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- Brain Inspired
- Publisher |
- Paul Middlebrooks
- Media Type |
- audio
- Podknife tags |
- Artificial Intelligence,
- Data Science,
- Interview,
- Machine Learning,
- Neuroscience,
- Philosophy,
- Psychology,
- Science & Medicine,
- Technology
- Publication Date |
- Aug 02, 2018
- Episode Duration |
- 00:45:39
David and I cover recurrent neural networks (RNNs), his work using RNNs to study motor brain processes, how dynamical systems theory is a useful approach to brains and AI, and more. Click the episode for the show notes.
David and I cover recurrent neural networks (RNNs), his work using RNNs to study motor brain processes, how dynamical systems theory is a useful approach to brains and AI, and more. Click the episode for the show notes.
- David’s Twitter account
- Papers we discuss:
- Sussillo, D.S. & Abbott, L. F. (2009). Abbott-Coherent-Patterns-August-2009.pdf">Generating Coherent Patterns of Activity from Chaotic Neural Networks. Neuron 63(4).
- Sussillo, D. (2014) Neural circuits as computational dynamical systems. Current Opinion in Neurobiology 25:156-63.
- Sussillo, D. & Barak, O. (2013). neco.pdf">Opening the Black Box: Low-dimensional dynamics in high-dimensional recurrent neural networks. Neural Computation 25(3).
- Sussillo, D., Jozefowicz, R., Abbott, L.F., Pandarinath, C. (2016) LFADS – Latent Factor Analysis via Dynamical Systems. arXiv:1608.06315
- What’s a recurrent neural network?
- What’s a dynamical system?
- Neuropixels
- Hopfield Networks
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