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 metabolic efficiency of neurons, with special reference to visual perception. Evidence from a diverse range of research papers is used to show how information theory defines absolute limits on neural efficiency; limits which ultimately determine the neuroanatomical microstructure of the eye and brain. Written in an informal style, with a comprehensive glossary, tutorial appendices, explainer boxes, and a list of annotated Further Readings, this book is an ideal introduction to cutting-edge research in neural information theory.
| Autorius: | James V Stone |
| Serija: | Tutorial Introductions |
| Leidėjas: | Tutorial Introductions |
| Išleidimo metai: | 2018 |
| Knygos puslapių skaičius: | 214 |
| ISBN-10: | 0993367925 |
| ISBN-13: | 9780993367922 |
| Formatas: | Knyga minkštu viršeliu |
| Kalba: | Anglų |
| Žanras: | Information architecture |
Parašykite atsiliepimą apie „Principles of Neural Information Theory: Computational Neuroscience and Metabolic Efficiency“
