untracks the output folder
creates code to assign a class to each RF using SVM. Also creates a file to compute RF sharpness but code not finished yet.
integrates training of the second layer with the training of the first layer (all within hebbianRL.py). Importantly, the reward now also affects learning in the classification layer, which addresses/solves the problem with having increased representation. That is, without using the reward in the classification layer, this layer does not take advantage of the increase in number of neurons. Now it should be checked whether this makes sense in terms of biology as well.
rearranges file structure. Now only hebbianRL.py needs to be called; neuralClassifier and assessRF are now under support/
adds some imports forgotten in last commit
puts all classifiers in the same file. There are now three types of classifiers: neural, SVM, and neuronClass. the change in learning rate is now made only at the learning step (increase in firing rate of post neurons), so it doesn't influence the learning of the classifier weights ('neural' probably doesn't work well). I know have to figure out an optimal way for benchmarking the representation (i.e., which classifier to use). I will also add in the next commit two different increases in learning rate: alpha for acetylcholine (relevance) and delta for dopamine (decision feedback).