A rigorous, unusually lucid textbook on neural dynamics that earns its authority by doing the mathematics properly, though it is not as neutral, or as complete, as its reputation might suggest.
I read Neuronal Dynamics with considerable respect, and also with the irritation that any serious scientist feels when a book is so competent it tempts one to forgive its blind spots. Gerstner and his co-authors have produced something rare in computational neuroscience: a textbook that does not treat mathematics as decorative scaffolding, but as the actual instrument of thought. The treatment of integrate-and-fire neurons, spike trains, adaptation, and synaptic plasticity is crisp, methodical, and in many places exemplary. When the book explains a model, it usually makes clear what that model can and cannot say, which is more intellectually honest than most texts in the field.
Who should read this
I would recommend this to graduate students, advanced undergraduates, and researchers who want a disciplined entry into spiking models and neural dynamics. It is especially valuable for readers who prefer derivation and conceptual clarity to qualitative hand-waving.
A personal note from Hoffmann
I trust this book more than I enjoy it, which is not faint praise. It is one of the better computational neuroscience texts I know, but its strength lies in precision rather than breadth, and I never quite forget what it leaves out.
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