Computational Neuroscience

What is Computational Neuroscience?

Computational neuroscience is used to research how nervous systems process their information. The computer-aided simulation of the nervous system and a lifelike representation of processes in the brain form the basis of computational neuroscience. The processing of sensory impressions is observed.

Neuro-researchers develop mathematical models based on experimentally obtained data, which are finally simulated by neuronal functions on the computer. In the process, predictions of models for neuronal behaviour are experimentally tested and optimised.

Many innovative technologies already benefit from research successes in this field. Knowledge about brain functions can now be used to design intelligent technical aids, such as driver assistance systems, self-learning computers, Robot and also intelligent prostheses. A central goal is that computer models help to recognise and heal brain malfunctions and the causes of diseases at an early stage. Therapeutic approaches can be tested virtually with the help of computational neuroscience and thus help in the continuous further development of real therapies and studies.

What models and foundations does computational neuroscience rely on?

The foundation of computational neuroscience builds on the further development of Artificial intelligence and the model of the artificial neural networks. Artificial neural networks (ANNs) can be seen as mathematical replicas of stimulus processing in the brain. These replicas are interconnected artificial neurons. Instead of electrical or chemical signals from the biological systems, algorithms with numerical values are now processed.

This system is based, for example, on machine vision on. The mathematical modelling is derived from the findings of the Neurosciencebiophysics and the theory of dynamic and complex systems. Because of their complexity, such models can usually only be simulated with the aid of computers. Experimental data often form the basis of these calculations, such as the electrophysiological properties of nerve cells and synapses and the network structures in real nerve networks.

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