Topic: MATLAB

The development of algorithms for high-accuracy animal tracking is fundamental to unravel the neural basis of behavior. The recent growth of Deeplabcut, an open source toolbox for marker-less pose estimation based on transfer learning with deep neural networks, promises to greatly enhance our ability to track animal pose with high-accuracy and high-efficiency, because it can match human labelling accuracy with minimal training data. Here, we implement Deeplabcut routines and develop a pipeline to accurately track the pose of mice undergoing defensive behaviors. Defensive behaviors are elicited

My talk will discuss the use of some of these four, simple Matlab functions to simulate models of timing, and to fit models to empirical data. Feel free to examine the code and the relatively brief book chapter that explains the code before the talk if you would like to learn more about computational/mathematical modeling.

Last in a three-part lecture series about using matlab in neuroimaging. For full details go here: https://bit.ly/matlabimaging

Second in a three-part lecture series about using matlab in neuroimaging. For full details: https://mailchi.mp/b3f65bbc346d/onneuro-7885485

First in a three-part lecture series about using matlab in neuroimaging. For full details go here: https://bit.ly/matlabimaging

Understanding the mechanisms underlying neurological disorders and offering fresh viewpoints and remedies, neuroscientists must bridge the gap between emerging technology and experimental research. We computed the cell migration ratio of confocal pictures of astrocytes and glioblastoma (GBM) in our recent study. The primary goal is to comprehend how astrocyte placement in the 3D cell culture model influences GBM migration. For image processing applications, there are numerous computational tools available. MATLAB provides an extensive library and the ability to create new, bespoke funct