Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory.
The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book’s Web site.1
Abstract
This book was given to me by a family member, it is quite long but contains a lot of the important theoretical concepts within neuroscience, and with a heavy emphasis on the Mathematics side of the field.
I look forward to reading it, will be breaking it up into chapters and their corresponding sub chapters as folders.
Chapters
Chapter 1 - Firing Rates and Spike Statistics
This serves as an introduction to the biophysical properties of neurons and how these characteristics form Action Potentials.
Extra Resources
I found this github repo that, in addition to containing a pdf of the book, also has some useful python scripts regarding working with neuron firings and whatnot.
Footnotes
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Description from amazon page ↩