Books in the Computational Neuroscience Series series

This volume, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.

This book is concerned with sensory cue integration both within and between sensory modalities, and focuses on the emerging way of thinking about cue combination in terms of uncertainty. The probabilistic approaches elaborated in Sensory Cue Integration aim to formalize the uncertainty of cues. They describe cue combination as the nervous system's attempt to minimize uncertainty in its estimates and to choose successful actions.

The complexity of the brain and the protean nature of behaviour remain the most elusive area of science, but also the most important. Written by 23 experts from many areas of systems neuroscience, this book provides a useful roadmap to the field of systems neuroscience, and aims to serve as a source of inspiration for explorers of the brain.

Using experimental and theoretical findings from cellular biophysics, this book explains the computational functions of single neurons. The topics include the linear cable equation; cable theory as applied to passive dendritic trees and dendritic spines; and chemical and electrical synapses and how to treat them from a computational point of view.