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Transfer entropy reconstruction and labeling of neuronal connections from simulated calcium imaging.

Orlandi JG, Stetter O, Soriano J, Geisel T, Battaglia D - PLoS ONE (2014)

Bottom Line: Neuronal dynamics are fundamentally constrained by the underlying structural network architecture, yet much of the details of this synaptic connectivity are still unknown even in neuronal cultures in vitro.We show that, due to the model-free nature of the developed measure, both kinds of connections can be reliably inferred if the average firing rate between synchronous burst events exceeds a small minimum frequency.Furthermore, we suggest, based on systematic simulations, that even lower spontaneous inter-burst rates could be raised to meet the requirements of our reconstruction algorithm by applying a weak spatially homogeneous stimulation to the entire network.

View Article: PubMed Central - PubMed

Affiliation: Departament d'Estructura i Consituents de la Matèria, Universitat de Barcelona, Barcelona, Spain.

ABSTRACT
Neuronal dynamics are fundamentally constrained by the underlying structural network architecture, yet much of the details of this synaptic connectivity are still unknown even in neuronal cultures in vitro. Here we extend a previous approach based on information theory, the Generalized Transfer Entropy, to the reconstruction of connectivity of simulated neuronal networks of both excitatory and inhibitory neurons. We show that, due to the model-free nature of the developed measure, both kinds of connections can be reliably inferred if the average firing rate between synchronous burst events exceeds a small minimum frequency. Furthermore, we suggest, based on systematic simulations, that even lower spontaneous inter-burst rates could be raised to meet the requirements of our reconstruction algorithm by applying a weak spatially homogeneous stimulation to the entire network. By combining multiple recordings of the same in silico network before and after pharmacologically blocking inhibitory synaptic transmission, we show then how it becomes possible to infer with high confidence the excitatory or inhibitory nature of each individual neuron.

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Optimal network reconstruction.A ROC curves for the reconstruction of a network with both excitatory and inhibitory connections active, supposing to know a priori information about neuronal type. GTE is first applied to the “E+I” data. Next, following Dale's principle and exploiting the available information on neuronal type, links are classified according to their excitatory (red) or inhibitory (blue) nature. B ROC curves for the best possible identification of excitatory and inhibitory connections, when information on neuronal type is unaccessible. Excitatory links (red) are identified by adding together the Transfer Entropy scores of simulations run in “E–only” and “E+I” conditions, and later thresholding them. Inhibitory links (blue) are identified by computing the difference in Transfer Entropy scores between the runs with inhibition present and blocked. Inset: fraction of excitatory and inhibitory neurons correctly identified from these ROC curves. Results were not significantly different from random guess (see Methods). All the results were averaged over different network realizations. The shaded areas in the main plots, as well as the error bars in the inset, correspond to 95% confidence intervals.
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pone-0098842-g003: Optimal network reconstruction.A ROC curves for the reconstruction of a network with both excitatory and inhibitory connections active, supposing to know a priori information about neuronal type. GTE is first applied to the “E+I” data. Next, following Dale's principle and exploiting the available information on neuronal type, links are classified according to their excitatory (red) or inhibitory (blue) nature. B ROC curves for the best possible identification of excitatory and inhibitory connections, when information on neuronal type is unaccessible. Excitatory links (red) are identified by adding together the Transfer Entropy scores of simulations run in “E–only” and “E+I” conditions, and later thresholding them. Inhibitory links (blue) are identified by computing the difference in Transfer Entropy scores between the runs with inhibition present and blocked. Inset: fraction of excitatory and inhibitory neurons correctly identified from these ROC curves. Results were not significantly different from random guess (see Methods). All the results were averaged over different network realizations. The shaded areas in the main plots, as well as the error bars in the inset, correspond to 95% confidence intervals.

Mentions: Being able to identify the type of a neuron — even with perfect accuracy — does not guarantee a priori that excitatory and inhibitory links can be inferred equally well. On the contrary, different reconstruction performances have to be expected in general, since the interaction mechanism of excitatory links is inherently different from the inhibitory ones, the former promoting the activity of the target neuron, whereas the latter restrain it. We have tested the accuracy of this ad hoc approach through numerical simulations. GTE is applied to the “E+I” data, and the reconstruction quality is assessed separately for the connections originating from neurons of different types (see Methods). Non trivially, the results of this analysis indicate that both types of connections are reconstructed with high accuracy (see Figure 3A). At a fraction of 10% of false positives, excitatory links are detected at a true positive rate of 80%. Inhibitory links show a lesser but still high detection accuracy, of about 60% of true positives.


