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Pairwise Ising Model Analysis of Human Cortical Neuron Recordings

Part of the Lecture Notes in Computer Science book series (LNIP,volume 10589)

Abstract

During wakefulness and deep sleep brain states, cortical neural networks show a different behavior, with the second characterized by transients of high network activity. To investigate their impact on neuronal behavior, we apply a pairwise Ising model analysis by inferring the maximum entropy model that reproduces single and pairwise moments of the neuron’s spiking activity. In this work we first review the inference algorithm introduced in Ferrari, Phys. Rev. E (2016) [1]. We then succeed in applying the algorithm to infer the model from a large ensemble of neurons recorded by multi-electrode array in human temporal cortex. We compare the Ising model performance in capturing the statistical properties of the network activity during wakefulness and deep sleep. For the latter, the pairwise model misses relevant transients of high network activity, suggesting that additional constraints are necessary to accurately model the data.

Keywords

  • Ising model
  • Maximum entropy principle
  • Natural gradient
  • Human temporal cortex
  • Multielectrode array recording
  • Brain states

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Notes

  1. 1.

    The results of this section are grounded on the repeated use of central limit theorem. See [1] for more detail.

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Acknowledgments

We thank B. Telenczuk, G. Tkacik and M. Di Volo for useful discussion. Research funded by European Community (Human Brain Project, H2020-720270), ANR TRAJECTORY, ANR OPTIMA, French State program Investissements dAvenir managed by the Agence Nationale de la Recherche [LIFESENSES: ANR-10-LABX-65] and NIH grant U01NS09050.

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Nghiem, TA., Marre, O., Destexhe, A., Ferrari, U. (2017). Pairwise Ising Model Analysis of Human Cortical Neuron Recordings. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2017. Lecture Notes in Computer Science(), vol 10589. Springer, Cham. https://doi.org/10.1007/978-3-319-68445-1_30

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  • DOI: https://doi.org/10.1007/978-3-319-68445-1_30

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