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Reconstruction of an average cortical column “in silico”

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Oberlaender, M. (2009). Reconstruction of an average cortical column “in silico”. Talk presented at Workshop on the Mechanism of Brain and Mind: The 9th Winter Workshop 2009: Large-scale Simulations and Database in Neuroscience. Rusutsu Ski Resort, Japan. 2009-01-13 - 2009-01-15.

Cite as: https://hdl.handle.net/21.11116/0000-0003-2A18-4
The group of Prof. Dr. Bert Sakmann at the Max Planck Institute of Neurobiology in Munich aims to understand principle mechanisms that underlie decision making in the mammalian brain.
The behavioral paradigm of our work is the so called “gap-crossing” experiment 1. It is a yes-or-no (binary) decision, in which a mouse chooses whether it crosses an approximately 6 cm wide gap to reach sweet milk or not. The decision depends on whisker deflection. If the whiskers reach and touch the other side of the gap, all mice reliably cross the gap. If the gap size is too big, mice never cross. The performance is independent of the number of whiskers. A single whisker (all others are trimmed) is sufficient to reliably trigger the crossing.
Each whisker has a functional correspondence in the primary somatosensory cortex (S1) that we refer to as the cortical column. Since the deflection of a single whisker is sufficient for triggering the crossing of the gap, the detailed study of physiology and anatomy of the corresponding cortical column will most likely reveal insights to basic mechanisms of the neuronal network that encodes sensory information from a whisker 2.
Each cortical column consists of more than 10000 thousand neurons 3, 4 and multiple neuron types. So far no experimental “in vivo” approach is capable to study the function and interaction of such a large scale ensemble of neurons on a cellular basis. We believe that the detailed anatomy of the network, the morphology of its constituent neurons and the wiring between them are key prerequisites for understanding cortical information processing. We therefore reconstruct an average cortical column as a model system for “in silico” network studies.
So far our group and collaborators physiologically characterized the cell-type specific input/output balance of a column 5, 6 and developed multiple tools to gather quantitative anatomical data such as the number and distribution of neurons in a column 7, mean characteristics of neuron types, axonal projection domains 8-10 and number of synaptic contacts.
Based upon this anatomical knowledge the generated average cortical column is realized as a network of full-compartmental neuron morphologies. A newly developed powerful simulation environment 11 allows for a quantitative “in silico” investigation of such large and complex neuronal ensembles. Finally, an interactive 3D visualization and analysis tool enables the correlation of simulation results to single cell, pair or network data from “in vivo” experiments.