Dr. Marcel Oberlaender
Organization: Research Center caesar
Phone number: +49 (0)228 9656-380
Department: In Silico Brain Sciences
Area: CIN Members (Alumni)
Scientific topic: Simulation of signal flow within 3D reconstructions of anatomically realistic neural network models
Field of Research
A cortical column is thought to represent the elementary functional unit of the mammalian cortex. In rodent vibrissal cortex, an anatomical equivalent, designated as a “barrel column”, has been described. The well-defined anatomical layout and the one-to-one correspondence between a single facial whisker and a barrel column render the vibrissal system as the ideal starting point to reverse engineer the structure and function of neural microcircuits. Our research focuses on developing imaging, image processing and analysis tools that allow (i) obtaining the 3D anatomical data that is necessary to reconstruct neural circuits, (ii) assembling average, anatomically realistic neural networks and (iii) simulating signal flow within the resulting large-scale, full-compartmental models of thousands of neurons and millions of synaptic contacts.
First, we developed a semi-automated imaging and tracing pipeline, called NeuroMorph, which allows reconstructing the complete 3D dendrite and axon morphology of individual neurons, labeled in vivo [6,8,10,11,14]. Compared to state-of-the-art manual tracing tools it reduces the reconstruction time from several months to approximately three days. More importantly, the results are independent of the experience and performance of a human tracer.
Using NeuroMorph, we reconstructed morphologies from all excitatory cell types in rat vibrissal cortex and thalamus [1,2,5]. Second, we developed an image processing pipeline, called NeuroCount, which allows automated detecting of neuron somata within large brain volumes [4,9]. Using NeuroCount, we determined the number and 3D distribution of all neurons within rat vibrissal cortex and thalamus . Third, we developed an interactive, visual-computing tool, called NeuroNet, which allows assembling average 3D network models that are based on anatomical data obtained by NeuroMorph and NeuroCount [3,12]. NeuroNet allowed reconstructing the cell type-specific excitatory network of a rat barrel column and estimating synaptic innervation of each excitatory neuron by the thalamus . Finally, we developed a concept to investigate signal flow within average network models using Monte Carlo simulations. The resultant simulation framework, called NeuroDUNE, was designed and implemented by Dr. Stefan Lang as part of a Bernstein collaboration with the University of Heidelberg. Using NeuroDUNE, we simulated the activation of an excitatory network in a barrel column by thalamocortical input after whisker deflection .
In summary, the described concepts and tools open the possibility to (i) reconstruct the average 3D structure and synaptic wiring of large brain structures, (ii) simulate signal flow within anatomically realistic network models, (iii) compare simulations with functional imaging data and (iv) elucidate mechanistic principles underlying sensory-evoked signal flow.