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Reservoir Computing for .NET (RCNet)

The aim of the project is to make the reservoir computing methods easy to use and available for .net platform without dependency on external heterogeneous libraries. Two main reservoir computing methods are called Echo State Network (ESN) and Liquid State Machine (LSM). The implemented solution supports both of these methods. However, since both ESN and LSM are based on very similar principles, it is also possible to combine them. It is possible to connect analog and spiking neurons within one reservoir. It is also possible to define separate analog and spiking reservoirs and then use them at the same time. This approach, as I believe, opens up new interesting possibilities. This general implementation is called "State Machine" in the context of RCNet.

State Machine conceptual view

Figure explanation:

• The data flow direction is from left to right
• The input data is inserted into the reservoir via special input neurons (yellow balls). The task of input neurons is to prepare input data into the desired form (analog data or spike train)

Training steps:

1. Collect all known input and desired output data pairs
2. Normalize and standardize the data
3. Transform the input data through a reservoir with recurrently connected neurons into a set of predictors
4. Train readout layer to be able to map the predictors to the desired outputs as best as possible

Usage steps:

1. Get next known input data
2. Normalize and standardize the data by the same way as during the training
3. Transform the input data into predictors
4. Let readout layer to compute output data (prediction)