Applying Nengo's unique approach to critical AI research areas

It is useless to apply the power of brain neuromorphic models to simple arithmetic or classical neural networks that do not require any more than TensorFlow 2.x, for example.

But it is also a waste of time to try to solve problems with classical networks that neuromorphic computing can solve better with organic brain models. For example:

• Deep learning, TensorFlow 2. Convolutional models use a unique activation function such as ReLU (see Chapter 9, Abstract Image Classification with Convolutional Neural Networks (CNNs)). Neuromorphic neurons have a variety of reactions when stimulated.
• Neuromorphic models integrate time versus more static DL algorithms. When we run neuromorphic models, we are closer to the reality of our time-driven biological models.
• The Human Brain Project, https://www.humanbrainproject.eu/en/, provides wide research and examples of how neuromorphic computing provides...