The In-Silico Neocortical Microcircuit: From Structure to Dynamics

The neocortex is one of the most important brain regions, occupying more than 80% of the brain volume in mammals and contributing massively to its capability of perceiving and interpreting then environment. While it has been extensively studied, it is still largely unclear how it accomplishes the enormous computational tasks it is faced with. A number of structural features are conserved across the cortex, such as its vertical organization in six individual layers with a large number of morphologically unique neuron types. The exceptional horizontal uniformity, lead to the hypothesis of horizontally repeated unitary microcircuits, the idea that the cortex consists of a number of relatively uniform horizontally repeated modules, each with their own function in the concert of neural activity. While a rough mapping of function to cortical region is virtually identical between individuals and very similar between species, the extraordinary plasticity of the cortex allows functions of damaged parts to be taken over by neighboring regions. This indicates that flexible, "reprogrammable" modules implement the functions of the cortex. The Blue Brain Project (BBP) aims to build a facility for the collection and integration of available data on the fundamental structure and operating principles of the cortex to build and continuously refine a detailed, bottom-up model of the unitary microcircuit. It can then be used as a building block for the construction and simulation of larger cortical regions. A bottom-upmodel such as the one of the BBP aims to match the object being represented primarily on an underlying physical level. As such it is guaranteed to be anchored in real world physical constraints, this however comes with the disadvantage that on a higher level the behavior of the model is undetermined. The goal of this thesis is to study and characterize the model to learnmore about its function and what determines it. In order to ensure the biological relevance, this endeavour was to be tightly linked with comparisons to and validations against relevant experimental data. I concluded that the simulation of extracellular electrical signals are the right tool to link characterization and validation, due to their great importance in in-vitro work. I authored several improvements to the model to ensure the biological accuracy of the computed extracellular signals, the most important being the derivation of the local connectome inside the modeled microcircuit. Next, I implemented software for the calculation of extracellular signals during the course of a simulation on a BlueGene/P supercomputer. The method for the calculation is flexible and can discern between the contributions of individual cells. In addition to validating the signal against in-vitro data, this allowed us to analyze the composition of the signals in a way that is impossible outside of a simulation. The method revealed the crucial role of active currents in the local field potential, i.e. the relatively slow deflections in extracellular signals. We also extensively analyzed and quantified the varying contributions of individual cell types and membrane mechanisms to extracellular signals at different frequencies.

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