Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)
Signiﬁcant plasticity in sensory cortical representations can be driven in mature animals either by behavioural tasks that pair sensory stimuli with reinforcement, or by electrophysiological experiments that pair sensory input with direct stimulation of neuromodulatory nuclei, but usually not by sensory stimuli presented alone. Biologically motivated theories of representational learning, however, have tended to focus on unsupervised mechanisms, which may play a signiﬁcant role on evolutionary or devel- opmental timescales, but which neglect this essential role of reinforce- ment in adult plasticity. By contrast, theoretical reinforcement learning has generally dealt with the acquisition of optimal policies for action in an uncertain world, rather than with the concurrent shaping of sensory representations. This paper develops a framework for representational learning which builds on the relative success of unsupervised generative- modelling accounts of cortical encodings to incorporate the effects of reinforcement in a biologically plausible way.