The network created by these researchers was an able student and readily learned to recognise a variety of words in grammatical contexts. The next thing was to manipulate the parameters of the network in ways that are thought to model what may be happening to biological neuronal networks in schizophrenia.

There are two major hypotheses that were modelled: the first is that networks in schizophrenia are “over-pruned”. This fits with a lot of observations, including neuroimaging data showing reduced connectivity in the brains of people suffering with schizophrenia. It also fits with the age of onset of the florid expression of this disorder, which is usually in the late teens to early twenties. This corresponds to a period of brain maturation characterised by an intense burst of pruning of synapses – the connections between neurons.

Each new frame represents the discovery of an improvement over the previous best RNN. The goal is to be able to classify the parity of a binary string, in this case, of length 10. The string is fed sequentially to the RNN, one bit per time step. The RNN I used for this particular experiment has a hidden layer of two neurons. This was done so that I could easily plot the state space in two dimensions. The x-axis represents the activation of the first neuron, the y-axis is the activation of the second. Each point within a given frame represents the 'state' of the RNN at the very end of processing a string. The number of points per frame corresponds to the number of training examples. The first few frames appear to have fewer points; this is just overlap. The points have been colored red and blue to denote whether the target output should have been even or odd parity; they are not representative of the actual output of the RNN.

If you’re worried about being able to understand why it makes a decision, you might want to look into a neurofuzzy classifier.