Simulating neuronal networks - basics and applications

The field of computational neuroscience has received a substantial boost during the last two decades. Based on experimental findings, scientists investigate the properties of neural networks both by analytical and numerical approaches. Simulations play a major role in this field and recent advances in simulation technology make it possible to simulate networks of more than 109 neurons. The basis of neuronal simulations is the reduction of biological neurons and synapses to one to few differential equations mimicking the behavior in diverse situations. The talk will introduce basic concepts of neuronal simulations and some of the most common models used to emulate neurons and synapses. A multitude of software tools with different focuses and strengths is being used for neuronal simulations. One of the most popular tools is NEST [1], which specializes in the simulation of small to large networks of point neurons, negecting the spatial structure of the cells. I will introduce the tool and show some basic examples. As a complex application of NEST, I will introduce our project about a multi-area model, which is designed to capture the dynamical properties of the vision-related cortical areas of a macaque monkey