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Journal of Computational Neuroscience

, Volume 41, Issue 3, pp 367–391 | Cite as

Linking dynamics of the inhibitory network to the input structure

  • Maxim Komarov
  • Maxim BazhenovEmail author
Article

Abstract

Networks of inhibitory interneurons are found in many distinct classes of biological systems. Inhibitory interneurons govern the dynamics of principal cells and are likely to be critically involved in the coding of information. In this theoretical study, we describe the dynamics of a generic inhibitory network in terms of low-dimensional, simplified rate models. We study the relationship between the structure of external input applied to the network and the patterns of activity arising in response to that stimulation. We found that even a minimal inhibitory network can generate a great diversity of spatio-temporal patterning including complex bursting regimes with non-trivial ratios of burst firing. Despite the complexity of these dynamics, the network’s response patterns can be predicted from the rankings of the magnitudes of external inputs to the inhibitory neurons. This type of invariant dynamics is robust to noise and stable in densely connected networks with strong inhibitory coupling. Our study predicts that the response dynamics generated by an inhibitory network may provide critical insights about the temporal structure of the sensory input it receives.

Keywords

Inhibitory neurons Information coding Neural network Olfactory system Spike sequences Odor discrimination 

Notes

Acknowledgments

This study was supported by grants from NIDCD (R01 DC012943) and ONR (MURI: N000141310672). We thank Andrey Shilnikov and Mark Stopfer for fruitful discussions.

Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflict of interest.

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© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of MedicineUniversity of California San DiegoLa JollaUSA
  2. 2.Department of Physics and AstronomyUniversity of PotsdamPotsdamGermany
  3. 3.Department of Control TheoryNizhny Novgorod State UniversityNizhny NovgorodRussia

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