Embedded thumbnail for Identifying Subgroups in Biomedical Datasets using Data Attribution
Recorded:
Oct 9, 2024
Uploaded:
October 28, 2024
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All Captioned Videos, Computational Tutorials
Speaker(s):
Djuna von Maydell, MIT
Understanding how training data influences model predictions ("data attribution") is an active area of machine learning research. In this tutorial, we will introduce a data attribution method (datamodels: https://gradientscience.org/datamodels-1/)...
Embedded thumbnail for Building and Training Deep Learning Models in PyTorch
Recorded:
Nov 8, 2023
Uploaded:
November 16, 2023
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Speaker(s):
Valmiki Kothare, MIT.
BCS Computational Tutorial Series with Valmiki Kothare, MIT. In this tutorial, we will use deep learning on EEG and EMG mice data to predict sleep stages (Wakefulness, REM, Non-REM). We will walk through an example Jupyter Notebook in which we load...
Embedded thumbnail for  FindingFive: An online, non-profit platform for behavioral research
Recorded:
Apr 28, 2023
Uploaded:
May 4, 2023
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Computational Tutorials
Speaker(s):
Ting Qian, Noah Nelson
Tutorial on FindingFive FindingFive is a non-profit organization dedicated to supporting behavioral scientists’ web-based research by making it easy and cost-effective to implement experiments and collect data. With FindingFive, researchers can...
Embedded thumbnail for Diffusion and Score-Based Generative Models
Recorded:
Dec 12, 2022
Uploaded:
December 16, 2022
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Speaker(s):
Yang Song, Stanford University
Generating data with complex patterns, such as images, audio, and molecular structures, requires fitting very flexible statistical models to the data distribution. Even in the age of deep neural networks, building such models is difficult because...
Embedded thumbnail for Cell-Type Specific Transcriptomics
Recorded:
Nov 21, 2022
Uploaded:
November 22, 2022
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Speaker(s):
Sebastian Pineda, MIT
Tutorial on transcriptomic assays - TRAP and snRNA-seq sequencing with Sebastian Pineda High-throughput sequencing assays have become ubiquitous and indispensable tools in molecular neurobiology. They provide a means to investigate gene expression,...
Embedded thumbnail for Tutorial on Statistical Inference On Representational Geometries
Recorded:
Oct 25, 2022
Uploaded:
November 8, 2022
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Speaker(s):
Heiko Schütt, NYU
Representational similarity analysis (RSA) is a popular method for comparing representations when a mapping between them is not available. One important comparison RSA is used for is between neuronal measurements and models of brain computation like...
Embedded thumbnail for GLMsingle: a toolbox for improving single-trial fMRI response estimates
Recorded:
Apr 28, 2022
Uploaded:
May 2, 2022
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Speaker(s):
Jacob Prince, MIT
Advances in modern artificial intelligence have inspired a paradigm shift in human neuroscience, yielding large-scale functional magnetic resonance imaging (fMRI) datasets that provide high-resolution brain responses to tens of thousands of...
Embedded thumbnail for ThreeDWorld (TDW) Tutorial
Recorded:
Apr 1, 2022
Uploaded:
April 13, 2022
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Speaker(s):
Jeremy Schwatrz, Seth Alter,
In this tutorial, Jeremy Schwartz will walk us through the features and capabilities of ThreeDWorld, a high-fidelity, multi-modal platform for interactive physical simulation. Next, Seth Alter will conduct a tutorial lab session. The repository is...
Embedded thumbnail for Continuous-time deconvolutional regression: A method for studying continuous dynamics in naturalistic data
Recorded:
Feb 28, 2022
Uploaded:
February 28, 2022
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Speaker(s):
Cory Shain, MIT
Abstract: Naturalistic experiments are of growing interest to neuroscientists and cognitive scientists. Naturalistic data can be hard to analyze because critical events can occur at irregular intervals, and measured responses to those events...
Embedded thumbnail for Tutorial: Recurrent neural networks for cognitive neuroscience
Recorded:
Aug 30, 2021
Uploaded:
August 30, 2021
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Computational Tutorials
CBMM Speaker(s):
Guangyu Robert Yang
Robert Guangyu Yang, MIT In this hands-on tutorial, we will work together through a number of coding exercises to see how RNNs can be easily used to study cognitive neuroscience questions. We will train and analyze RNNs on various cognitive...

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