A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical Networks In Vitro

By Aonan Tang, D. Jackson, Jon P. Hobbs, Wei Chen, Jodi L. Smith, H. Patel, Anita Prieto, D. Petrusca, M. Grivich, Alexander Sher and 4 more. Journal of Neuroscience, 2008

302 citations recorded by the source corpus.

Multineuron firing patterns are often observed, yet are predicted to be rare by models that assume independent firing. To explain these correlated network states, two groups recently applied a second-order maximum entropy model that used only observed firing rates and pairwise interactions as parameters (Schneidman et al., 2006; Shlens et al., 2006). Interestingly, with these minimal assumptions they predicted 90–99% of network correlations. If generally applicable, this approach could vastly simplify analyses of complex networks. However, this initial work was done largely on retinal tissue, and its applicability to cortical circuits is mostly unknown. This work also did not address the temporal evolution of correlated states. To investigate these issues, we applied the model to multielectrode data containing spontaneous spikes or local field potentials from cortical slices and cultures. The model worked slightly less well in cortex than in retina, accounting for 88 ± 7% (mean ± SD) of network correlations. In addition, in 8 of 13 preparations, the observed sequences of correlated states were significantly longer than predicted by concatenating states from the model. This suggested that temporal dependencies are a common feature of cortical network activity, and should be considered in future models. We found a significant relationship between strong pairwise temporal correlations and observed sequence length, suggesting that pairwise temporal correlations may allow the model to be extended into the temporal domain. We conclude that although a second-order maximum entropy model successfully predicts correlated states in cortical networks, it should be extended to account for temporal correlations observed between states.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/c7ca0ee26647facce4065cf2d5ecd01689627998

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Citations in the corpus

A Small World of Neuronal Synchrony (2008, background): “…are correlated positively because strongly correlated units are more likely to interact strongly, but this relation, as in previous studies (Schneidman et al. 2006; Tang et al. 2008), is not straightforward: Some neuronal pairs with strong correlations have weak interactions and vice versa.”
Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods (2009, background): “neuronal systems, such as the recordings from slices of vertebrate cortex (12).”
Inverse statistical problems: from the inverse Ising problem to data science (2017, result): “Similar results were obtained also from cortical cells in cell cultures [217].”
Recurrent Network Models of Sequence Generation and Memory. (2016, background): “…(Luczak et al., 2007; Schwartz and Moran, 1999; Andersen et al., 2004; Pulvermu¨ller and Shtyrov, 2009; Buonomano, 2003; Ikegaya et al., 2004; Tang et al., 2008; Seidemann et al., 1996; Fujisawa et al., 2008; Crowe et al., 2010; Harvey et al., 2012), hippocampus (Na´dasdy et al., 1999; Louie…”