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Reconstruction of chaotic neural network from observed firing rates

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A. Pikovsky

Chaotic Dynamics · Submitted 3 Apr 2016

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We demonstrate that for a large enough data set, the method gives an accurate estimation of the coupling matrix and of other parameters of the system, including the gain function.

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arXiv ID1604.00619 (version 1)
Pith IDH42JRZBE, Pith's permanent record of this paper and its review
CitationA. Pikovsky. "Reconstruction of chaotic neural network from observed firing rates." arXiv:1604.00619 (2016). https://pith.science/paper/H42JRZBE
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,637 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.58 · human participants 0.02 · controlled comparison 0.02 · randomized assignment 0.02 · states quantitative result 0.08 · compares with prior results 0.18 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Randomly coupled neural fields demonstrate chaotic variation of firing rates, if the coupling is strong enough, as has been shown by Sompolinsky et. al [Phys. Rev. Lett., v. 61, 259 (1988)]. We present a method for reconstruction of the coupling matrix from the observations of the chaotic firing rates. The approach is based on the particular property of the nonlinearity in the coupling, as the latter is determined by a sigmoidal gain function. We demonstrate that for a large enough data set, the method gives an accurate estimation of the coupling matrix and of other parameters of the system, including the gain function.

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What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Randomly coupled neural fields demonstrate chaotic variation of firing rates, if the coupling is strong enough, as has been shown by Sompolinsky et. al [Phys. Rev. Lett., v. 61, 259 (1988)]. background 1.00 0.03 0.03 asserted 0.98
s1 We present a method for reconstruction of the coupling matrix from the observations of the chaotic firing rates. contribution 0.99 0.92 0.21 asserted 0.99
s2 The approach is based on the particular property of the nonlinearity in the coupling, as the latter is determined by a sigmoidal gain function. method 1.00 0.36 0.06 asserted 1.00
s3 We demonstrate that for a large enough data set, the method gives an accurate estimation of the coupling matrix and of other parameters of the system, including the gain function. result 1.00 0.95 0.82 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.82.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Randomly coupled neural fields demonstrate chaotic variation of firing rates, if the coupling is strong enough, as has been shown by Sompolinsky et. al [Phys. Rev. Lett., v. 61, 259 (1988)]. We present a method for reconstruction of the coupling matrix from the observations of the chaotic firing rates. The approach is based on the particular property of the nonlinearity in the coupling, as the latter is determined by a sigmoidal gain function. We demonstrate that for a large enough data set, the method gives an accurate estimation of the coupling matrix and of other parameters of the system, including the gain function.

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