Sparse coding and lateral inhibition arising from balanced and unbalanced dendrodendritic excitation and inhibition

The precise mechanism by which synaptic excitation and inhibition interact with each other in odor coding through the unique dendrodendritic synaptic microcircuits present in olfactory bulb is unknown. Here a scaled-up model of the mitral-granule cell network in the rodent olfactory bulb is used to...

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Bibliographic Details
Published inThe Journal of neuroscience Vol. 34; no. 41; pp. 13701 - 13713
Main Authors Yu, Yuguo, Migliore, Michele, Hines, Michael L, Shepherd, Gordon M
Format Journal Article
LanguageEnglish
Published United States Society for Neuroscience 08.10.2014
Subjects
Animals
Dendrites - physiology
Learning - physiology
Models, Neurological
Neural Inhibition - physiology
Neural Networks (Computer)
Neural Pathways - physiology
Neuronal Plasticity - physiology
Olfactory Bulb - cytology
Olfactory Bulb - physiology
Rats
Smell - physiology
Synapses - physiology
lateral inhibition
olfactory bulb
balanced excitation/inhibition
odor coding
sparse coding
dendrodendritic synapse
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Summary:The precise mechanism by which synaptic excitation and inhibition interact with each other in odor coding through the unique dendrodendritic synaptic microcircuits present in olfactory bulb is unknown. Here a scaled-up model of the mitral-granule cell network in the rodent olfactory bulb is used to analyze dendrodendritic processing of experimentally determined odor patterns. We found that the interaction between excitation and inhibition is responsible for two fundamental computational mechanisms: (1) a balanced excitation/inhibition in strongly activated mitral cells, leading to a sparse representation of odorant input, and (2) an unbalanced excitation/inhibition (inhibition dominated) in surrounding weakly activated mitral cells, leading to lateral inhibition. These results suggest how both mechanisms can carry information about the input patterns, with optimal level of synaptic excitation and inhibition producing the highest level of sparseness and decorrelation in the network response. The results suggest how the learning process, through the emergent development of these mechanisms, can enhance odor representation of olfactory bulb.
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Y.Y. and M.M. contributed equally to this work.
Author contributions: Y.Y., M.M., and G.M.S. designed research; Y.Y. and M.L.H. performed research; Y.Y. analyzed data; Y.Y., M.M., and G.M.S. wrote the paper.
ISSN:0270-6474
1529-2401
DOI:10.1523/jneurosci.1834-14.2014