An overview of the mechanisms and evolution of spatial cognition, integrating evidence from psychology, neuroscience, cognitive science, and computational geometry. Understanding how we deal with space requires input from many fields, including ethology, neuroscience, psychology, cognitive science, linguistics, geography, and spatial information theory. In From Geometry to Behavior, cognitive neuroscientist Hanspeter A. Mallot provides an overview of the basic mechanisms of spatial behavior in animals and humans, showing how they combine to support higher-level performance. Mallot explores the biological mechanisms of dealing with space, from the perception of visual space to the constructions of large space representations: that is, the cognitive map. The volume is also relevant to the epistemology of spatial knowledge in the philosophy of mind. Mallot aims to establish spatial cognition as a scientific field in its own right. His general approach is psychophysical, in that it focuses on quantitative descriptions of behavioral performance and their real-world determinants, thus connecting to the work of theorists in computational neuroscience, robotics, and computational geometry. After an overview of scientific thinking about space, Mallot covers spatial behavior and its underlying mechanisms in the order of increasing memory involvement. He describes the cognitive processes that underlie advanced spatial behaviors such as directed search, wayfinding, spatial planning, spatial reasoning, object building and manipulation, and communication about space. These mechanisms are part of the larger cognitive apparatus that also serves visual and object cognition; understanding events, actions, and causality; and social cognition, which includes language. Of all of these cognitive domains, spatial cognition most likely occurred first in the course of evolution and is the most widespread throughout the animal kingdom.

Explains the relationship of electrophysiology, nonlinear dynamics, and the computational properties of neurons, with each concept presented in terms of both neuroscience and mathematics and illustrated using geometrical intuition. In order to model neuronal behavior or to interpret the results of modeling studies, neuroscientists must call upon methods of nonlinear dynamics. This book offers an introduction to nonlinear dynamical systems theory for researchers and graduate students in neuroscience. It also provides an overview of neuroscience for mathematicians who want to learn the basic facts of electrophysiology. Dynamical Systems in Neuroscience presents a systematic study of the relationship of electrophysiology, nonlinear dynamics, and computational properties of neurons. It emphasizes that information processing in the brain depends not only on the electrophysiological properties of neurons but also on their dynamical properties. The book introduces dynamical systems, starting with one- and two-dimensional Hodgkin-Huxley-type models and continuing to a description of bursting systems. Each chapter proceeds from the simple to the complex, and provides sample problems at the end. The book explains all necessary mathematical concepts using geometrical intuition; it includes many figures and few equations, making it especially suitable for non-mathematicians. Each concept is presented in terms of both neuroscience and mathematics, providing a link between the two disciplines. Nonlinear dynamical systems theory is at the core of computational neuroscience research, but it is not a standard part of the graduate neuroscience curriculum—or taught by math or physics department in a way that is suitable for students of biology. This book offers neuroscience students and researchers a comprehensive account of concepts and methods increasingly used in computational neuroscience. An additional chapter on synchronization, with more advanced material, can be found at the author's website, www.izhikevich.com.

A unique overview of the human language faculty at all levels of organization. Language is not only one of the most complex cognitive functions that we command, it is also the aspect of the mind that makes us uniquely human. Research suggests that the human brain exhibits a language readiness not found in the brains of other species. This volume brings together contributions from a range of fields to examine humans' language capacity from multiple perspectives, analyzing it at genetic, neurobiological, psychological, and linguistic levels. In recent decades, advances in computational modeling, neuroimaging, and genetic sequencing have made possible new approaches to the study of language, and the contributors draw on these developments. The book examines cognitive architectures, investigating the functional organization of the major language skills; learning and development trajectories, summarizing the current understanding of the steps and neurocognitive mechanisms in language processing; evolutionary and other preconditions for communication by means of natural language; computational tools for modeling language; cognitive neuroscientific methods that allow observations of the human brain in action, including fMRI, EEG/MEG, and others; the neural infrastructure of language capacity; the genome's role in building and maintaining the language-ready brain; and insights from studying such language-relevant behaviors in nonhuman animals as birdsong and primate vocalization. Section editors Christian F. Beckmann, Carel ten Cate, Simon E. Fisher, Peter Hagoort, Evan Kidd, Stephen C. Levinson, James M. McQueen, Antje S. Meyer, David Poeppel, Caroline F. Rowland, Constance Scharff, Ivan Toni, Willem Zuidema