Computational Tutorials Recordings
Recordings
Jun 16, 2017
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 dopamine neurons.
Taught By: Sam Gershman, Harvard...
May 10, 2017
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, Boston University
References for the tutorial can...
Apr 26, 2017
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 LSTMs and implements a basic LSTM in TensorFlow. The...
Mar 22, 2017
In in-vivo animal models, neuroscience experiments in electrophysiology are commonly performed with the use of extracellular electrodes implanted in the cell layer of specific brain regions of interest. These electrodes record voltage traces and capture the occurrence of putative action potentials...
Feb 16, 2017
Evan Remington, MIT
An introduction to the OpenMind computing resources, and best practices when using it.
More information can be found here - https://stellar.mit.edu/S/project/bcs-comp-tut/index.html
Feb 16, 2017
Satrajit Ghosh, MIT
The tutorial will focus on singularity, an HPC container service, and how to incorporate it into your openmind workflow.
More information can be found here - https://stellar.mit.edu/S/project/bcs-comp-tut/index.html
Update from the presenter:
From the presenter:
"The singularity...
Sep 13, 2016
Oscillatory neuronal synchronization has been hypothesized to provide a mechanism for dynamic network coordination. Rhythmic neuronal interactions can be quantified using multiple metrics, each with their own advantages and disadvantages. This tutorial reviews current analysis methods used in the...
Jan 22, 2016
Introduction to linear dynamical systems, stochastics, and the evolution of probability density, with application to modeling the generation of neural signals.
Taught by: Seth Egger, MIT
Each tutorial consists of a lecture, and then 'office hours' time to work through exercises and discuss current...
Jan 21, 2016
Taught by: Sam Norman-Haignere
Dimensionality Reduction using the method of Independent Components Analysis, and its application to the analysis of fMRI data. Dimensionality Reduction II tutorial from the tutorial series in computational topics for brain and cognitive sciences. Lecture slides,...
Jan 19, 2016
Taught by: Emily Mackevicius and Greg Ciccarelli
Introduction to dimensionality reduction and the methods of Principal Components Analysis and Singular Value Decomposition. Dimensionality Reduction I tutorial from the tutorial series in computational topics for brain and cognitive sciences. Lecture...
Jun 17, 2015
Introduction to using and understanding deep neural networks, their application to visual object recognition, and software tools for building deep neural network models.
Taught By: Phillip Isola, MIT
Exercises and references posted here: https://stellar.mit.edu/S/project/bcs-comp-tut/
Thanks to BCS...
Jun 10, 2015
Taught by: Larry Abbott
Introduction to recurrent neural networks and their application to modeling and understanding real neural circuits.
Exercises and references posted here: https://stellar.mit.edu/S/project/bcs-comp-tut/
Thanks to BCS seminar committee and postdoc committee (especially Evan...