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Inferring Neural Circuit Interactions and Neuromodulation from Local Field Potential and Electroencephalogram Measures

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Brain Informatics (BI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12960))

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Abstract

Electrical recordings of neural mass activity, such as local field potentials (LFPs) and electroencephalograms (EEGs), have been instrumental in studying brain function. However, being aggregate signals that lack cellular resolution, these signals are not easy to interpret directly in terms of neural functions. Developing tools for a reliable estimation of key neural parameters from these signals, such as the interaction between excitation and inhibition or the level of neuromodulation, is important both for neuroscience and clinical applications. Over the years we have developed tools based on the combination of neural network modelling and computational analysis of empirical data to estimate neural parameters from aggregate neural signals. The purpose of this paper, which accompanies an Invited Plenary Lecture in this conference, is to review the main tools that we have developed to estimate neural parameters from mass signals, and to outline future challenges and directions for developing computational tools to invert aggregate neural signals in terms of neural circuit parameters.

This work was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie (grant agreement No 893825 to P.M.C), the NIH Brain Initiative (grants U19NS107464 to S.P. and NS108410 to S.P.) and the Simons Foundation (SFARI Explorer 602849 to S.P.).

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Author information

Authors and Affiliations

  1. Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Genova and Rovereto, Italy

    Pablo Martínez-Cañada, Shahryar Noei & Stefano Panzeri

  2. Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia, Genova, Italy

    Pablo Martínez-Cañada

  3. CIMeC, University of Trento, Rovereto, Italy

    Shahryar Noei

  4. Department of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany

    Stefano Panzeri

Authors
  1. Pablo Martínez-Cañada
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  2. Shahryar Noei
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  3. Stefano Panzeri
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Corresponding author

Correspondence to Stefano Panzeri .

Editor information

Editors and Affiliations

  1. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  2. Jahangirnagar University, Dhaka, Bangladesh

    M Shamim Kaiser

  3. University of Padua, Padua, Italy

    Stefano Vassanelli

  4. Tsinghua University, Beijing, China

    Qionghai Dai

  5. Maebashi Institute of Technology, Maebashi, Japan

    Ning Zhong

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Martínez-Cañada, P., Noei, S., Panzeri, S. (2021). Inferring Neural Circuit Interactions and Neuromodulation from Local Field Potential and Electroencephalogram Measures. In: Mahmud, M., Kaiser, M.S., Vassanelli, S., Dai, Q., Zhong, N. (eds) Brain Informatics. BI 2021. Lecture Notes in Computer Science(), vol 12960. Springer, Cham. https://doi.org/10.1007/978-3-030-86993-9_1

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  • DOI: https://doi.org/10.1007/978-3-030-86993-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86992-2

  • Online ISBN: 978-3-030-86993-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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