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Principles of Neural Coding

Edited by Rodrigo Quian Quiroga, Stefano Panzeri

CRC Press – 2013 – 663 pages

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  • Add to CartHardback: $149.95
    978-1-43-985330-6
    May 5th 2013

Description

Understanding how populations of neurons encode information is the challenge faced by researchers in the field of neural coding. Focusing on the many mysteries and marvels of the mind has prompted a prominent team of experts in the field to put their heads together and fire up a book on the subject. Simply titled Principles of Neural Coding, this book covers the complexities of this discipline. It centers on some of the major developments in this area and presents a complete assessment of how neurons in the brain encode information. The book collaborators contribute various chapters that describe results in different systems (visual, auditory, somatosensory perception, etc.) and different species (monkeys, rats, humans, etc).Concentrating on the recording and analysis of the firing of single and multiple neurons, and the analysis and recording of other integrative measures of network activity and network states—such as local field potentials or current source densities—is the basis of the introductory chapters.

  • Provides a comprehensive and interdisciplinary approach
  • Describes topics of interest to a wide range of researchers

The book then moves forward with the description of the principles of neural coding for different functions and in different species and concludes with theoretical and modeling works describing how information processing functions are implemented. The text not only contains the most important experimental findings, but gives an overview of the main methodological aspects for studying neural coding. In addition, the book describes alternative approaches based on simulations with neural networks and in silico modeling in this highly interdisciplinary topic. It can serve as an important reference to students and professionals.

Contents

Section I Methods

Physiological Foundations of Neural Signals

Kevin Whittingstall and Nikos K. Logothetis

Biophysics of Extracellular Spikes

Costas A. Anastassiou, György Buzsáki, and Christof Koch

Local Field Potentials: Biophysical Origin and Analysis

Gaute T. Einevoll, Henrik Lindén, Tom Tetzlaff, Szymon Łęski,

and Klas H. Pettersen

Spike Sorting

Juan Martínez and Rodrigo Quian Quiroga

Spike-Train Analysis

Inés Samengo, Daniel Elijah, and Marcelo A. Montemurro

Synchronization Measures

Thomas Kreuz

Role of Correlations in Population Coding

Peter E. Latham and Yasser Roudi

Decoding and Information Theory in Neuroscience

Rodrigo Quian Quiroga and Stefano Panzeri

Section II Experimental Results

Neural Coding of Visual Objects

Charles E. Connor

Coding in the Auditory System

Jan Schnupp

Coding in the Whisker Sensory System

Mathew E. Diamond and Ehsan Arabzadeh

Neural Coding in the Olfactory System

Ron A. Jortner

Coding across Sensory Modalities: Integrating the Dynamic Face with the Voice

Chandramouli Chandrasekaran and Asif A. Ghazanfar

Population Coding by Place Cells and Grid Cells

Jill K. Leutgeb, Emily A. Mankin, and Stefan Leutgeb

Coding of Movement Intentions

Hansjörg Scherberger, Rodrigo Quian Quiroga, and Richard A. Andersen

Neural Coding of Short-Term Memory

Stefanie Liebe and Gregor Rainer

Role of Temporal Spike Patterns in Neural Codes

Rasmus S. Petersen

Adaptation and Sensory Coding

Miguel Maravall

Sparse and Explicit Neural Coding

Peter Földiák

Information Coding by Cortical Populations

Kenneth D. Harris

Information Content of Local Field Potentials: Experiments and Models

Alberto Mazzoni, Nikos K. Logothetis, and Stefano Panzeri

Principles of Neural Coding from EEG Signals

Fernando H. Lopes da Silva

Gamma-Band Synchronization and Information Transmission

Martin Vinck, Thilo Womelsdorf, and Pascal Fries

Decoding Information from fMRI Signals

Jakob Heinzle and John-Dylan Haynes

Section III Theoretical and In Silico Approaches

Dynamics of Neural Networks

Nicolas Brunel

Learning and Coding in Neural Networks

Timothée Masquelier and Gustavo Deco

Ising Models for Inferring Network Structure from Spike Data

John A. Hertz, Yasser Roudi, and Joanna Tyrcha

Vocal Learning with Inverse Models

Richard H. R. Hahnloser and Surya Ganguli

Computational Models of Visual Object Recognition

Gabriel Kreiman

Coding in Neuromorphic VLSI Networks

Giacomo Indiveri

Open-Source Software for Studying Neural Codes

Robin A. A. Ince

Author Bio

Rodrigo Quian Quiroga is a neuroscientist at the University of Leicester UK. He holds a research chair and is the director of the Centre for Systems Neuroscience and the head of the Bioengineering Research Group at the University of Leicester. In 2010, he obtained the Royal Society Wolfson Research Merit Award. His main research interest is on the study of the principles of visual perception and memory. Together with colleagues at Caltech and UCLA, he discovered what has been named "Concept cells" or "Jennifer Aniston neurons"—neurons in the human brain that play a key role in memory formation.

Stefano Panzeri received a Laurea in Physics from the University of Torino, and a PhD in computational neuroscience from SISSA, Trieste, Italy. He has held personal research fellowship awards in theoretical physics and computational neuroscience, including an INFN Junior Fellowship in Theoretical Physics at Turin University, an EU Marie Curie Postdoctoral Fellowship at the University of Oxford, and an MRC Research Fellowship in Neuroinformatics at the University of Newcastle. He has worked as senior scientist at the Italian Institute of Technology since 2007 and as chair in the Formal Analysis of Cortical Networks at the University of Glasgow since 2012.

Name: Principles of Neural Coding (Hardback)CRC Press 
Description: Edited by Rodrigo Quian Quiroga, Stefano Panzeri. Understanding how populations of neurons encode information is the challenge faced by researchers in the field of neural coding. Focusing on the many mysteries and marvels of the mind has prompted a prominent team of experts in the field to put their...
Categories: Neuroscience, Biomedical Engineering