Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods

By Simona Cocco, Stanislas Leibler, Rémi Monasson. 2009

187 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Direct-coupling analysis of residue coevolution captures native contacts across many protein families (2011, neutral): “;AL exp ∑ i<j eijðAi;AjÞ þ∑ i hiðAiÞ [7]”
Are Biological Systems Poised at Criticality? (2010, methodology): “Approximate analytic methods, such as high temperature expansions [35, 36] or message-passing algorithms [37, 38], were also developed, and shown to be fast and accurate in the perturbative regime of weak correlations.”
From Principal Component to Direct Coupling Analysis of Coevolution in Proteins: Low-Eigenvalue Modes are Needed for Structure Prediction (2012, unknown): “MaxEnt has been used to analyze many types of biological data, ranging from multi-electrode recording of neural activities [25,26], gene concentrations in genetic networks [27], bird flocking [28] etc.”
Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model. (2018, methodology): “In particular, the Ising (39) and Potts (40) models were used to study the collective behavior of neurons (41,42), infer gene-interaction networks from experimentally observed transcription proﬁles (43), predict residue-residue contacts from multiple sequence alignments (37,44,45), study protein…”
Inverse statistical problems: from the inverse Ising problem to data science (2017, background): “Simoncelli and collaborators [154, 164] and Cocco, Leibler, and Monasson [48] use an integrate-and-ﬁre model [39] to infer how the neurons are interconnected on the basis of time series of spikes in all neurons.”
Spin glass models for a network of real neurons (2009, methodology): “For larger systems, this “inverse Ising problem” or Boltzmann machine learning, as it is known in computer science [49], is a hard computational problem that has to be solved by approximate schemes [14, 15, 21].”
Colloquium: Criticality and dynamical scaling in living systems (2017, background): “44 Obtaining the optimal parameter set –i.e. inferring effective interactions from correlations– is a computationally costly task, usually referred as “inverse Ising problem” (Aurell and Ekeberg, 2012; Cocco et al., 2009; Schneidman et al., 2006). in the inferred model Eq.”
Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems (2016, methodology): “One way to model the spiking activity of a population of neurons is through the classic Ising model [344, 345], in terms of the following maximum entropy distribution:”