Embedded thumbnail for Principles and applications of relational inductive biases in deep learning
Recorded:
Apr 11, 2019
Uploaded:
April 19, 2019
Part of
Computational Tutorials
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
Recorded:
Apr 2, 2019
Uploaded:
April 3, 2019
Part of
Computational Tutorials
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)
Recorded:
Nov 13, 2018
Uploaded:
November 14, 2018
Part of
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...
Embedded thumbnail for Unsupervised discovery of temporal sequences in high-dimensional datasets (1:14:24)
Recorded:
Apr 19, 2018
Uploaded:
May 9, 2018
Part of
Computational Tutorials
CBMM Speaker(s):
Emily Mackevicius
Speaker(s):
Andrew Bahle
The ability to identify interpretable, low-dimensional features that capture the dynamics of large-scale neural recordings is a major challenge in neuroscience. Dynamics that include repeated temporal patterns (which we call sequences), are not...
Embedded thumbnail for Dimensionality Reduction for Matrix- and Tensor-Coded Data [Part 2] (46:05)
Recorded:
Sep 5, 2017
Uploaded:
September 11, 2017
Part of
Computational Tutorials
Speaker(s):
Alex Williams
Taught by: Alex Williams, Stanford University In many scientific domains, data is coded in large tables or higher-dimensional arrays. Compressing these data into smaller, more manageable representations is often critical for extracting scientific...
Embedded thumbnail for Dimensionality Reduction for Matrix- and Tensor-Coded Data [Part 1] (53:36)
Recorded:
Sep 5, 2017
Uploaded:
September 11, 2017
Part of
Computational Tutorials
Speaker(s):
Alex Williams
Taught by: Alex Williams, Stanford University In many scientific domains, data is coded in large tables or higher-dimensional arrays. Compressing these data into smaller, more manageable representations is often critical for extracting scientific...
Embedded thumbnail for Calcium Imaging Data Cell Extraction (1:07:56)
Recorded:
Jul 12, 2017
Uploaded:
July 17, 2017
Part of
Computational Tutorials
Speaker(s):
Pengcheng Zhou
[recording cut short due to technical issues] In vivo calcium imaging through microendoscopic lenses enables imaging of previously inaccessible neuronal populations deep in the brains of freely moving animals. It is computationally challenging to...
Embedded thumbnail for  Reinforcement Learning (1:09:49)
Recorded:
Jun 16, 2017
Uploaded:
June 29, 2017
Part of
Computational Tutorials
CBMM Speaker(s):
Samuel Gershman
This tutorial introduces the basic concepts of reinforcement learning and how they have been applied in psychology and neuroscience. Hands-on exercises explore how simple algorithms can explain aspects of animal learning and the firing of...
Embedded thumbnail for Better Science Code (43:43)
Recorded:
May 10, 2017
Uploaded:
May 12, 2017
Part of
Computational Tutorials
Speaker(s):
Eric Denovellis
This tutorial focuses on best coding practices to develop code that is reusable, sharable, and bug free. It highlights issues such as documentation, version control, and unit testing, through hands-on computer exercises. Taught by: Eric Denovellis,...
Embedded thumbnail for An Introduction to LSTMs in TensorFlow (59:45)
Recorded:
Apr 26, 2017
Uploaded:
April 28, 2017
Part of
Computational Tutorials
Speaker(s):
Harini Suresh, Nicholas Locascio
Long Short Term Memory networks (LSTMs) are a type of recurrent neural network that can capture long term dependencies, which are frequently used for natural language modeling and speech recognition. This tutorial covers the conceptual basics of...

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