A Model of the Contribution of Interneuron Diversity to Recurrent Network Oscillation Generation and Information Coding | SpringerLink
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A Model of the Contribution of Interneuron Diversity to Recurrent Network Oscillation Generation and Information Coding

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Brain Informatics (BI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13974))

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Abstract

Spiking neural network models that have studied how oscillations are generated by recurrent cortical circuits and how they encode information have been focused on describing the encoding of information about external sensory stimuli carried by feed-forward inputs in a two-population circuit configuration that includes excitatory cells and fast-spiking interneurons. Here we extend these models to explore the contribution of different classes of cortical interneurons to cortical oscillations. We found that in our extended model, the feed-forward stimulus is still mainly encoded in the gamma frequency range, consistent with earlier works using a single interneuron type. However, we also found that the information carried by different regions of the gamma frequency range was larger than the sum of the information carried by the two individual frequencies. This shows that the power values at different frequencies carried information about the feedforward input in a synergistic way. This is in contrast to previous models with only one interneuron type, which mainly led to redundant information between frequencies in the gamma range. These results suggest that interneuron diversity has useful properties for enriching the encoding of information in the gamma frequency range.

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Author information

Authors and Affiliations

  1. Istituto Italiano di Tecnologia, Rovereto, Italy

    Gabriel Matías Lorenz & Stefano Panzeri

  2. Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy

    Gabriel Matías Lorenz

  3. Department of Excellence for Neural Information Processing, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany

    Gabriel Matías Lorenz & Stefano Panzeri

  4. Department of Computer Engineering, Automation and Robotics, University of Granada, Granada, Spain

    Pablo Martínez-Cañada

Authors
  1. Gabriel Matías Lorenz
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  2. Pablo Martínez-Cañada
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  3. Stefano Panzeri
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Corresponding author

Correspondence to Gabriel Matías Lorenz .

Editor information

Editors and Affiliations

  1. Stevens Institute of Technology, Hoboken, NJ, USA

    Feng Liu

  2. Lehigh University, Bethlehem, PA, USA

    Yu Zhang

  3. Maebashi Institute of Technology, Maebashi, Japan

    Hongzhi Kuai

  4. Boston University, Boston, MA, USA

    Emily P. Stephen

  5. Stevens Institute of Technology, Hoboken, NJ, USA

    Hongjun Wang

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Lorenz, G.M., Martínez-Cañada, P., Panzeri, S. (2023). A Model of the Contribution of Interneuron Diversity to Recurrent Network Oscillation Generation and Information Coding. In: Liu, F., Zhang, Y., Kuai, H., Stephen, E.P., Wang, H. (eds) Brain Informatics. BI 2023. Lecture Notes in Computer Science(), vol 13974. Springer, Cham. https://doi.org/10.1007/978-3-031-43075-6_4

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  • DOI: https://doi.org/10.1007/978-3-031-43075-6_4

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