Improved stability and convergence with three factor learning

摘要

Donald Hebb postulated that if neurons fire together they wire together. However, Hebbian learning is inherently unstable because synaptic weights will self-amplify themselves: the more a synapse drives a postsynaptic cell the more the synaptic weight will grow. We present a new biologically realistic way of showing how to stabilise synaptic weights by introducing a third factor which switches learning on or off so that self-amplification is minimised. The third factor can be identified by the activity of dopaminergic neurons in ventral tegmental area which leads to a new interpretation of the dopamine signal which goes beyond the classical prediction error hypothesis.