This version of the documentation is NOT an official release. You are reading the documentation version which is in active and ongoing development. You can change versions on the bottom left of the screen.
First steps toward brain simulation
In this course, students will learn how computer simulations are employed in brain research. Sometimes, these simulations are very close to experiments, and a viable goal may be to mimic the known biological reality as good as possible. In other cases, the motivation is rather to devise a strategy for entirely new experiments. In this case, a computer simulation is more like a thought experiment, leading to a new hypothesis how things might work. This is the promise and the potential of computational neuroscience.
In experiments with animals or humans, brain activity must be recorded with suitable advanced instruments (e.g. electrodes, optical instruments, or brain scanners). This is exactly what researchers also do in simulations. For that reason, simulated data typically looks very much like experimental data, and they have to be analyzed like experimental data. To some degree, therefore, this is also a course on neuroscientific data analysis. In contrast to experiments with biological brains, in a simulation one has access to much more detailed information about the inner workings. As a consequence, computer simulations can sometimes provide insight that goes beyond what is achieved by experiments, even with latest techniques. This sets the stage for what students can expect from this course, and what students need to do to get the most out of it.
Specifically, students learn how to …
- select a neuron model
- construct a neuronal network model
- apply a stimulus to the network
- record the activity of neurons
- run a simulation
- visualize the results
- analyze the activity data
- write protocol of a simulation