Neural-network models of learning and memory: leading questions and an emerging framework

Real-time neural-network models provide a conceptual framework for formulat ing questions about the nature of cognition, an architectural framework far mapping cognitive Functions to brain regions, a semantic framework far def ining terms, and a computational framework for testing hypotheses. This art icle considers key questions about how a physical system might simultaneous ly support one-trial teaming and lifetime memories, in the context of neura l models that test possible solutions to the problems posed. Model properti es point to partial answers, and model limitations lead to new questions. P lacing individual system components in the context of a unified real-time n etwork allows analysis to move from the revel of neural processes, includin g teaming laws and rules of synaptic transmission, to cognitive processes, including attention and consciousness.