bogged down

Earlier this week I thought I was on the verge of a breakthrough, but instead I sank into the bog of mathematics, analytic issues, and philosophical delusions.

There is a tendency to think of the identification of an object, say a dog or better still, a specific dog, as coinciding in the brain with the firing of a specific neuron, or perhaps a set of neurons. That might in turn fire a pre-verbal response that one could be conscious of, then the actual verbal response, whether as a thought or as speach: "Hugo," my dog.

Some would make this a paradigm for invariance. Hugo can change in position, wear a sweater, age or even die, but the Hugo object is invariant.

But that, the noun, is the end result. It is not the system that creates invariance. Nor do I think that the system of building up small clues, as described by Jeff Hawkins and implemented to some extent by Numenta, is sufficient to explain intelligence, though it might serve for object identification.

I am even wondering about Hebbian learning, in which transitions in neural systems are achieved by changing weights of neural connections. It is simple to model, but if it isn't what is really going on in the brain (or is only part of what is really going on), assuming it is sufficient could be a block to forward progress.

Maybe I am way off track here. I just read again about how no one could explain all the spectral data accumulated in the 19th century. Then Bohr threw out two assumptions about electrodynamics and added a very simple assumption, that electrons near an atomic nucleus have a minimal energy orbit, and quantum physics finally was off to the races.

On the other hand, sometimes a slow steady program like Numenta's works better than waiting for a breakthrough. I'm giving my neurons the weekend off and going to the German film festival at the Point Arena Theater.

Pointing Choices

Quote of the day:

"This convention is at variance with that used in many expert systems (e.g. MYCIN), where rules point from evidence to hypothesis (e.g., if symptom, then disease), thus denoting a flow of mental inference. By contrast, the arrows in Bayesian networks point from causes to effects, or from conditions to consequences, thus denoting a flow of constraints attributed to the physical world."

Judea Pearl, Probabilistic Reasoning in Intelligent Systems, p. 151

Eyes Follow Brain Shifting of Attention

In case you missed it:

Human Brain Predicts Visual Attention Before Eyes Even Move

This is a confirmation that one of the principle jobs of the brain is to make predictions, then use the senses to confirm or deny the predictions.

Tensor Introduction

Largely as a prelude to my own work, I posted an introduction to tensors at OpenIcon. It may improve as time passes.