This is an excerpt from my email I wrote regarding next steps:
1. Make a diagram of the conception of the system as it was before, including how I was thinking of links between high level neurons having different weights for each high level feature (which may be made up of a vector of lower level features, eg. histogram). This would be the context in which to situate discussions of machine learning systems.
2. Read more about LIDA to understand better how percepts and knowledge from memory stores are structured, and if a subset of the architecture can be used.
3. Wish list for machine learning / clustering (in no particular order)
• Ideally statistical (stores a distribution of patterns, not all the patterns themselves, but this is flexible)
• Output could be manifest in connection weights between neuron-like structures that represent higher level concepts. (concept as distribution of activation) Each item should belong to a concept to a degree, not Boolean.
• If it is deep learning and I can forgo the feature extraction, it must be elegant (simple enough for me to understand) and be feasible for high resolution colour input images.
• If it is deep learning it should be generative: can learn high level abstractions (concepts) from sensory data, and also construct sensory data from those high level abstractions.
Thank is all I can think of off hand.