We use a type of reservoir computing called the liquid state machine (LSM) to explore learning transfer. The Liquid State Machine (LSM) is a neural network model that uses a reservoir of recurrent spiking neurons as a filter for a readout function. We develop a method of training the reservoir, or liquid, that is not driven by residual error. Instead, the liquid is evaluated based on its ability to separate different classes of input into different spatial patterns of neural activity. Using this method, we train liquids on two qualitatively different types of artificial problems. Resulting liquids are shown to substantially improve performance on either problem regardless of which problem was used to train the liquid, thus demonstrating a significant level of learning transfer.
['David Norton', 'Dan Ventura']
Improving the separability of a reservoir facilitates learning transfer