2022–2023
- FindingFive: An Online, Non-Profit Platform for Behavioral Research Ting Qian and Noah Nelson, FindingFive (April 28, 2023)
- Diffusion and Score-Based Generative Models Yang Song, Stanford University (December 12, 2022)
- Cell-Type Specific Transcriptomics Sebastian Pineda, MIT (November 21, 2022)
- Tutorial on Statistical Inference on Representational Geometries Heiko Schütt, NYU (October 25, 2022)
- GLMsingle: A Toolbox for Improving Single-Trial fMRI Response Estimates Jacob Prince, MIT (April 28, 2022)
- ThreeDWorld (TDW) Tutorial Jeremy Schwartz and Seth Alter, MIT (April 1, 2022)
- Continuous-Time Deconvolutional Regression: A Method for Studying Continuous Dynamics in Naturalistic Data Cory Shain, MIT (February 28, 2022)
2020–2021
- Recurrent Neural Networks for Cognitive Neuroscience Guangyu Robert Yang, CBMM (August 30, 2021)
- Suite2P: A Fast and Accurate Pipeline for Automatically Processing Functional Imaging Recordings Carsen Stringer, HHMI Janelia Research Campus (July 29, 2021)
- Learning What We Know and Knowing What We Learn: Gaussian Process Priors for Neural Data Analysis Guillaume Hennequin and Kris Jensen, University of Cambridge (July 8, 2021)
- Exiting Flatland: Measuring, Modeling, and Synthesizing Animal Behavior in 3D Jesse Marshall, Harvard University (April 8, 2021)
- Linear Analysis of RNN Dynamics Eli Pollock, MIT (November 19, 2020)
- Nonlinear Dimensionality Reduction Christian Bueno, University of California, Santa Barbara (September 22, 2020)
- Using Lookit to Run Developmental Studies Online Maddie Pelz, MIT (September 3, 2020)
- Adversarial Examples and Human-ML Alignment Shibani Santurkar, MIT (July 23, 2020)
- Decoding Animal Behavior Through Pose Tracking Talmo Pereira, Princeton University (July 9, 2020)
2017–2019
- Principles and Applications of Relational Inductive Biases in Deep Learning Kelsey Allen, MIT (April 11, 2019)
- Neural Decoding of Spike Trains and Local Field Potentials with Machine Learning in Python Omar Costilla Reyes, MIT (April 2, 2019)
- Bayesian Inference in Generative Models Luke Hewitt, MIT (November 13, 2018)
- Unsupervised Discovery of Temporal Sequences in High-Dimensional Datasets Emily Mackevicius and Andrew Bahle, MIT (April 19, 2018)
- Dimensionality Reduction for Matrix- and Tensor-Coded Data (1 & 2) Alex Williams, Stanford (September 5, 2017)
- Calcium Imaging Data Cell Extraction Pengcheng Zhou, Columbia (July 12, 2017)
- Reinforcement Learning Sam Gershman, Harvard (June 16, 2017)
- Better Science Code Eric Denovellis, BU (May 10, 2017)
- An Introduction to LSTMs in TensorFlow Harini Suresh and Nick Locascio, MIT (April 26, 2017)
- An Introduction to Spike Sorting Jai Bhagat and Caroline Moore-Kochlacs, MIT (March 22, 2017)
2015–2016
- Linear Network Theory and Sloppy Models Mark Goldman, UC Davis (November 21, 2016)
- Functional Connectivity Analysis Methods and Their Interpretational Pitfalls Andre M. Bastos, MIT (September 13, 2016)
- Tensor Methods Anima Anandkumar, UC Irvine (July 21, 2016)
- Dynamical Systems in Neuroscience Seth Egger, MIT (January 22, 2016)
- Dimensionality Reduction II Sam Norman-Haignere, MIT (January 21, 2016)
- Dimensionality Reduction I Emily Mackevicius and Greg Ciccarelli, MIT (January 19, 2016)
- Cluster Computing and OpenMind (1 & 2) Evan Remington and Satrajit Ghosh, MIT (January 12, 2016)
- Decoding Analyses to Understand Neural Content and Coding Ethan Meyers, Hampshire College and MIT (July 23, 2015)
- Learning in Deep Neural Networks Phillip Isola, MIT (June 17, 2015)
- Learning in Recurrent Neural Networks Larry Abbott, Columbia (June 10, 2015)
- Bayesian Methods: Brain and Cognitive Perspectives Mehrdad Jazayeri and Josh Tenenbaum, MIT (June 9, 2015)
