Topic: connectome-based reservoirs

Reservoir computing is a machine learning framework in which only the output layer is trained to perform a task; the rest – composed of an input layer and recurrent neural network – has fixed connections. The benefit of restricting training to the output layer is that it does not require the computationally expensive and biologically-implausible backpropagation typically used to train deep-learning networks. However, while reservoirs can do tasks well, their performance is unreliable; depending on the random initialisation of fixed weights within the network, their capabilities vary wildly, si