Just a note to say that I'm still doing a lot of background research. I'm currently writing a review paper for a psych class I'm in. It will cover reinforcement learning in natural and artificial neural networks and why biologically-realistic algorithms are important. A lot of the papers I've been reading recently are from Nature Neuroscience and Nature Reviews Neuroscience.
Something my Verve code has been lacking is reward sensing/predicting by the neural network. After reading a few neuroscience papers dealing with reward signals in human brains, I think my artificial neural network should be able to: 1) sense "rewards" (kind of a vague term, but this will probably be programmed explicitly, as in neuralNet->addReward(0.5)), and 2) predict rewards (i.e. some special neurons should output a signal when rewards are expected to be given).
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The idea is that if the neural net could learn which actions produced which rewards (either positive or negative reinforcement), it could use that mechanism to maximize reward intake by outputting sequences that lead to more positive rewards.
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