Book recommendation for learning Computational Neuroscience
I started my academic career with a more experimental major (Pharmacy for my undergraduate and Neuropharmacology for PhD) and then got training in Computational Neuroscience. I learned a lot from diverse online and offline courses and resources. But the most important resource is the books I read (either by reading in detail or just skimming). Here I listed some books I find helpful for learning Computational Neuroscience from different backgounds:
Mathematics
Algebra
Contemporary Abstract Algebra, 6th, Joseph A. Gallian
Linear Algebra Done Right, 3rd, Sheldon Axler
Geometry
Visual Differential Geometry and Forms, Tristan Needham
Riemannian Geometry, Manfredo Perdigao Do Carmo
An Introduction to Manifolds, 2ed, Loring W. Tu
Differential Geometry of Curves and Surfaces, Kristopher Tapp
Analysis
Nonlinear Dynamics and Chaos, Steven H. Strogatz
Matrix Analysis, 2nd, Roger A. Horn, Charles R. Johnson
Visual Complex Analysis, Tristan Needham
Stochastic Prosesses in Physics and Chemistry, N.G. Van Kampen
Physics
Mathematics for Physics:An illustrated Handbook, Adam March
Entropy, Order Parameters, and Complexity, James P. Sethna
Computational Neuroscience
Neuronal Dynamics, Wulfram Gerstner, Werner M. Kistler, Richard Naud and Liam Paninski
Dynamical Systems in Neuroscience, Eugene M. Izhikevich
Neuroscience
Principles of Neuroscience, 6th, Eric Kandel, John D. Koester, Sarah H. Mack, Steven Siegelbaum
Cognitive Neuroscience, 4th, Michael S. Gazzaniga, Richard B. Ivry, George R. Mangun