Summary
Uses mathematical and computer models to explain how neurons and networks process information.
In depth
Models range from single neurons (integrate-and-fire) to networks and Bayesian brain theories. Artificial neural networks were inspired by neurons; deep networks are used to model visual cortex.
Models range from biophysical to abstract. They generate predictions and test ideas about coding and learning.
Example: neural networks model visual recognition.
Levels: biophysical models (Hodgkin-Huxley, cable theory), network models (attractor networks for memory, balanced excitation-inhibition for irregular firing), and normative or algorithmic models (efficient coding, Bayesian inference, reinforcement learning with temporal difference errors that match dopamine signals). Hebbian learning, Hopfield networks and later backpropagation inspired artificial neural networks, which in turn are used as models of cortex (deep networks predict neural responses in the visual system).
Large-scale projects include the Blue Brain Project and Human Brain Project, with debate over their value. Data analysis methods (dimensionality reduction, decoding, spike sorting) are central. Theories like predictive coding and the free energy principle propose that the brain minimises prediction error. Neuromorphic engineering builds brain-inspired chips.
Frequently asked questions
What is Computational Neuroscience?
Uses mathematical and computer models to explain how neurons and networks process information.
Which area of biology covers Computational Neuroscience, and how advanced is it?
Computational Neuroscience is one of the 54 topics in Neuroscience. It is rated Advanced, so it suits learners who want specialist depth. Related topics in this area include Hodgkin-Huxley Model, Nervous System, Neural Development.
Why does Computational Neuroscience matter?
Models formalise ideas, predict outcomes and guide experiments.
Can you give an example of Computational Neuroscience?
Reinforcement learning models explain dopamine signals as prediction errors.
What should I learn before Computational Neuroscience?
Work through the Beginner topics in Neuroscience first, such as Nervous System, Neuron, Central and Peripheral Nervous Systems, then return to Computational Neuroscience. Advanced topics assume the Intermediate vocabulary.
What topics are related to Computational Neuroscience?
The closest topics to Computational Neuroscience are Hodgkin-Huxley Model, Nervous System, Neural Development, Consciousness Research. Comparing them side by side is a good way to see how the ideas connect.
How can I check that I understand Computational Neuroscience?
Read the summary and the in-depth section, then use the quick check on the interactive lesson for Computational Neuroscience. To practise the whole of Neuroscience, use the Neuroscience quiz.
How is Computational Neuroscience different from Hodgkin-Huxley Model?
Computational Neuroscience: Uses mathematical and computer models to explain how neurons and networks process information. Hodgkin-Huxley Model: A set of equations describing how sodium and potassium conductances produce the action potential. Read both side by side and note what each one explains.
How should I study Computational Neuroscience step by step?
First, read the summary and write the definition in your own words. Second, read the in-depth section and note any example. Third, compare it with Hodgkin-Huxley Model and explain the difference aloud. Finally, take the Neuroscience quiz and come back to this page for any question you miss.