Stone J. Principles of Neural Information Theory.Computational Neuroscience 2018

Download this torrent!

Stone J. Principles of Neural Information Theory.Computational Neuroscience 2018

Category: Other
Total size: 9.71 MB
Added: 2025-03-10 23:38:56

Share ratio: 7 seeders, 4 leechers
Info Hash: 4FD88E02AF5B0009CAAA5A17EF72A529635B85C8
Last updated: 1.7 days ago

Textbook in PDF format The brain is the most complex computational machine known to science, even though its components (neurons) are slow and unreliable compared to a laptop computer. In this richly illustrated book, Shannon's mathematical theory of information is used to explore the computational efficiency of neurons, with special reference to visual perception and the efficient coding hypothesis. Evidence from a diverse range of research papers is used to show how information theory defines absolute limits on neural processing; limits which ultimately determine the neuroanatomical microstructure of the eye and brain. Written in an informal style, with a comprehensive glossary, tutorial appendices, and a list of annotated Further Readings, this book is an ideal introduction to the principles of neural information theory. In the Light of Evolution Introduction All That We See In the Light of Evolution In Search of General Principles Information Theory and Biology An Overview of Chapters Information Theory Introduction Finding a Route, Bit by Bit Information and Entropy Maximum Entropy Distributions Channel Capacity Mutual Information The Gaussian Channel Fourier Analysis Summary Measuring Neural Information Introduction The Neuron Why Spikes? Neural Information Gaussian Firing Rates Information About What? Does Timing Precision Matter? Rate Codes and Timing Codes Summary Pricing Neural Information Introduction The Efficiency-Rate Trade-Off Paying with Spikes Paying with Hardware Paying with Power Optimal Axon Diameter Optimal Distribution of Axon Diameters Axon Diameter and Spike Speed Optimal Mean Firing Rate Optimal Distribution of Firing Rates Optimal Synaptic Conductance Summary Encoding Colour Introduction The Eye How Aftereffects Occur The Problem with Colour A Neural Encoding Strategy Encoding Colour Why Aftereffects Occur Measuring Mutual Information Maximising Mutual Information Principal Component Analysis PCA and Mutual Information Evidence for Efficiency Summary Encoding Time Introduction Linear Models Neurons and Wine Glasses The LNP Model Estimating LNP Parameters The Predictive Coding Model Estimating Predictive Coding Parameters Evidence for Predictive Coding Summary Encoding Space Introduction Spatial Frequency Do Ganglion Cells Decorrelate Images? Optimal Receptive Fields: Overview Receptive Fields and Information Measuring Mutual Information Maximising Mutual Information van Hateren’s Model Predictive Coding of Images Evidence for Predictive Coding Is Receptive Field Spacing Optimal? Summary Encoding Visual Contrast Introduction The Compound Eye Not Wasting Capacity Measuring the Eye’s Response Maximum Entropy Encoding Evidence for Maximum Entropy Coding Summary The Neural Rubicon Introduction The Darwinian Cost of Efficiency Crossing the Neural Rubicon Further Reading Appendices Glossary Mathematical Symbols Correlation and Independence A Vector Matrix Tutorial Neural Information Methods Key Equations References Index