Theoretical Neuroscience
by Peter Dayan, L. F. Abbott · Peter Dayan
The standard graduate text on computational modeling of the brain, covering neural encoding and decoding, information theory, biophysical and network models, and synaptic plasticity. Requires calculus and linear algebra; gives you the mathematical vocabulary used across the field.
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More resources on Computational Neuroscience
NeuronIF
Yale-developed simulation environment by Michael Hines and Ted Carnevale for building biophysically detailed models of single neurons and networks, scripted in Python or hoc. Includes documentation and tutorials for simulating membrane potentials, ion channel kinetics, and synaptic input.
3Blue1Brown - Neural Networks series
Grant Sanderson's 3Blue1Brown channel offers visually intuitive and mathematically rigorous explanations of complex topics. His series on neural networks is particularly praised for making the core concepts, like backpropagation, understandable through animated visuals.
neuromatch.io
Neuromatch.io is the hub for Neuromatch’s online neuroscience education and community resources, offering free materials for learning computational neuroscience—courses, lectures, tutorials, and code notebooks—along with information about events and the Neuromatch Academy.
Synapses, Neurons and Brains
Hebrew University of Jerusalem course on how synapses, neurons and neural networks compute, covering synaptic and dendritic integration, the plasticity underlying learning and memory, and large-scale cortical simulation like the Blue Brain Project. Learners understand neurons as computational elements and debates in brain research.
Introduction to Neural Computation (MIT 9.40)
Quantitative models of brain function: mathematical descriptions of neurons, responses to sensory stimuli, simple networks, statistical inference and decision making, plus analysis tools such as convolution, spectral analysis and principal components. Includes 20 lecture videos, slides and problem sets.
Brian2 Simulator
A Python library for simulating spiking neural networks, where neuron and synapse models are written directly as differential equations in strings and compiled to C++. The documentation includes tutorials that take you from a single integrate-and-fire neuron to networks.