This also seems much more intuitive and efficient + it falls in line well with what apparently happens in Machine Learning for example in word encodings: Each word/concept will rather be represented by some vector in the space of activations, the "neurons"/dimensions themselves meaning different things.
This also seems much more intuitive and efficient + it falls in line well with what apparently happens in Machine Learning for example in word encodings: Each word/concept will rather be represented by some vector in the space of activations, the "neurons"/dimensions themselves meaning different things.