Transfer entropy reconstruction and labeling of neuronal connections from simulated calcium imaging.

Orlandi JG, Stetter O, Soriano J, Geisel T, Battaglia D - PLoS ONE (2014)

Optimal network reconstruction.A ROC curves for the reconstruction of a network with both excitatory and inhibitory connections active, supposing to know a priori information about neuronal type. GTE is first applied to the “E+I” data. Next, following Dale's principle and exploiting the available information on neuronal type, links are classified according to their excitatory (red) or inhibitory (blue) nature. B ROC curves for the best possible identification of excitatory and inhibitory connections, when information on neuronal type is unaccessible. Excitatory links (red) are identified by adding together the Transfer Entropy scores of simulations run in “E–only” and “E+I” conditions, and later thresholding them. Inhibitory links (blue) are identified by computing the difference in Transfer Entropy scores between the runs with inhibition present and blocked. Inset: fraction of excitatory and inhibitory neurons correctly identified from these ROC curves. Results were not significantly different from random guess (see Methods). All the results were averaged over different network realizations. The shaded areas in the main plots, as well as the error bars in the inset, correspond to 95% confidence intervals.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4048312&req=5

pone-0098842-g003: Optimal network reconstruction.A ROC curves for the reconstruction of a network with both excitatory and inhibitory connections active, supposing to know a priori information about neuronal type. GTE is first applied to the “E+I” data. Next, following Dale's principle and exploiting the available information on neuronal type, links are classified according to their excitatory (red) or inhibitory (blue) nature. B ROC curves for the best possible identification of excitatory and inhibitory connections, when information on neuronal type is unaccessible. Excitatory links (red) are identified by adding together the Transfer Entropy scores of simulations run in “E–only” and “E+I” conditions, and later thresholding them. Inhibitory links (blue) are identified by computing the difference in Transfer Entropy scores between the runs with inhibition present and blocked. Inset: fraction of excitatory and inhibitory neurons correctly identified from these ROC curves. Results were not significantly different from random guess (see Methods). All the results were averaged over different network realizations. The shaded areas in the main plots, as well as the error bars in the inset, correspond to 95% confidence intervals.
Mentions: Being able to identify the type of a neuron — even with perfect accuracy — does not guarantee a priori that excitatory and inhibitory links can be inferred equally well. On the contrary, different reconstruction performances have to be expected in general, since the interaction mechanism of excitatory links is inherently different from the inhibitory ones, the former promoting the activity of the target neuron, whereas the latter restrain it. We have tested the accuracy of this ad hoc approach through numerical simulations. GTE is applied to the “E+I” data, and the reconstruction quality is assessed separately for the connections originating from neurons of different types (see Methods). Non trivially, the results of this analysis indicate that both types of connections are reconstructed with high accuracy (see Figure 3A). At a fraction of 10% of false positives, excitatory links are detected at a true positive rate of 80%. Inhibitory links show a lesser but still high detection accuracy, of about 60% of true positives.

Bottom Line: Neuronal dynamics are fundamentally constrained by the underlying structural network architecture, yet much of the details of this synaptic connectivity are still unknown even in neuronal cultures in vitro.We show that, due to the model-free nature of the developed measure, both kinds of connections can be reliably inferred if the average firing rate between synchronous burst events exceeds a small minimum frequency.Furthermore, we suggest, based on systematic simulations, that even lower spontaneous inter-burst rates could be raised to meet the requirements of our reconstruction algorithm by applying a weak spatially homogeneous stimulation to the entire network.

View Article: PubMed Central - PubMed

Affiliation: Departament d'Estructura i Consituents de la Matèria, Universitat de Barcelona, Barcelona, Spain.

ABSTRACT
Neuronal dynamics are fundamentally constrained by the underlying structural network architecture, yet much of the details of this synaptic connectivity are still unknown even in neuronal cultures in vitro. Here we extend a previous approach based on information theory, the Generalized Transfer Entropy, to the reconstruction of connectivity of simulated neuronal networks of both excitatory and inhibitory neurons. We show that, due to the model-free nature of the developed measure, both kinds of connections can be reliably inferred if the average firing rate between synchronous burst events exceeds a small minimum frequency. Furthermore, we suggest, based on systematic simulations, that even lower spontaneous inter-burst rates could be raised to meet the requirements of our reconstruction algorithm by applying a weak spatially homogeneous stimulation to the entire network. By combining multiple recordings of the same in silico network before and after pharmacologically blocking inhibitory synaptic transmission, we show then how it becomes possible to infer with high confidence the excitatory or inhibitory nature of each individual neuron.

Show MeSH
Related in: MedlinePlus