A maximum entropy model applied to spatial and temporal correlations from cortical networks in vitro

Aonan Tang, David Jackson, Jon Hobbs, Wei Chen, Jodi Smith, Hema Patel, Anita Prieto, Dumitru Petrusca, Matthew I. Grivich, Alexander Sher, Pawel Hottowy, Wladyslaw Dabrowski, Alan M. Litke, John M. Beggs

Research output: Contribution to journalArticle

184 Citations (Scopus)

Abstract

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 acommonfeature 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.

Original languageEnglish
Pages (from-to)505-518
Number of pages14
JournalJournal of Neuroscience
Volume28
Issue number2
DOIs
StatePublished - Jan 9 2008

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Entropy
Retina
In Vitro Techniques

Keywords

  • Culture
  • Human tissue
  • Local field potential
  • Microelectrode array
  • Neuronal avalanche
  • Slice

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

A maximum entropy model applied to spatial and temporal correlations from cortical networks in vitro. / Tang, Aonan; Jackson, David; Hobbs, Jon; Chen, Wei; Smith, Jodi; Patel, Hema; Prieto, Anita; Petrusca, Dumitru; Grivich, Matthew I.; Sher, Alexander; Hottowy, Pawel; Dabrowski, Wladyslaw; Litke, Alan M.; Beggs, John M.

In: Journal of Neuroscience, Vol. 28, No. 2, 09.01.2008, p. 505-518.

Research output: Contribution to journalArticle

Tang, A, Jackson, D, Hobbs, J, Chen, W, Smith, J, Patel, H, Prieto, A, Petrusca, D, Grivich, MI, Sher, A, Hottowy, P, Dabrowski, W, Litke, AM & Beggs, JM 2008, 'A maximum entropy model applied to spatial and temporal correlations from cortical networks in vitro', Journal of Neuroscience, vol. 28, no. 2, pp. 505-518. https://doi.org/10.1523/JNEUROSCI.3359-07.2008
Tang, Aonan ; Jackson, David ; Hobbs, Jon ; Chen, Wei ; Smith, Jodi ; Patel, Hema ; Prieto, Anita ; Petrusca, Dumitru ; Grivich, Matthew I. ; Sher, Alexander ; Hottowy, Pawel ; Dabrowski, Wladyslaw ; Litke, Alan M. ; Beggs, John M. / A maximum entropy model applied to spatial and temporal correlations from cortical networks in vitro. In: Journal of Neuroscience. 2008 ; Vol. 28, No. 2. pp. 505-518.
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