01 / Reading Guide
Reading Guide
Who this book is for
For advanced undergraduates and beginning graduate students in computational neuroscience or related quantitative fields. The book makes a route from individual spiking neurons to population behavior, connecting biological questions with formal models. It is useful when readers want to explain what a simulation assumes and predicts, rather than simply reproduce a plotted spike train.
Prerequisites
Differential equations, probability and elementary linear algebra are important. Basic cellular neuroscience helps with interpretation. Python is useful for the linked exercises, but mathematical understanding of the models is the central requirement.
What it covers
Single-neuron models, membrane dynamics and spike generation lead into stochastic models, coding, estimation and networks. Classical frameworks such as Hodgkin-Huxley and Hopfield are combined with generalized linear models and decision-related topics. The online contents provide a complete sequence, while linked Python exercises support numerical exploration of selected chapters.
How to use it
Begin with a simple neuron model and state which biological properties it preserves and discards. Predict the response to a small input change before running the simulation. Vary time step and random seed to separate numerical behavior from the phenomenon of interest. Compare a mechanistic model with a statistical model on the same question, noting what can be interpreted from each. Use summaries and exercises to reconstruct the reasoning, and avoid treating a successful fit as proof that the underlying biological mechanism has been identified.
Edition and sources
The complete authorized online book is hosted by EPFL and identifies Cambridge University Press copyright. Free reading is explicitly provided, but reproduction requires publisher permission; no Creative Commons license is assumed. The separate exercise software has its own distribution terms and is not interchangeable with the book’s rights.
