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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.

A comprehensive, integrated, and accessible textbook presenting core neuroscientific topics from a computational perspective, tracing a path from cells and circuits to behavior and cognition.

A Guide for the Practicing Neuroscientist

A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data.

Learning Invariant Representations

A mathematical framework that describes learning of invariant representations in the ventral stream, offering both theoretical development and applications.

Foundations of Neural Computation
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.

Probabilistic Approaches to Neural Coding

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

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.

Computational and Mathematical Modeling of Neural Systems
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
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
Exploring the Neural Code

What does it mean to say that a certain set of spikes is the right answer to a computational problem? In what sense does a spike train convey information about the sensory world? Spikes begins by providing precise formulations of these and related questions about the representation of sensory signals in neural spike trains. The answers to these questions are then pursued in experiments on sensory neurons. Intended for neurobiologists with an interest in mathematical analysis of neural data as well as the growing number of physicists and mathematicians interested in information processing by "real" nervous systems, Spikes provides a self-contained review of relevant concepts in information theory and statistical decision theory.

Neuropsychology and Cognitive Neuroscience

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

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.

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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