CHOICE_Magazine Review

This "bottom-up" approach to data-driven neuroscientific discovery serves as the perfect primer for those who study brain sciences, cognitive sciences, artificial intelligence, neuro-engineering, neuropsychology, and empirically oriented philosophy. Though this approach also clearly embraces the structure and function of system-based analyses (sensory systems, memory, cognition, and language), discussion is very much based at the level of individual neuronal activities. The "What?" of neuroscientific inquiry is complemented by probing questions and discoveries concerning neuron-based computations--the "How?" of neuroscientific inquiry. The author emphasizes that "understanding cognitive functions such as object recognition, memory recall, attention, and decision-making requires single neuron data to be closely linked to computational models of how the interactions between large numbers of neurons and many networks of neurons allow these cognitive problems to be solved" (p. 5). In the end, Rolls (Univ. of Warwick, Oxford Centre for Computational Neuroscience) has three goals: to understand humans better, to develop more-effective treatments in cases of malfunction, and "to produce more useful computers and machines." This neuronal network approach stands in contrast to connectionist approaches and also focuses exclusively on higher primate and human modeling. Helpful chapter highlights and several practical appendixes are provided, and the bibliography is excellent. Summing Up: Highly recommended. Upper-division undergraduates. Graduate students, faculty, and professionals. --Heidi Storl, Augustana College (IL)