Neuromorphic computing
Let's go directly to the core of our thought process to understand neuromorphic computing. For AI experts, I would like to summarize the voyage from our classical models to cutting-edge neuromorphic models in a single phrase:
from mind to brain
If we take this further, M is the set of all of our mental representations and B is the world of physical reactions that lead to thinking patterns.
In this sense, M is a set of everything we have explored up to this point in this book:
M = {rule based systems, machine learning, deep learning, evolutionary algorithms … m}
m is any mathematical mental representation of the world surrounding us. In deep learning, for example, an artificial neural network will try to make sense of the chaos of an image by searching the patterns it can find in an image through lower dimensions and higher levels of abstraction.
However, a mental construction, no matter how efficient it seems, remains...