Percept Diversity – Videos

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:

Percept Diversity

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.


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Synthetic Dataset – Replay

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)


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Synthetic Dataset Continued

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?

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Synthetic Dataset

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:

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