sounds really interesting….yes, please, go ahead and prepare a post… figures will be helpful!

Max

It’s a bit too long to go into much detail in a blog comment, but the basic idea is that the model is hierarchical, with standard forward connections and also delay lines between layers. Representations are formed by the conjunctive activity of current and delayed activation from units in lower layers, effectively binding past and present activity at a higher level in the hierarchy. The successive firing of the units in the lower layer, closely followed by the firing of the unit in the higher layer strengthens the weights between them, effectively recruiting the higher-level unit to represent the conjunction.

This probably isn’t very clear from the brief description. Max, you had mentioned possibly contributing a guest entry. If you like I can try to put something together after the IJCNN. Just let me know.

Max

And hello Max. Too bad we couldn’t get a game together at the conference. I believe George and Hawkins refer to the clustering stages you’re talking about as “temporal pooling”. I read through George’s dissertation, but for me there was a significant gap between how the model was learning invariant representations and how biological neurons might implement similar algorithms. Luckily this provided motivation for my dissertation work.

As for the swap paradigm used in both the human and monkey studies, I was wondering how well a paradigm would work that involved a subject observing a moving object where the image is swapped periodically (ideally only for a short time period, possibly such that they can’t subjectively notice the swap) as the subject tracks the stimuli moving across a field. Similar experiments could also be done by swapping while rotating or swapping while increasing and/or decreasing the size of the stimulus. Has the swap paradigm been used in this way?

Max Versace