Tuomo Mäki-Marttunen joins us for a special journal club session this week.
The recent progress in electrophysiological and optical methods for neuronal recordings provides vast amounts of high-resolution data. In parallel, the development of computer technology has allowed simulating ever larger neuronal circuits. A challenge in taking advantage of these developments is the construction of single-cell and network models in a way that faithfully reproduces neuronal biophysics with subcellular level of details while keeping the simulation costs at an acceptable level. In this talk, I will present our work on a stepwise method for fitting a neuron model to data with fine spatial resolution, such as that achievable with voltage sensitive dyes (VSDs) and Ca2+ imaging.
We applied our method to simulated data from layer 5 pyramidal cells (L5PCs) and constructed a model with reduced neuronal morphology. We connected the reduced-morphology neurons into a network and validated against simulated data from a high-resolution L5PC network model. The reduced-morphology neuron model obtained using our approach reliably reproduced the membrane potential dynamics across the dendrites as predicted by the full-morphology model, and was more than 20 times faster to simulate. The network models produced using our method are cost-efficient and predict that interconnected L5PCs, largely due to the medium afterhyperpolarization mediated by the Ca2+-activated SK current, are able to amplify delta-range oscillatory inputs across a large range of network sizes and topologies.