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Reverse-engineering sensory-evoked signal flow in rat barrel cortex

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Egger,  R
Former Research Group Computational Neuroanatomy, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Kerr,  JN
Former Research Group Network Imaging, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Research Group Neural Population Imaging, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84910

Oberlaender,  M
Former Research Group Computational Neuroanatomy, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Egger, R., Schmitt, A., Dercksen, V., De Kock, C., Kerr, J., & Oberlaender, M. (2013). Reverse-engineering sensory-evoked signal flow in rat barrel cortex. Poster presented at 43rd Annual Meeting of the Society for Neuroscience (Neuroscience 2013), San Diego, CA, USA.


Cite as: http://hdl.handle.net/21.11116/0000-0001-512B-4
Abstract
We present a novel reverse-engineering approach that allows investigating sensory-evoked signal flow through individual neurons within the context of their surrounding neural networks. To do so, spontaneous and sensory-evoked activity is recorded from individual neurons in vivo. In addition, the complete 3D dendrite and axon projection patterns of these neurons are reconstructed and registered into an anatomically realistic model of rat barrel cortex. This model allows estimating the number and cell type-specific subcellular distribution of synapses on these neurons. Next, the neurons are “wired” into the network by connecting the synapses to presynaptic neurons based on cell type-specific connection probabilities. The number of functional synapses on this neuron is determined by including measurements of cell type-specific ongoing and sensory-evoked spiking probabilities for all presynaptic cell types. Finally, this neuron is turned into a compartmental model and constrained by comparing model responses to ongoing and sensory-evoked synaptic inputs to in vivo measured responses. For the first time, this allows investigating in vivo measured sensory-evoked responses of single neurons in anatomically realistic computer models, complementing previous biophysically detailed models of single neurons based on in vitro experiments. To investigate the mechanistic principles underlying sensory responses in different cell types, we use a Monte Carlo method for sampling the parameter space of the network-embedded neuron model. For example, by varying the functional connectivity or timing of sensory input, we identify model configurations that match the in vivo observed responses of these neurons. As a first demonstration of the feasibility of this approach, we investigate sensory-evoked responses in two different pathways in rat barrel cortex. First, based on axonal innervation, we find that Layer 1 inhibitory interneurons may form synapses with the apical tuft dendrites of Layer 2 (L2) neurons. Our model and experiments show that these inhibitory synapses may serve to reduce the trial-to-trial variability of the sensory-evoked PSP in L2 neurons located in surrounding columns after deflection of the principal whisker. Second, we investigate thalamocortical activation of Layer 4 (L4) neurons after passive whisker touch. Our model shows that this cell type is preferentially activated by synchronous thalamic input from VPM and ongoing intracortical activity, which together create an NMDA receptor-mediated global depolarization that leads to spiking output of this cell type.