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Journal Article

Non-linear blend coding in the moth antennal lobe emerges from random glomerular networks

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Olsson,  Shannon
Department of Evolutionary Neuroethology, Prof. B. S. Hansson, MPI for Chemical Ecology, Max Planck Society;
Research Group Dr. S. Olsson, iCHEM, Department of Evolutionary Neuroethology, Prof. B. S. Hansson, MPI for Chemical Ecology, Max Planck Society;

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Kuebler,  Linda
Department of Evolutionary Neuroethology, Prof. B. S. Hansson, MPI for Chemical Ecology, Max Planck Society;
IMPRS on Ecological Interactions, MPI for Chemical Ecology, Max Planck Society;

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Hansson,  Bill
Department of Evolutionary Neuroethology, Prof. B. S. Hansson, MPI for Chemical Ecology, Max Planck Society;

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Citation

Capurro, A., Baroni, F., Olsson, S., Kuebler, L., Karout, S., Hansson, B., et al. (2012). Non-linear blend coding in the moth antennal lobe emerges from random glomerular networks. Frontiers in Neuroengineering, 5: 6. doi:10.3389/fneng.2012.00006.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-5A68-D
Abstract
Neural responses to odor blends often exhibit non-linear interactions to blend components. The first olfactory processing center in insects, the antennal lobe (AL), exhibits a complex network connectivity. We attempt to determine if non-linear blend interactions can arise purely as a function of the AL network connectivity itself, without necessitating additional factors such as competitive ligand binding at the periphery or intrinsic cellular properties. To assess this, we compared blend interactions among responses from single neurons recorded intracellularly in the AL of the moth Manduca sexta with those generated using a population-based computational model constructed from the morphologically based connectivity pattern of projection neurons (PNs) and local interneurons (LNs) with randomized connection probabilities from which we excluded detailed intrinsic neuronal properties. The model accurately predicted most of the proportions of blend interaction types observed in the physiological data. Our simulations also indicate that input from LNs is important in establishing both the type of blend interaction and the nature of the neuronal response (excitation or inhibition) exhibited by AL neurons. For LNs, the only input that significantly impacted the blend interaction type was received from other LNs, while for PNs the input from olfactory sensory neurons and other PNs contributed agonistically with the LN input to shape the AL output. Our results demonstrate that non-linear blend interactions can be a natural consequence of AL connectivity, and highlight the importance of lateral inhibition as a key feature of blend coding to be addressed in future experimental and computational studies.