaires or interviews to gather information about attitudes, behaviors, preferences, or demographics. §   Experiments : Experiments involve manipulating variables in a controlled setting to test causal relationships and determine the effects of interventions or treatments. §   Observational Studies : Observational studies involve observing and recording behaviors, interactions, or phenomena in natural settings without intervention. §   Secondary Data Analys...
Post a Comment
Read more

Distinguishing Features of Paroxysmal Fast Activity

Dr. Rishabh Pathak
The distinguishing features of Paroxysmal Fast Activity (PFA) are critical for differentiating it from other EEG patterns and understanding its clinical significance.  1. Waveform Characteristics Sudden Onset and Resolution : PFA is characterized by an abrupt appearance and disappearance, contrasting sharply with the surrounding background activity. This sudden change is a hallmark of PFA. Monomorphic Appearance : PFA typically presents as a repetitive pattern of monophasic waves with a sharp contour, produced by high-frequency activity. This monomorphic nature differentiates it from more disorganized patterns like muscle artifact. 2. Frequency and Amplitude Frequency Range : The frequency of PFA bursts usually falls within the range of 10 to 30 Hz, with most activity occurring between 15 and 25 Hz. This frequency range is crucial for identifying PFA. Amplitude : PFA bursts often have an amplit...
Post a Comment
Read more