Taught by: Larry Abbott, Columbia University (June 10, 2015)
Video: Learning in Recurrent Neural Networks (1:16:39)
Description: Introduction to recurrent neural networks and their application to modeling and understanding real neural circuits.
Slides: Learning in Recurrent Neural Networks (PDF)
Additional Resources:
- Notes on Boerlin, Machens and Deneve (learning in recurrent spiking network) - Larry Abbott’s notes
- Exercises
- Recursive Least-Squares Algorithm - Larry Abbott’s notes
- Sompolinsky, H., Crisanti, A., and Sommers, H. J. (1988) “Chaos in Random Neural Networks.” Physical Review Letters 61: 259.
- Rajan, K., Abbott, L. F. & Sompolinsky, H. (2010) “Stimulus-Dependent Suppression of Chaos in Recurrent Neural Networks.” Physical Review E 82: 011903.
- Sussillo, D. & Abbott, L. F. (2009) “Generating Coherent Patterns of Activity from Chaotic Neural Networks.” Neuron 63: 544–557.
- Boerlin, M., Machens, C. K. & Deneve, S. (2013) Predictive coding of dynamic variables in balanced spiking networks, PLoS Computational Biology 9: e1003258.
