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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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Neuronal network dynamics.A Top: Bright field and fluorescence images of a small region of a neuronal culture at day in vitro 12. Bright spots correspond to firing neurons. Bottom: Representative time traces of recorded fluorescence signals of 3 individual neurons. The numbers beside each trace identify the neurons on the images. Data shows, for the same neurons, the signal in recordings with only excitation active (“E”) and the signal with both excitation and inhibition active (“E+I”). B Population-averaged fluorescence signals in experiments (left) and simulations (right), illustrating the semi-quantitative matching between in vitro and in silico data. Top: excitatory-only traces (“E–only” data). For the experiments, inhibition was silenced through application of saturating concentrations of bicuculline. For the simulations, inhibitory synapses were silenced by setting their efficacy to zero. Bottom: traces for both excitation and inhibition active (“E+I” data). Network bursts appear as a fast increase of the fluorescence signal followed by a slow decay. Bursts are more frequent and display lower and more heterogeneous amplitudes in the presence of inhibitory connections. C Histogram of population-averaged fluorescence intensity for a 1 h recordings in experiments (left) and simulations (right). Data is shown in semilogarithmic scale for clarity. Red curves correspond to the “E–only” condition, and the blue curves to the “E+I” one.
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pone-0098842-g001: Neuronal network dynamics.A Top: Bright field and fluorescence images of a small region of a neuronal culture at day in vitro 12. Bright spots correspond to firing neurons. Bottom: Representative time traces of recorded fluorescence signals of 3 individual neurons. The numbers beside each trace identify the neurons on the images. Data shows, for the same neurons, the signal in recordings with only excitation active (“E”) and the signal with both excitation and inhibition active (“E+I”). B Population-averaged fluorescence signals in experiments (left) and simulations (right), illustrating the semi-quantitative matching between in vitro and in silico data. Top: excitatory-only traces (“E–only” data). For the experiments, inhibition was silenced through application of saturating concentrations of bicuculline. For the simulations, inhibitory synapses were silenced by setting their efficacy to zero. Bottom: traces for both excitation and inhibition active (“E+I” data). Network bursts appear as a fast increase of the fluorescence signal followed by a slow decay. Bursts are more frequent and display lower and more heterogeneous amplitudes in the presence of inhibitory connections. C Histogram of population-averaged fluorescence intensity for a 1 h recordings in experiments (left) and simulations (right). Data is shown in semilogarithmic scale for clarity. Red curves correspond to the “E–only” condition, and the blue curves to the “E+I” one.

Mentions: Dissociated neurons grown in vitro self-organize and connect to one another, giving rise to a spontaneously active neuronal network within a week (see Figure 1A) [24], [30], [51], [52]. About 70–80% of the grown connections are excitatory, while the remaining 20–30% are inhibitory [51]. Activity in neuronal cultures is characterized by a bursting dynamics, where the whole network is active and displays quasi-synchronous, high frequency firing within 100–200 ms windows [30]. The timing of the bursts themselves is irregular, with average inter-burst intervals on the order of 10 s in a typical preparation. Between different bursts, firing across the network has a low-frequency and can be described as asynchronous.


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)

Neuronal network dynamics.A Top: Bright field and fluorescence images of a small region of a neuronal culture at day in vitro 12. Bright spots correspond to firing neurons. Bottom: Representative time traces of recorded fluorescence signals of 3 individual neurons. The numbers beside each trace identify the neurons on the images. Data shows, for the same neurons, the signal in recordings with only excitation active (“E”) and the signal with both excitation and inhibition active (“E+I”). B Population-averaged fluorescence signals in experiments (left) and simulations (right), illustrating the semi-quantitative matching between in vitro and in silico data. Top: excitatory-only traces (“E–only” data). For the experiments, inhibition was silenced through application of saturating concentrations of bicuculline. For the simulations, inhibitory synapses were silenced by setting their efficacy to zero. Bottom: traces for both excitation and inhibition active (“E+I” data). Network bursts appear as a fast increase of the fluorescence signal followed by a slow decay. Bursts are more frequent and display lower and more heterogeneous amplitudes in the presence of inhibitory connections. C Histogram of population-averaged fluorescence intensity for a 1 h recordings in experiments (left) and simulations (right). Data is shown in semilogarithmic scale for clarity. Red curves correspond to the “E–only” condition, and the blue curves to the “E+I” one.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0098842-g001: Neuronal network dynamics.A Top: Bright field and fluorescence images of a small region of a neuronal culture at day in vitro 12. Bright spots correspond to firing neurons. Bottom: Representative time traces of recorded fluorescence signals of 3 individual neurons. The numbers beside each trace identify the neurons on the images. Data shows, for the same neurons, the signal in recordings with only excitation active (“E”) and the signal with both excitation and inhibition active (“E+I”). B Population-averaged fluorescence signals in experiments (left) and simulations (right), illustrating the semi-quantitative matching between in vitro and in silico data. Top: excitatory-only traces (“E–only” data). For the experiments, inhibition was silenced through application of saturating concentrations of bicuculline. For the simulations, inhibitory synapses were silenced by setting their efficacy to zero. Bottom: traces for both excitation and inhibition active (“E+I” data). Network bursts appear as a fast increase of the fluorescence signal followed by a slow decay. Bursts are more frequent and display lower and more heterogeneous amplitudes in the presence of inhibitory connections. C Histogram of population-averaged fluorescence intensity for a 1 h recordings in experiments (left) and simulations (right). Data is shown in semilogarithmic scale for clarity. Red curves correspond to the “E–only” condition, and the blue curves to the “E+I” one.
Mentions: Dissociated neurons grown in vitro self-organize and connect to one another, giving rise to a spontaneously active neuronal network within a week (see Figure 1A) [24], [30], [51], [52]. About 70–80% of the grown connections are excitatory, while the remaining 20–30% are inhibitory [51]. Activity in neuronal cultures is characterized by a bursting dynamics, where the whole network is active and displays quasi-synchronous, high frequency firing within 100–200 ms windows [30]. The timing of the bursts themselves is irregular, with average inter-burst intervals on the order of 10 s in a typical preparation. Between different bursts, firing across the network has a low-frequency and can be described as asynchronous.

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