Embedded thumbnail for Learning what we know and knowing what we learn: Gaussian process priors for neural data analysis
Learning what we know and knowing what we learn: Gaussian process priors for neural data analysis
Recorded:
Jul 8, 2021
Uploaded:
July 12, 2021
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Speaker(s):
Guillaume Hennequin, Kris Jensen
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...
Embedded thumbnail for Exiting flatland: measuring, modeling, and synthesizing animal behavior in 3D
Exiting flatland: measuring, modeling, and synthesizing animal behavior in 3D
Recorded:
Apr 8, 2021
Uploaded:
April 9, 2021
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Speaker(s):
Jesse Marshall, Harvard University
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...
Embedded thumbnail for Linear Analysis of RNN Dynamics
Linear Analysis of RNN Dynamics
Recorded:
Nov 19, 2020
Uploaded:
November 23, 2020
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Speaker(s):
Eli Pollock
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...
Embedded thumbnail for Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
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Sep 22, 2020
Uploaded:
September 23, 2020
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Speaker(s):
Christian Bueno, University of California, Santa Barbara
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,...
Embedded thumbnail for Using Lookit to run developmental studies online
Using Lookit to run developmental studies online
Recorded:
Sep 3, 2020
Uploaded:
September 4, 2020
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CBMM Speaker(s):
Maddie Pelz
Lookit is an online platform for designing and running asynchronous developmental studies. This technology allows for more diverse and representative populations to participate in developmental studies than would typically be able to engage in the...
Embedded thumbnail for Adversarial examples and human-ML alignment
Adversarial examples and human-ML alignment
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Jul 23, 2020
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July 24, 2020
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Speaker(s):
Shibani Santurkar, MIT
Machine learning models today achieve impressive performance on challenging benchmark tasks. Yet, these models remain remarkably brittle---small perturbations of natural inputs, known as adversarial examples, can severely degrade their behavior. Why...
Embedded thumbnail for Decoding Animal Behavior Through Pose Tracking
Decoding Animal Behavior Through Pose Tracking
Recorded:
Jul 9, 2020
Uploaded:
July 10, 2020
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Speaker(s):
Talmo Pereira, Princeton University
Talmo Pereira, Princeton University Behavioral quantification, the problem of measuring and describing how an animal interacts with the world, has been gaining increasing attention across disciplines as new computational methods emerge to automate...
Embedded thumbnail for Principles and applications of relational inductive biases in deep learning
Principles and applications of relational inductive biases in deep learning
Recorded:
Apr 11, 2019
Uploaded:
April 19, 2019
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CBMM Speaker(s):
Kelsey Allen
Kelsey Allen, MIT Common intuition posits that deep learning has succeeded because of its ability to assume very little structure in the data it receives, instead learning that structure from large numbers of training examples. However, recent work...
Embedded thumbnail for Neural decoding of spike trains and local field potentials with machine learning in python
Neural decoding of spike trains and local field potentials with machine learning in python
Recorded:
Apr 2, 2019
Uploaded:
April 3, 2019
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Speaker(s):
Omar Costilla Reyes
Speaker: Omar Costilla Reyes, PhD Neural decoding has applications in neuroscience from understanding neural populations to build brain-computer interfaces. In this computational tutorial, I will introduce neural decoding principles from a machine...
Embedded thumbnail for Bayesian Inference in Generative Models (49:45)
Bayesian Inference in Generative Models (49:45)
Recorded:
Nov 13, 2018
Uploaded:
November 14, 2018
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Computational Tutorials
CBMM Speaker(s):
Maddie Cusimano
Speaker(s):
Luke Hewitt, MIT
Speaker: Luke Hewitt, MIT
Talk prepared and Q&A session by: Maddie Cusimano & Luke Hewitt, MIT Bayesian inference is ubiquitous in models and tools across cognitive science and neuroscience. While the mathematical formulation of...

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