Computational modeling of electric brain signals across scales: From the spikes of single neurons to the local field potentials of brain areas

Abstract

Our thoughts, feelings, memories, and behavior all arise from the actions of neurons in our brain. Meticuluous scientific research over the last few decades has given us a fairly good understanding of how single neurons work, but we know comparatively little about how thousands to billions of neurons work in concert to produce our inner life. To improve our understanding of how many neurons behave together in networks, experimental neuroscientists have developed methods and tools to record the electrical activity of up to thousands of neurons at the same time. However, inferring neural activity from these electric signals can often be challenging. In this thesis, we have developed a large-scale computer model of a part of the mouse brain – the primary visual cortex. This model can be used to simulate the neural activity and the electric signals measured in experiments, and enables us to investigate how the activity of single neurons and populations of neurons generate the electric signatures we observe. This can enhance the insight into brain circuits and neural mechanisms gained from experimental measurements.