Abstract
We extend previous mean-field approaches for non-equilibrium neural network models to estimate correlations in the system. This offers a powerful tool for approximating the system dynamics, as well as a fast method for inferring network parameters from observations. We develop our method for the asymmetric kinetic Ising model and test its performance on 1) synthetic data generated by an asymmetric Sherrington Kirkpatrick model and 2) recordings of in vitro neuron spiking activity from the mouse somatosensory cortex. We find that our mean-field method outperforms previous ones in estimating networks correlations and successfully reconstructs network dynamics from data near a phase transition showing large fluctuations.
Á. Poc-López and M. Aguilera—Contributed equally to this work.
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Acknowledgements
M.A. was funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 892715.
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Poc-López, Á., Aguilera, M. (2021). Inference in Neural Networks Using Conditional Mean-Field Methods. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13109. Springer, Cham. https://doi.org/10.1007/978-3-030-92270-2_20
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