Dense Statistical Connectome of Rat Barrel Cortex

Abstract

Synaptic connectivity is one important constrain for cortical signal flow and function. Consequently, a complete synaptic connectivity map (i.e., connectome) of a cortical area across spatial scales would advance our understanding of cortex organization and function. We present a dense statistical connectome of the entire rat vibrissal cortex based on measured 3D distributions of axons/dendrites/somata of excitatory and inhibitory neurons. By calculating the structural overlap between pre- and postsynaptic cells our model provides quantitative estimates on connectivity measurements like connection probability and number of synapses on cell type, cellular, and subcellular levels. We found that our model reproduces connectivity measurements between thalamic and excitatory/inhibitory neurons reported in paired recordings and light- and electron-microscopic studies. Similarly, intracortical synaptic connectivity of our model matches most connectivity measurements. However, the location and distance between pre- and postsynaptic cells and - in case of slicing experiments - the degree of truncation strongly influences the connectivity. When reproducing electronmicroscopic and in vitro slicing experiments in our model, we found that measurements obtained under the respective experimental conditions are in line with our model's results, but represent only a small fraction of the underlying distribution. The experimental conditions such as the small volume analyzed in electron-microscopic studies or the truncation of morphologies thus biases the conclusions that are drawn, e.g. an underestimation of the connection probability. Our approach can therefore be used to improve experimental design and seen as a starting point to simulate sensory-evoked signal flow and investigate structural and functional organization of the cortex