Computational Neuroscience

Scope of the method

The Method relates to
  • Human health
The Method is situated in
  • Basic Research
  • Translational - Applied Research
Type of method
  • In silico
Specify the type of cells/tissues/organs
Animal and Human Neurons


Method keywords
  • computational models
  • in silico
  • Data analysis
  • prediction models
  • Neurons
Scientific area keywords
  • computational modelling
  • neuroscience
  • bioengineering
  • Biomedical Engineering
Method description

Computational neuroscience aims to study the nervous system by mathematical and computer simulations. Computational models can be built on multilevel scales. With the bottom-up approach, the model is built from the same building blocks as observed in human or animal tissue. As such, the functioning of a neuron depends on the behavior of its ion channels. Subsequently, these neurons are connected via synapses to obtain the network response. Ion channels and synapses are mathematically represented by a set of differential equations, modeled after the Hodgkin-and-Huxley formalism. The models enable simple and systematic parameter manipulation of often experimentally inaccessible aspects. They can therefore be used to test hypotheses concerning underlying mechanisms of neurological diseases on multilevel scale. New hypotheses can be formulated as well. The obtained results can be used to design more delineated in-vivo I in-vitro experiments, reducing the amount of experiments needed. Finally, they are an ideal tool to gain more insights in the effect of treatment strategies and provide improved treatment protocols.

Lab equipment
  • - High performance cluster
  • - NEURON simulation software (open source)
Method status
  • Still in development
  • Published in peer reviewed journal

Pros, cons & Future potential

  • - Systematic research
  • - Cost reduction
  • - Reduction in-vivo and in-vitro experiments
  • - Improved understanding of underlying mechanisms
  • - Generalization of sparse data *Treatment optimization
  • - Validation of model assumptions / predictions / results
  • - Animal's in-silico model to human outcome translation
  • - Computational complexity / cost
  • - Simplified
  • - Intersubject variations
  • Increased computational power and better experimental methods will result in more accurate models.
Future & Other applications
  • - Optimization of treatments for neurological diseases,
  • - Improved understanding of pathologies of nervous system diseases.

References, associated documents and other information

  • - Carnevale, N. T., & Hines, M. L. (2006). The NEURON book. Cambridge University Press. - Fan, X., & Markram, H. (2019). A brief history of simulation neuroscience. Frontiers in neuroinformatics, 13, 32.
  • - Tarnaud, T., Joseph, W., Schoeters, R., Martens, L., & Tanghe, E. (2021). Improved alpha-beta power reduction via combined electrical and ultrasonic stimulation in a parkinsonian cortex-basal ganglia-thalamus computational model. Journal of Neural Engineering, 18(6), 066043.


Ghent University (UGent)
Information Technology
Flemish Region