--- title: Computational Neuroscience description: This field uses mathematical models, computer simulations, and theoretical analysis to study brain function. Learners will understand how neural circuits process information, represent sensory data, and direct behavior. category: social-sciences subcategory: psychology difficulty: beginner, intermediate, advanced url: /subject/computational-neuroscience --- # Computational Neuroscience This field uses mathematical models, computer simulations, and theoretical analysis to study brain function. Learners will understand how neural circuits process information, represent sensory data, and direct behavior. ## Available Resources 1 Videos • 2 Books • 4 Courses • 6 Websites ## Websites ### 1. 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. **Difficulty:** Beginner | **Price:** Free **Link:** https://www.neuron.yale.edu/neuron/ **Tags:** neuron-simulator, computational-neuroscience, compartmental-modeling, ion-channels, neural-simulation ### 2. 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. **Difficulty:** Beginner | **Price:** Free **Link:** https://brian2.readthedocs.io/ **Tags:** spiking-neural-networks, simulation, python, neuron-models, computational-neuroscience ### 3. 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. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://neuromatch.io **Tags:** websites, humanities-social-sciences, psychology ### 4. NeuroMorpho.Org Open-access database of digitally reconstructed neuron morphologies from many species, brain regions and cell types, with downloadable SWC files, standardized metadata and online 3D viewing. Researchers and students can compare dendritic and axonal structure and reuse reconstructions for analysis or modeling. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://neuromorpho.org **Tags:** neuron-morphology, neuronal-reconstruction, dendrites, neuroscience-databases, neuroanatomy ### 5. modeldb.yale.edu ModelDB is a curated online repository of computational neuroscience models and simulations, where researchers share neuron and network models, parameters, and runnable code. It provides downloadable model files (e.g., NEURON, GENESIS) along with metadata to facilitate reuse, replication, and exploration of neural circuits. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://modeldb.yale.edu **Tags:** websites, humanities-social-sciences, psychology ### 6. International Neuroinformatics Coordinating Facility (INCF) Site of the International Neuroinformatics Coordinating Facility, which endorses standards and best practices for sharing neuroscience data, models and software, and runs the TrainingSpace platform, helping researchers adopt FAIR data practices and find vetted neuroinformatics tools and training. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://incf.org **Tags:** neuroinformatics, fair-data, data-standards, open-science, computational-neuroscience ## Courses ### 1. Introduction to Neural Computation (MIT 9.40) **Author:** Michale Fee, Daniel Zysman 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. **Difficulty:** Intermediate | **Price:** Free **Link:** https://ocw.mit.edu/courses/9-40-introduction-to-neural-computation-spring-2018/ **Tags:** computational-neuroscience, neural-coding, hodgkin-huxley, spectral-analysis, principal-component-analysis ### 2. Computational Neuroscience **Author:** Adrienne Fairhall, Rajesh Rao University of Washington course on how neurons and networks represent and process information, covering neural encoding and decoding, information theory, single-neuron biophysical models, synaptic plasticity and network learning. Learners can build and interpret quantitative models of neural computation through programming exercises. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://www.coursera.org/learn/computational-neuroscience **Tags:** computational-neuroscience, neural-coding, information-theory, neuron-models, synaptic-plasticity ### 3. Synapses, Neurons and Brains **Author:** Idan Segev 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. **Difficulty:** Beginner | **Language:** English | **Duration:** 16 hours | **Price:** Free **Link:** https://www.coursera.org/learn/synapses **Tags:** neurons, synapses, synaptic-plasticity, neural-computation, brain-simulation ### 4. Neuronal Dynamics: Computational Neuroscience of Single Neurons **Author:** Wulfram Gerstner EPFL graduate-level course on mathematical models of single neurons, from Hodgkin-Huxley and integrate-and-fire models to dendrites, synapses, noise and spike-train coding, preparing learners to analyse and simulate neuronal activity with differential equations and Python exercises. **Difficulty:** Advanced | **Language:** English | **Price:** Free **Link:** https://www.edx.org/learn/neuroscience/ecole-polytechnique-federale-de-lausanne-neuronal-dynamics **Tags:** neuron-models, hodgkin-huxley, spiking-neurons, neural-coding, dynamical-systems ## Podcasts ### 1. Brain Inspired **Author:** Paul Middlebrooks Interview podcast hosted by neuroscientist Paul Middlebrooks, featuring researchers working where neuroscience meets artificial intelligence. Episodes cover computational models of the brain, deep and reinforcement learning, decision-making, neuromorphic hardware and philosophy of mind, showing how each field informs the other. **Difficulty:** Intermediate | **Language:** en-US | **Price:** Free **Link:** https://braininspired.co/series/brain-inspired/ **Tags:** computational-neuroscience, neuroai, deep-learning, reinforcement-learning, cognitive-science ## Videos ### 1. 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. **Difficulty:** Beginner | **Language:** English | **Price:** Free **Link:** https://www.youtube.com/playlist?list=PLW_xnxm7jEso **Tags:** neural-networks, backpropagation, gradient-descent, deep-learning, visual-explanation ## Books ### 1. Theoretical Neuroscience **Author:** Peter Dayan, L. F. Abbott 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. **Difficulty:** Intermediate | **Language:** English | **Price:** Paid **Link:** https://www.amazon.com/dp/0262541858?tag=edmonddante07-20 **Tags:** books, humanities-social-sciences, psychology ### 2. Dynamical Systems in Neuroscience **Author:** Eugene M. Izhikevich Applies bifurcation theory and phase-plane analysis to single neurons, explaining why cells fire as they do and classifying excitability types. Working through it, you can read a neuron model's phase portrait and predict its spiking behavior from bifurcation structure. **Difficulty:** Intermediate | **Language:** English | **Price:** Paid **Link:** https://www.amazon.com/dp/0262514206?tag=edmonddante07-20 **Tags:** books, humanities-social-sciences, psychology --- *This content is part of Dantes.io - Your Treasure Map to Knowledge* *Curated by humans at Dantes.io. Personal study use welcome; republishing this curation requires permission (team@dantes.io).* View this page online: https://dantes.io/subject/computational-neuroscience