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+Neural Network library used for the Guppies project. Parameters that can be set
+by it follow:
+
+## NETWORK STRUCTURE
+Defines the main topology of the Neural Network (i.e. the way that Nodes
+connect with each other). The four possible choices (in order of
+complexity) are:
+
+### Single MultiLayer Perceptron
+A FeedForward network with 1 hidden layer. Information flows
+unidirectionally from input nodes to output nodes (and not the
+other way around).
+
+### Dual MultiLayer Perceptron
+Same as above, but with two hidden layers instead of one.
+
+### Simple Recurrent Network (Elman Network)
+This type of recurrent network has a set of "context" units that
+store the output of the (single) hidden layer and feeds it back
+to the input layer on the next time-step, giving it a kind of
+short term memory.
+
+### Fully Recurrent Network
+All Nodes in this network are connected to each other. It's the
+most complex (and processor intensive) network of the four.
+
+## NODE STRUCTURE
+Defines the structure of each network node. The two choices are:
+
+### Neuron
+Each neuron computes its outputs from a given set of inputs. Output
+equals the weighted sum of all inputs.
+
+### Memory Cell
+This kind of node is based in the Long-Short Term Memory recurrent
+network model. It contains 4 neurons, 3 of them act as "gates" that
+allow it to block input, store it and output it, thus being able to
+hold in information or "memories" for a long time span. It's the
+most complex (and processor intensive) type of node.
+
+## NODES PER HIDDEN LAYER
+Number of nodes that reside on each hidden layer. SingleMLPs, SimpleRNs and
+FullyRNs have one hidden layer. DualMLPs have two (thus, their number of
+hidden nodes is "this value" x 2). The more nodes, the more complex the
+Neural Networks of the Guppies are (and the more time it'll take to evolve
+them).
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