SENSATION AND PERCEPTION provided the starting points for modern research into our mental processes. In the early nineteenth century the French philosopher
Auguste Comte argued that the study of behavior should become a branch of the biological sciences and that the laws governing the mind should be derived from
objective observation. Comte's new philosophy, which he called positivism, was influenced by the British empiricists John Locke, George Berkeley, and David Hume,
who maintained that all knowledge is obtained through sensory experience—from what we see, hear, feel, taste, and smell. At birth, Locke proposed, the human mind
is a tabula rasa, a blank slate upon which experience leaves its mark.
Table of Contents
Chapter 1 – Physiological Foundations of Neural Signals
Chapter 2 – Biophysics of Extracellular Spikes
Chapter 3 – Local Field Potentials: Biophysical Origin and Analysis
Chapter 4 – Spike Sorting
Chapter 5 – Spike-Train Analysis
Chapter 6 – Synchronization Measures
Chapter 7 – Role of Correlations in Population Coding
Chapter 8 – Decoding and Information Theory in Neuroscience
Chapter 9 – Neural Coding of Visual Objects
Chapter 10 – Coding in the Auditory System
Chapter 11 – Coding in the Whisker Sensory System
Chapter 12 – Neural Coding in the Olfactory System
Chapter 13 – Coding across Sensory Modalities: Integrating the Dynamic Face with the Voice
Chapter 14 – Population Coding by Place Cells and Grid Cells
Chapter 15 – Coding of Movement Intentions
Chapter 16 – Neural Coding of Short-Term Memory
Chapter 17 – Role of Temporal Spike Patterns in Neural Codes
Chapter 18 – Adaptation and Sensory Coding
Chapter 19 – Sparse and Explicit Neural Coding
Chapter 20 – Information Coding by Cortical Populations
Chapter 21 – Information Content of Local Field Potentials: Experiments and Models
Chapter 22 – Principles of Neural Coding from EEG Signals
Chapter 23 – Gamma-Band Synchronization and Information Transmission
Chapter 24 – Decoding Information from fMRI Signals
Chapter 25 – Dynamics of Neural Networks
Chapter 26 – Learning and Coding in Neural Networks
Chapter 27 – Ising Models for Inferring Network Structure from Spike Data
Chapter 28 – Vocal Learning with Inverse Models
Chapter 29 – Computational Models of Visual Object Recognition
Chapter 30 – Coding in Neuromorphic VLSI Networks
Chapter 31 – Open-Source Software for Studying Neural Codes
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