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Full 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. Biophysics of Extracellular Spikes. Local Field Potentials: Biophysical Origin and Analysis. Spike Sorting. Spike-Train Analysis. Synchronization Measures. Role of Correlations in Population Coding. Decoding and Information Theory in Neuroscience. Section II Experimental Results. Neural Coding of Visual Objects. Coding in the Auditory System. Coding in the Whisker Sensory System. Neural Coding in the Olfactory System. Coding across Sensory Modalities: Integrating the Dynamic Face with the Voice. Population Coding by Place Cells and Grid Cells. Coding of Movement Intentions. Neural Coding of Short-Term Memory. Role of Temporal Spike Patterns in Neural Codes. Adaptation and Sensory Coding. Sparse and Explicit Neural Coding. Information Coding by Cortical Populations. Information Content of Local Field Potentials: Experiments and Models. Principles of Neural Coding from EEG Signals. Gamma-Band Synchronization and Information Transmission. Decoding Information from fMRI Signals. Section III Theoretical and In Silico Approaches. Dynamics of Neural Networks. Learning and Coding in Neural Networks. Ising Models for Inferring Network Structure from Spike Data. Vocal Learning with Inverse Models. Computational Models of Visual Object Recognition. Coding in Neuromorphic VLSI Networks. Open-Source Software for Studying Neural Codes. Index.
