Principles of Neural Coding

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.

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 Leski, 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

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.