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Computational Neuroscience

Computational neuroscience is an approach to understanding the development and function of nervous systems at many different structural scales, including the biophysical, the circuit, and the systems levels. Methods include theoretical analysis and modeling of neurons, networks, and brain systems and are complementary to empirical techniques in neuroscience. Areas and topics of particular interest to this book series include computational mechanisms in neurons, analysis of signal processing in neural circuits, representation of sensory information, systems models of sensorimotor integration, computational approaches to biological motor control, and models of learning and memory. Further topics of interest include the intersection of computational neuroscience with engineering, from representation and dynamics, to observation and control.

An argument that the complexities of brain function can be understood hierarchically, in terms of different levels of abstraction, as silicon computing is.

Computational and Mathematical Modeling of Neural Systems

Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate.

Global State Interactions

Experimental and theoretical approaches to global brain dynamics that draw on the latest research in the field.

How the Brain Builds Representations, Predicts Events, and Makes Decisions

A novel theoretical framework that describes a possible rationale for the regularity in how we move, how we learn, and how our brain predicts events.

The Emerging Intersection between Control Theory and Neuroscience

How powerful new methods in nonlinear control engineering can be applied to neuroscience, from fundamental model formulation to advanced medical applications.

Toward a Common Multivariate Framework for Cell Recording and Functional Imaging

How visual content is represented in neuronal population codes and how to analyze such codes with multivariate techniques.

The Geometry of Excitability and Bursting

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.

A guide to computational modeling methods in neuroscience, covering a range of modeling scales from molecular reactions to large neural networks.

Probabilistic Approaches to Neural Coding

Experimental and theoretical neuroscientists use Bayesian approaches to analyze the brain mechanisms of perception, decision-making, and motor control.

A Foundation for Motor Learning

An introduction to the computational biology of reaching and pointing, with an emphasis on motor learning.

Computation, Representation, and Dynamics in Neurobiological Systems
The Collected Papers of Wilfrid Rall with Commentaries

This collection of fifteen previously published papers, some of them not widely available, have been carefully chosen and annotated by Rall's colleagues and other leading neuroscientists.

The authors encompass a broad background, from biophysics and electrophysiology to psychophysics, neurology, and computational vision. However, all the chapters focus on a common issue: the role of the primate (including human) cerebral cortex in memory, visual perception, focal attention, and awareness.

Foundations of Neural Computation

This book exemplifies the interplay between the general formal framework of graphical models and the exploration of new algorithm and architectures. The selections range from foundational papers of historical importance to results at the cutting edge of research.

Foundations of Neural Computation
Information Processing in Perception and Visual Behavior
Foundations of Neural Computation
Neuropsychology and Cognitive Neuroscience

Provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes.

This book brings together the biology and computational features of the basal ganglia and their related cortical areas along with select examples of how this knowledge can be integrated into neural network models.

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