Computational Tutorials Recordings

Recordings

Embedded thumbnail for Diffusion and Score-Based Generative Models
Dec 12, 2022
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 they typically require an intractable normalization...
Embedded thumbnail for Cell-Type Specific Transcriptomics
Nov 21, 2022
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, dissect gene interactions and pathways, and...
Embedded thumbnail for Tutorial on Statistical Inference On Representational Geometries
Oct 25, 2022
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 deep neural networks. RSA is a two step process,...
Embedded thumbnail for GLMsingle: a toolbox for improving single-trial fMRI response estimates
Apr 28, 2022
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 naturalistic visual stimuli. Because such experiments...
Embedded thumbnail for ThreeDWorld (TDW) Tutorial
Apr 1, 2022
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 available here (please note that it is not needed...
Embedded thumbnail for Continuous-time deconvolutional regression: A method for studying continuous dynamics in naturalistic data
Feb 28, 2022
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 can overlap and interact in complex ways. For...
Embedded thumbnail for Tutorial: Recurrent neural networks for cognitive neuroscience
Aug 30, 2021
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 neuroscience tasks. Familiarity of Python and basic...
Embedded thumbnail for suite2P: a fast and accurate pipeline for automatically processing functional imaging recordings
Jul 29, 2021
The combination of two-photon microscopy recordings and powerful calcium-dependent fluorescent sensors enables simultaneous recording of unprecedentedly large populations of neurons. While these sensors have matured over several generations of development, computational methods to process their...
Embedded thumbnail for Learning what we know and knowing what we learn: Gaussian process priors for neural data analysis
Jul 8, 2021
Guillaume Hennequin, Kris Jensen - University of Cambridge Colab notebooks: Introduction to FA and GPFA as probabilistic generative models Fitting an example data set from a primate reaching task with GPFA Additional papers and resources Rasmussen & Williams (2006) - The standard textbook...
Embedded thumbnail for Exiting flatland: measuring, modeling, and synthesizing animal behavior in 3D
Apr 8, 2021
Mechanistic studies of complex, ethological animal behaviors are poised to define the next decade of neuroscience. Fully understanding the ontogeny, evolution, and neural basis of these behaviors requires precise 3D measurements of their underlying kinematics. While 2D convolutional networks have...
Embedded thumbnail for Linear Analysis of RNN Dynamics
Nov 19, 2020
Recurrent neural networks (RNNs) are a powerful model for neural and cognitive phenomena. However, interpreting these models can be a challenge. In this tutorial, we will discuss how dynamical systems theory provides some tools for understanding RNNs. In particular, we will focus on the theory and...
Embedded thumbnail for Nonlinear Dimensionality Reduction
Sep 22, 2020
Christian Bueno, University of California, Santa Barbara Working with lower dimensional representations of data can be valuable for simplifying models, removing noise, and visualization. When data is distributed in geometrically complicated ways, tools such as PCA can quickly run into limitations...

Pages