Another guest editor here... I met Max at this year's ICCNS and he suggested writing a guest entry for Neurdon. The ideas hopefully compliment some of the stuff Ethan blogged about.
I'm a 4th-year PhD student in the Institute of Cognitive Science at The University of Louisiana at Lafayette. When I entered the program, I was mostly interested in AI and evolutionary algorithms. I wanted to evolve a Go-playing program. But my interests shifted, especially in my first year when I read Jeff Hawkins' On Intelligence. I thought it was great stuff, and I liked two things central to his framework: 1) The temporal aspect of cognition, and 2) The crucial role of feedback. He made a convincing case that every modality and skill is essentially a matter of learning and processing sequences. So that's where I started focusing my attention. Read the rest of this entry »
First, a hearty welcome to Ethan, you're starting to make this whole enterprise a little less incestuous! Anyway, 
Max asked me to post some information about how time could act as a ‘supervising’ learning signal to create invariant representations in IT (particular in reference to