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Published August 9, 2022 | Version 1.0
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A Model of Rat Non-barrel Somatosensory Cortex Anatomy


We present a data-driven computational model of the anatomy of non-barrel primary somatosensory cortex of juvenile rat. The modeling process is based on a previously established workflow for a single cortical column, but is extended here to build a much larger circuit in an atlas-based geometry. Neurons in the model belong to 60 different morphological types and are connected by synapses placed by two established algorithms, one modeling local connectivity determined by axo-dendritic overlap, and one for long-range connectivity between sub-regions. Long-range connectivity is defined with topographic mapping and laminar connectivity profiles, providing intrinsic feed-forward and feedback pathways. Additionally, we incorporate core- and matrix-type thalamocortical projection systems, associated with VPM and POm thalamic nuclei respectively, that enable extrinsic input.

The model comprises 211712 neurons in the front limb and jaw subregions and the dysgranular zone of the Paxinos & Watson rat brain atlas, scaled down to juvenile size. It is available in the open SONATA standard and contains neuron locations and their properties (such as morphological types, cortical layer, etc.), their detailed morphologies, and synaptic connectivity associated with all systems described above. Modeled synapses are associated with their exact location in the dendritic tree, and additional anatomical parameters, such as spine length (where biologically plausible). Extrinsic synaptic connections from neurons in the remainder of non-barrel somatosensory cortex and thalamic inputs are also contained.

Note that this is an anatomical model: Parameters and files related to neuronal and synaptic physiology can be found in our release of the physiological model.

Please refer to the documentation of the SONATA format for information how to load and analyze the model. A jupyter notebook has been included with basic examples of how to load the data using our open-source packages NeuroM and Blue Brain SNAP.

This study was supported by funding to the Blue Brain Project, a research center of the Ecole polytechnique federale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology. RL, JPS and JL were supported by EPSRC under grant number EP/P025072/1. RL was supported by a collaboration grant from EPFL.


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