Following are some short videos that show some of the more interesting dreams generated by the system in the last test. They give a sense of the periodicity of some dreams and how dreams look with these very noisy percepts:
As refining the MLP beyond what it already does seems no easy task, I thought I would move to the previous problem: To make sure that there is enough temporal diversity in the percepts for the predictor to learn from longer temporal sequences. I’m running a proper test now, but following are a few frames selected from a botched previous test. The percepts are drawn on a black background, and there is no visual difference between perception, mind-wandering or dreaming.
Due to the results from previous posts, I thought I would try another approach: train the network by feeding it not the state at one moment in time, but concatenate multiple moments of time into a single vector such that the network has some history to learn from. In implementing this, I did not find any improvement over the old method: (PHASE2 is the old method, PHASE3 is the new method)
If the MLP is able to learn a sequence, and demonstrates that learning by producing the correct pattern for a particular input, then feedback should result in the network replaying the sequence. There is no difference between feeding the network state t+1 no matter where that pattern comes from. So why is the network apparently learning the sequence in the previous post, while feedback does not result in replaying the learned sequence?
In order to deal with the problem of static dreams Philippe asked me to create a synthetic data set that has particular temporal properties. The idea is that we can use it to get a sense of both the distribution of percepts over time and the resemblance / boundedness of dreaming and mind wandering compared to perception. The data-set is 2000 frames and contains three objects: a blue circle that gets bigger, a red circle that gets smaller, and a green rectangle that moves from the left to the right. The background toggles between white and grey every 200 frames. Additionally, there was a single all black frame at the end of the data-set to mark epochs. Following are the first and last frames of the synthetic data set, not including the trailing black frame: