Reservoir Computing Architecture as Neurotrophic Computing
Dat Tran, Electrical and Computer Engineering
Reservoir Computing (RC) is an alternative to the traditional RNN for machine learning that avoids the training of large recurrent networks. In an RC architecture, inputs are mapped to the internal states of a reservoir and the outputs from the untrained reservoir provide input signals to a readout layer. The readout layer is trained with a simple algorithm to perform a particular task. The reservoirs are simulated with different devices using Modal Nodal Analysis (MNA) to build PSpice simulation platforms. The software is python-based with OrGanic Environment for Reservoir computing (OGER) as a Python toolbox for building, training, and evaluating modular learning architectures on large datasets.
