Deep Learning

Abstract

Abstract Deep learning—the study of learning in artificial neural networks containing many layers of neuron-like elements—captures and even exceeds human abilities in many domains. Because human brains are also deep neural networks that learn, deep networks provide a fertile ground for modeling human memory and learning, and they open up the possibility of joint engagement between the study of biological and artificial intelligence. This chapter introduces the basic constructs employed in deep learning and considers several of the widely used deep-learning paradigms and architectures. It then considers how the constructs of deep neural network models relate to traditional constructs in the psychological literature on learning and memory. Next, the chapter reviews recent developments in the field of reinforcement learning that have broad implications for human learning and memory. The chapter concludes by noting that human intelligence still exceeds current deep learning systems in many ways and describes future directions for research aimed toward bridging the gap.