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Intermediate Topic 32 of 51 Emerging & Frontier Topics

Computational Neuroscience

Modeling brains with computation.

How it works

Computational neuroscience builds mathematical and computer models of how neurons and brains process information. Single-neuron models range from the simple integrate-and-fire neuron, which accumulates input and fires at a threshold, to the Hodgkin-Huxley equations describing ion channels. Network models study spiking activity, synaptic plasticity such as Hebbian learning and spike-timing-dependent plasticity, and circuits for vision, memory and decision making. Methods include differential equations, information theory, Bayesian models of perception and large simulations with tools such as NEURON and Brian. Data come from electrodes, imaging and connectomics.

Where it is used

Researchers use models to test theories, understand disease such as epilepsy, design brain-machine interfaces and inspire artificial neural networks.

Common pitfall

More detailed models are not necessarily better, many parameters are poorly measured and artificial networks differ biologically from real brains. State assumptions, compare with data and choose the right level of abstraction.

Where it fits in the course

Computational Neuroscience is topic 32 of 51 in Emerging & Frontier Topics, rated Intermediate. It follows Sequence Alignment. The next topic is Neuromorphic Computing.

Practice and explore

How to study this topic

  1. Read the explanation above and restate the definition in your own words.
  2. Open the interactive lesson and answer its mini quiz.
  3. Read the common pitfall, then write a short example that avoids it.
  4. Check your understanding in the Emerging & Frontier Topics quiz, and revisit the topic if you miss a question.

Frequently asked questions

What is Computational Neuroscience?
Modeling brains with computation. It belongs to Emerging & Frontier Topics and is an intermediate-level topic.
Where do people use Computational Neuroscience?
Researchers use models to test theories, understand disease such as epilepsy, design brain-machine interfaces and inspire artificial neural networks.
What is the most common mistake with Computational Neuroscience?
More detailed models are not necessarily better, many parameters are poorly measured and artificial networks differ biologically from real brains.
What should I learn before this topic?
Start with Sequence Alignment, the topic just before it in Emerging & Frontier Topics.
How can I practice it?
Answer the mini quiz in the interactive lesson, then take the Emerging & Frontier Topics quiz.
Is Computational Neuroscience hard to learn?
It is an Intermediate topic. Be comfortable with the Beginner topics in this area first, then read the common pitfall carefully, since it is where most mistakes happen.
How long does it take to learn Computational Neuroscience?
Most learners can read the explanation and finish the interactive lesson in under an hour. Fluency comes from applying the idea, so plan a few short practice sessions with the quiz.
Which topics are related to Computational Neuroscience?
Sequence Alignment, Neuromorphic Computing, Bioinformatics are closely connected. The full list is in the related topics section of this page.
What should I study after Computational Neuroscience?
Continue with Neuromorphic Computing, the next topic in Emerging & Frontier Topics.

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