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

@article{citeulike:2371765, 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 9099\% 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. 10.1523/JNEUROSCI.3359-07.2008}, author = {Tang, Aonan and Jackson, David and Hobbs, Jon and Chen, Wei and Smith, Jodi L. and Patel, Hema and Prieto, Anita and Petrusca, Dumitru and Grivich, Matthew I. and Sher, Alexander and Hottowy, Pawel and Dabrowski, Wladyslaw and Litke, Alan M. and Beggs, John M. }, citeulike-article-id = {2371765}, doi = {10.1523/JNEUROSCI.3359-07.2008}, journal = {J. Neurosci.}, keywords = {toread-tribes}, month = {January}, number = {2}, pages = {505--518}, posted-at = {2008-02-13 22:44:52}, priority = {2}, title = {A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical Networks In Vitro}, url = {http://dx.doi.org/10.1523/JNEUROSCI.3359-07.2008}, volume = {28}, year = {2008} }

TY - JOUR ID - citeulike:2371765 L3 - citeulike-article-id:2371765 TI - A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical Networks In Vitro JF - J. Neurosci. VL - 28 IS - 2 SP - 505 EP - 518 N2 - 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 9099% 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. 10.1523/JNEUROSCI.3359-07.2008 KW - toread-tribes AU - Tang, Aonan AU - Jackson, David AU - Hobbs, Jon AU - Chen, Wei AU - Smith, Jodi AU - Patel, Hema AU - Prieto, Anita AU - Petrusca, Dumitru AU - Grivich, Matthew AU - Sher, Alexander AU - Hottowy, Pawel AU - Dabrowski, Wladyslaw AU - Litke, Alan AU - Beggs, John PY - 2008/01/09/ UR - http://dx.doi.org/10.1523/JNEUROSCI.3359-07.2008 ER -