v0.11.0: Improved support for recurrent and spiking neural networks

• added a class FeedbackNetwork that allows to add feedback connections between nodes in a network. Intended use: First, build the feedforward network structure that maps from input to output. Then add feedback edges, marking them via the keyword argument feedback=True of the FeedbackNetwork.add_edge method.
• both feedforward and feedback edges can be trained
• Re-worked the backpropagation through time and recursive least squares training methods to work with feedback weights
• updated the readthedocs documentation and its configuration file
• added new pytests for the different optimization methods: Ridge regression, backpropagation, and recursive least squares
• added a class MultiSpikeNode that allows to define multiple spiking variables within a single differential equation-based node.