Self–organizing neural systems based on predictive learning

Abstract
The ability to predict future events based on the past is an important attribute of organisms that engage in adaptive behaviour. One prominent computational method for learning to predict is called temporal–difference (TD) learning. It is so named because it uses the difference between successive predictions to learn to predict correctly. TD learning is well suited to modelling the biological phenomenon of conditioning, wherein an organism learns to predict a reward even though the reward may occur later in time. We review a model for conditioning in bees based on TD learning. The model illustrates how the TD–learning algorithm allows an organism to learn an appropriate sequence of actions leading up to a reward, based solely on reinforcement signals. The second part of the paper describes how TD learning can be used at the cellular level to model the recently discovered phenomenon of spike–timing–dependent plasticity. Using a biophysical model of a neocortical neuron, we demonstrate that the shape of the spike–timing–dependent learning windows found in biology can be interpreted as a form of TD learning occurring at the cellular level. We conclude by showing that such spike–based TD–learning mechanisms can produce direction selectivity in visual–motion–sensitive cells and can endow recurrent neocortical circuits with the powerful ability to predict their inputs at the millisecond time–scale.