> Nothing works nearly as well by comparison. Depends on what you mean by "well". It would be foolish of me to deny the advances of deep learning. However, the

> Nothing works nearly as well by comparison. Depends on what you mean by "well". It would be foolish of me to deny the advances of deep learning. However, there are limitations and the NEF can act as a bridge in those cases. For example, if you want: 1. To approximate a dynamic system 2. To use symbolic computation 3. Use the neural networks as an initial guess at a solution to a problem and then want to use gradient descent to figure out the actual solution I talk about using NEF as a bridge in [this blog post](https://medium.com/@seanaubin/deep-learning-is-almost-the-brain-3aaecd924f3d https://medium.com/@seanaubin/deep-learning-is-almost-the-br...), if you're curious. Unfortunately, I don't know of any examples using the "initial guess" paradigm that I describe, but that's more of a "not enough people using Nengo" problem.