RLAI Reinforcement Learning and Artificial Intelligence (RLAI)

Computational Modeling of Animal Learning

The ambition of this page is to keep track of some of the things going on in the animal-learning research meeting in the RLAI group.

This meeting started with James taking an independent study with rich, and ended with the completion of that course in Dec 2004.

This meeting took place in Rich's office once a week. 

2004/11/17 -

After the meeting, we discussed an important problem in physics, whose solution is here.

2004/10/20 -
Today we considered a variety of strategies for stimulus representation such as a series of exponentials of varying lengths triggered by onset and offset of stimuli.  We considered running these traces through tile-coding mechanisms and also just series of multiple-timescale bumps.  

Then we tried to make a network model to explain sensory precondition.  The fundamental mystery was why a prediction of a stimulus should be treated similarly to the stimulus itself.  This seems to be required to get the natural, easy network model to work.  To achieve this, we had to resort to the theory that a stimulus is one of its own best predictors.  Then the presence of the stimulus will give rise to the prediction of the stimulus.  Then we achieve the idea of: if A predicts B and B predicts C, then A should predict C.  If A is a short stimulus, then it should not predict itself, which should result in the extinction of its self prediction.  If the CR actually prevents the US, will the prediction of the US be extinguished and therefore the CR as well, or will a new stimulus serve to reinforce the CR instead?

We decided a good goal for James's project (or maybe for all of us) would be to block out the space of models (of response generation and stimulus representation) and propose some specific experiments that could be done to distinguish them.


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