According to Parks, Levine, and Long, as well as many of the contributors to this edited volume, neural networks are needed to bridge the gap between neurobiology and psychology, including psychopathology. The book starts with a general introduction to network modeling, but subsequent chapters take the term "neural network" to denote just any connected brain areas. So we learn—in well-written chapters by well-known authors—about neuroanatomy (chapter 2), attention networks (chapter 3), lexical retrieval (chapter 12), and semantic abnormalities in patients with Alzheimer’s disease (chapter 16) without learning anything about neural network models. But there are also role-model chapters on simulations and real-world data, which show what this type of research can do for the understanding of such diverse phenomena as mathematics and acalculia (chapter 10), cognition and schizophrenia (chapter 8), and hippocampal function and memory (chapter 13). Within these and other chapters, the reader can find valuable ideas and discussions regarding neural network modeling. Such models, for example, rarely prove any hypotheses, but they are great in generating them. They are the "theoretical concepts that are intermediate between the details of neuroscientific observations and the box-and-arrow diagrams of traditional information processing or neuropsychological theories," as Servan-Schreiber and Cohen (p. 191) point out, and they may in some cases "be viewed as an existence proof that such a learning mechanism is feasible" (Dehaene et al., p. 246). The limitations of some models are explicitly discussed in some chapters (for example, p. 267), while other chapters explore their applications to psychology.