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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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Reconstruction improvement through external stimulation.A and B, fraction of true positives from the reconstructions at the 5% false positive mark for the studied networks. “E–only” networks are shown in A; “E+I” networks in B. Inset: dependence of the spontaneous firing rate on the applied external drive, emulated here by increasing the rate of the background drive to the culture in silico. All the excitatory reconstructions reach a stable plateau in the reconstruction after removal of the instantaneous feedback term (IFT) correction (see Methods). The inhibitory reconstruction is accurate only for higher values of the external drive. C ROC curves extracted from A and B with an external stimulation of 4 Hz. Inset: fraction of excitatory and inhibitory neurons correctly identified from these reconstructions. Identification was statistically significant compared to random guessing. For excitatory neurons,  (**); for inhibitory neurons,  (***). D Example of an actual reconstruction after identification of neuronal type. Identified excitatory neurons are shown in red and inhibitory ones in blue. Incorrectly identified neurons are shown in grey. Correctly identified excitatory and inhibitory links are shown in red and blue, respectively, and wrongly identified links are shown in black. For clarity in the representation of the links, a threshold value lower than the optimal has been applied.
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pone-0098842-g004: Reconstruction improvement through external stimulation.A and B, fraction of true positives from the reconstructions at the 5% false positive mark for the studied networks. “E–only” networks are shown in A; “E+I” networks in B. Inset: dependence of the spontaneous firing rate on the applied external drive, emulated here by increasing the rate of the background drive to the culture in silico. All the excitatory reconstructions reach a stable plateau in the reconstruction after removal of the instantaneous feedback term (IFT) correction (see Methods). The inhibitory reconstruction is accurate only for higher values of the external drive. C ROC curves extracted from A and B with an external stimulation of 4 Hz. Inset: fraction of excitatory and inhibitory neurons correctly identified from these reconstructions. Identification was statistically significant compared to random guessing. For excitatory neurons, (**); for inhibitory neurons, (***). D Example of an actual reconstruction after identification of neuronal type. Identified excitatory neurons are shown in red and inhibitory ones in blue. Incorrectly identified neurons are shown in grey. Correctly identified excitatory and inhibitory links are shown in red and blue, respectively, and wrongly identified links are shown in black. For clarity in the representation of the links, a threshold value lower than the optimal has been applied.

Mentions: The performance of our GTE algorithm combined with a weak network stimulation is illustrated in Fig. 4A, where we show the fraction of true positives in the reconstruction of “E–only” networks at 5% false positives. The presence of even very small external drives substantially enhances reconstruction based on GTE. For higher drives, reconstruction performance reaches a plateau that quantifies the range of optimum stimulation. Performance later decays due to the excess of stimulation, which substantially perturbs spontaneous activity and alters qualitatively the global network dynamics. We incidentally remark that the incorporation of the external drive makes unnecessary — actually, even deleterious — the instantaneous feedback term correction (IFT, see Methods), i.e., an ad hoc modification to the original formulation of TE which was introduced in [22] to cope with the poor frame rate of calcium fluorescence recordings, definitely slower than the time-scale of monosynaptic interaction delays. The IFT correction allows to encompass interactions occurring in the same temporal bin of the recording for TE estimation, a feature useful to enhance reconstruction results when the time-scale of pre-postsynaptic neuron interactions is fast relative to the time resolution of the recording. However, same–bin interactions also result in an overestimation of bidirectional connections, since one cannot establish directionality within a single time bin. When the firing rate is enhanced with respect to spontaneous conditions these negative effects of the IFT corrections become predominant.


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)

Reconstruction improvement through external stimulation.A and B, fraction of true positives from the reconstructions at the 5% false positive mark for the studied networks. “E–only” networks are shown in A; “E+I” networks in B. Inset: dependence of the spontaneous firing rate on the applied external drive, emulated here by increasing the rate of the background drive to the culture in silico. All the excitatory reconstructions reach a stable plateau in the reconstruction after removal of the instantaneous feedback term (IFT) correction (see Methods). The inhibitory reconstruction is accurate only for higher values of the external drive. C ROC curves extracted from A and B with an external stimulation of 4 Hz. Inset: fraction of excitatory and inhibitory neurons correctly identified from these reconstructions. Identification was statistically significant compared to random guessing. For excitatory neurons,  (**); for inhibitory neurons,  (***). D Example of an actual reconstruction after identification of neuronal type. Identified excitatory neurons are shown in red and inhibitory ones in blue. Incorrectly identified neurons are shown in grey. Correctly identified excitatory and inhibitory links are shown in red and blue, respectively, and wrongly identified links are shown in black. For clarity in the representation of the links, a threshold value lower than the optimal has been applied.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0098842-g004: Reconstruction improvement through external stimulation.A and B, fraction of true positives from the reconstructions at the 5% false positive mark for the studied networks. “E–only” networks are shown in A; “E+I” networks in B. Inset: dependence of the spontaneous firing rate on the applied external drive, emulated here by increasing the rate of the background drive to the culture in silico. All the excitatory reconstructions reach a stable plateau in the reconstruction after removal of the instantaneous feedback term (IFT) correction (see Methods). The inhibitory reconstruction is accurate only for higher values of the external drive. C ROC curves extracted from A and B with an external stimulation of 4 Hz. Inset: fraction of excitatory and inhibitory neurons correctly identified from these reconstructions. Identification was statistically significant compared to random guessing. For excitatory neurons, (**); for inhibitory neurons, (***). D Example of an actual reconstruction after identification of neuronal type. Identified excitatory neurons are shown in red and inhibitory ones in blue. Incorrectly identified neurons are shown in grey. Correctly identified excitatory and inhibitory links are shown in red and blue, respectively, and wrongly identified links are shown in black. For clarity in the representation of the links, a threshold value lower than the optimal has been applied.
Mentions: The performance of our GTE algorithm combined with a weak network stimulation is illustrated in Fig. 4A, where we show the fraction of true positives in the reconstruction of “E–only” networks at 5% false positives. The presence of even very small external drives substantially enhances reconstruction based on GTE. For higher drives, reconstruction performance reaches a plateau that quantifies the range of optimum stimulation. Performance later decays due to the excess of stimulation, which substantially perturbs spontaneous activity and alters qualitatively the global network dynamics. We incidentally remark that the incorporation of the external drive makes unnecessary — actually, even deleterious — the instantaneous feedback term correction (IFT, see Methods), i.e., an ad hoc modification to the original formulation of TE which was introduced in [22] to cope with the poor frame rate of calcium fluorescence recordings, definitely slower than the time-scale of monosynaptic interaction delays. The IFT correction allows to encompass interactions occurring in the same temporal bin of the recording for TE estimation, a feature useful to enhance reconstruction results when the time-scale of pre-postsynaptic neuron interactions is fast relative to the time resolution of the recording. However, same–bin interactions also result in an overestimation of bidirectional connections, since one cannot establish directionality within a single time bin. When the firing rate is enhanced with respect to spontaneous conditions these negative effects of the IFT corrections become predominant.

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