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A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical NetworksIn Vitro
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| Zeitschriftentitel: | The Journal of Neuroscience |
|---|---|
| Personen und Körperschaften: | , , , , , , , , , , , , , |
| In: | The Journal of Neuroscience, 28, 2008, 2, S. 505-518 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Society for Neuroscience
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| Schlagwörter: |
| author_facet |
Tang, Aonan Jackson, David Hobbs, Jon Chen, Wei Smith, Jodi L. Patel, Hema Prieto, Anita Petrusca, Dumitru Grivich, Matthew I. Sher, Alexander Hottowy, Pawel Dabrowski, Wladyslaw Litke, Alan M. Beggs, John M. Tang, Aonan Jackson, David Hobbs, Jon Chen, Wei Smith, Jodi L. Patel, Hema Prieto, Anita Petrusca, Dumitru Grivich, Matthew I. Sher, Alexander Hottowy, Pawel Dabrowski, Wladyslaw Litke, Alan M. Beggs, John M. |
|---|---|
| author |
Tang, Aonan Jackson, David Hobbs, Jon Chen, Wei Smith, Jodi L. Patel, Hema Prieto, Anita Petrusca, Dumitru Grivich, Matthew I. Sher, Alexander Hottowy, Pawel Dabrowski, Wladyslaw Litke, Alan M. Beggs, John M. |
| spellingShingle |
Tang, Aonan Jackson, David Hobbs, Jon Chen, Wei Smith, Jodi L. Patel, Hema Prieto, Anita Petrusca, Dumitru Grivich, Matthew I. Sher, Alexander Hottowy, Pawel Dabrowski, Wladyslaw Litke, Alan M. Beggs, John M. The Journal of Neuroscience A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical NetworksIn Vitro General Neuroscience |
| author_sort |
tang, aonan |
| spelling |
Tang, Aonan Jackson, David Hobbs, Jon Chen, Wei Smith, Jodi L. Patel, Hema Prieto, Anita Petrusca, Dumitru Grivich, Matthew I. Sher, Alexander Hottowy, Pawel Dabrowski, Wladyslaw Litke, Alan M. Beggs, John M. 0270-6474 1529-2401 Society for Neuroscience General Neuroscience http://dx.doi.org/10.1523/jneurosci.3359-07.2008 <jats:p>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.</jats:p> A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical Networks<i>In Vitro</i> The Journal of Neuroscience |
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The Journal of Neuroscience |
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49 |
| title |
A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical NetworksIn Vitro |
| title_unstemmed |
A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical NetworksIn Vitro |
| title_full |
A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical NetworksIn Vitro |
| title_fullStr |
A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical NetworksIn Vitro |
| title_full_unstemmed |
A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical NetworksIn Vitro |
| title_short |
A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical NetworksIn Vitro |
| title_sort |
a maximum entropy model applied to spatial and temporal correlations from cortical networks<i>in vitro</i> |
| topic |
General Neuroscience |
| url |
http://dx.doi.org/10.1523/jneurosci.3359-07.2008 |
| publishDate |
2008 |
| physical |
505-518 |
| description |
<jats:p>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.</jats:p> |
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| author | Tang, Aonan, Jackson, David, Hobbs, Jon, Chen, Wei, Smith, Jodi L., Patel, Hema, Prieto, Anita, Petrusca, Dumitru, Grivich, Matthew I., Sher, Alexander, Hottowy, Pawel, Dabrowski, Wladyslaw, Litke, Alan M., Beggs, John M. |
| author_facet | Tang, Aonan, Jackson, David, Hobbs, Jon, Chen, Wei, Smith, Jodi L., Patel, Hema, Prieto, Anita, Petrusca, Dumitru, Grivich, Matthew I., Sher, Alexander, Hottowy, Pawel, Dabrowski, Wladyslaw, Litke, Alan M., Beggs, John M., Tang, Aonan, Jackson, David, Hobbs, Jon, Chen, Wei, Smith, Jodi L., Patel, Hema, Prieto, Anita, Petrusca, Dumitru, Grivich, Matthew I., Sher, Alexander, Hottowy, Pawel, Dabrowski, Wladyslaw, Litke, Alan M., Beggs, John M. |
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| description | <jats:p>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.</jats:p> |
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| institution | DE-Pl11, DE-Rs1, DE-105, DE-14, DE-Ch1, DE-L229, DE-D275, DE-Bn3, DE-Brt1, DE-Zwi2, DE-D161, DE-Gla1, DE-Zi4, DE-15 |
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| series | The Journal of Neuroscience |
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| spelling | Tang, Aonan Jackson, David Hobbs, Jon Chen, Wei Smith, Jodi L. Patel, Hema Prieto, Anita Petrusca, Dumitru Grivich, Matthew I. Sher, Alexander Hottowy, Pawel Dabrowski, Wladyslaw Litke, Alan M. Beggs, John M. 0270-6474 1529-2401 Society for Neuroscience General Neuroscience http://dx.doi.org/10.1523/jneurosci.3359-07.2008 <jats:p>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.</jats:p> A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical Networks<i>In Vitro</i> The Journal of Neuroscience |
| spellingShingle | Tang, Aonan, Jackson, David, Hobbs, Jon, Chen, Wei, Smith, Jodi L., Patel, Hema, Prieto, Anita, Petrusca, Dumitru, Grivich, Matthew I., Sher, Alexander, Hottowy, Pawel, Dabrowski, Wladyslaw, Litke, Alan M., Beggs, John M., The Journal of Neuroscience, A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical NetworksIn Vitro, General Neuroscience |
| title | A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical NetworksIn Vitro |
| title_full | A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical NetworksIn Vitro |
| title_fullStr | A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical NetworksIn Vitro |
| title_full_unstemmed | A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical NetworksIn Vitro |
| title_short | A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical NetworksIn Vitro |
| title_sort | a maximum entropy model applied to spatial and temporal correlations from cortical networks<i>in vitro</i> |
| title_unstemmed | A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical NetworksIn Vitro |
| topic | General Neuroscience |
| url | http://dx.doi.org/10.1523/jneurosci.3359-07.2008 |