This chapter reviews briefly the history of connectionist models, identifying major ideas and major areas of applications. It gives a brief review of major paradigms of learning in neural networks. The chapter addresses the issue of symbolic processing in connectionist models. It looks in particular at the following types of learning: supervised learning, unsupervised learning, and reinforcement learning. The connectionist revolution has spurred vigorous theoretical debates about the nature of cognition and the various approaches toward understanding. The chapter discusses the hybrid connectionist models, which incorporate both connectionist and symbolic processing methods. In contrast to connectionist implementationism, hybrid connectionist models may be considered a synthesis of connectionist models and traditional symbolic models. Hybrid models, such as CLARION, have been used to address a wide variety of issues in cognitive science and artificial intelligence, including human learning, reasoning, problem solving, creativity, motivational dynamics, metacognitive processes, and above all, human consciousness.