research
Resources
RNNs in Neuroscience
In February 2021, I had the honor of hosting the COSYNE (Computational and Systems Neuroscience) Tutorial on recurrent neural network models in neuroscience. Given the virtual format of the meeting, I’m pleased to be able to make the materials accessible to a larger community of learners.
lecture materials
Both lectures are available on the COSYNE YouTube channel (see lecture title links) under a Creative Commons license. To request access to the lecture slides, please email: [email protected] & [email protected]
Foundational elements of recurrent neural network (RNN) models
Dr. Rajan covered the basic design elements (“building blocks”) of neural network models, the role of recurrent connections, linear and non-linear activity, types of time-varying activity (“dynamics”) produced by RNNs, input-driven vs. spontaneous dynamics, etc. The main goal of this lecture is to look at both the tractability and computational power of RNN models, and to appreciate why they have become such a crucial part of the neuroscientific arsenal.
Applications of RNNs in the field of neuroscience
Dr. Rajan reviewed some of the ways in which RNNs have been applied in neuroscience to leverage existing experimental data, to infer mechanisms inaccessible from measurements alone, and to make predictions that guide experimental design. The main goal of this lecture is to appreciate what sorts of insight can be gained by "training RNNs to do something” in a manner consistent with experimental data collected from the biological brain.
problem set
If you’d like to deepen your understanding of recurrent neural networks, I encourage you to complete a problem set created in collaboration with the COSYNE Tutorial TAs. The problem set has detailed instructions and questions to work through. Problems 1 and 2 are intermediate and should be done after watching Lecture 1; Problem 3 is advanced and should be done after watching Lecture 2. Solutions are available in Julia, MATLAB, and Python.
Suggested process for beginners:
Make a personal copy of the Solution Scripts. Read through the script (with solutions) and annotate it with questions and/or your understanding of the process. Attempt to solve Problems 1 and 2 using the solutions as a scaffold.
Suggested process for advanced students or for use in a class setting:
Make a personal copy of the Solution Scripts. Delete the provided solutions and then work through the problems in your own copy. Once you've completed the problems, compare your answers against the provided solutions to check your work.
Solution Scripts
- Julia solution script
- MATLAB solution scripts
- Python:
teaching assistants
Thank you to the fearless group of TAs who helped shape the tutorial with their hard work and quick intellect.
references
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