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Adaptive regulation of sparseness by feedforward inhibition

Nature Neuroscience volume 10, pages 1176–1184 (2007)Cite this article

Abstract

In the mushroom body of insects, odors are represented by very few spikes in a small number of neurons, a highly efficient strategy known as sparse coding. Physiological studies of these neurons have shown that sparseness is maintained across thousand-fold changes in odor concentration. Using a realistic computational model, we propose that sparseness in the olfactory system is regulated by adaptive feedforward inhibition. When odor concentration changes, feedforward inhibition modulates the duration of the temporal window over which the mushroom body neurons may integrate excitatory presynaptic input. This simple adaptive mechanism could maintain the sparseness of sensory representations across wide ranges of stimulus conditions.

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Figure 1: Network structure.
Figure 2: Oscillatory dynamics of antennal lobe neurons for different odor concentrations.
Figure 3: Collective dynamics of neurons in the modeled antennal lobe.
Figure 4: Collective dynamics of neurons in the modeled mushroom body.
Figure 5: PN and LHI responses for different odor concentrations.
Figure 6: Effect of adaptive feedforward inhibition of KC activity.
Figure 7: Phase advance of LHIs maintains the sparseness of KC activity.
Figure 8: Role of feedforward inhibition in a minimal neural circuit.

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Acknowledgements

This work was supported by grants from the US National Institute of Deafness and other Communication Disorders (C.A., G.L. and M.B.), the National Science Foundation (G.L.) and a US National Institute of Child Health and Human Development intramural award (M.S.).

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Authors and Affiliations

  1. The Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, 92037, California, USA

    Collins Assisi & Maxim Bazhenov

  2. US National Institutes of Health, National Institute of Child Health and Human Development, 35 Lincoln Drive, Bethesda, MSC 3715, 20892, Maryland, USA

    Mark Stopfer

  3. California Institute of Technology, 1201 East California Boulevard, Pasadena, 91125, California, USA

    Gilles Laurent

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  1. Collins Assisi
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Correspondence to Maxim Bazhenov.

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Assisi, C., Stopfer, M., Laurent, G. et al. Adaptive regulation of sparseness by feedforward inhibition. Nat Neurosci 10, 1176–1184 (2007). https://doi.org/10.1038/nn1947

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