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Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity.

Zaytsev YV, Morrison A, Deger M - J Comput Neurosci (2015)

Bottom Line: Previous methods, including those of the same class, did not allow recurrent networks of that order of magnitude to be reconstructed due to prohibitive computational cost and numerical instabilities.Finally, we successfully reconstruct the connectivity of a hidden synfire chain that is embedded in a random network, which requires clustering of the network connectivity to reveal the synfire groups.Our results demonstrate how synaptic connectivity could potentially be inferred from large-scale parallel spike train recordings.

View Article: PubMed Central - PubMed

Affiliation: Simulation Laboratory Neuroscience - Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich Research Center, Jülich, Germany, yury@zaytsev.net.

ABSTRACT
Dynamics and function of neuronal networks are determined by their synaptic connectivity. Current experimental methods to analyze synaptic network structure on the cellular level, however, cover only small fractions of functional neuronal circuits, typically without a simultaneous record of neuronal spiking activity. Here we present a method for the reconstruction of large recurrent neuronal networks from thousands of parallel spike train recordings. We employ maximum likelihood estimation of a generalized linear model of the spiking activity in continuous time. For this model the point process likelihood is concave, such that a global optimum of the parameters can be obtained by gradient ascent. Previous methods, including those of the same class, did not allow recurrent networks of that order of magnitude to be reconstructed due to prohibitive computational cost and numerical instabilities. We describe a minimal model that is optimized for large networks and an efficient scheme for its parallelized numerical optimization on generic computing clusters. For a simulated balanced random network of 1000 neurons, synaptic connectivity is recovered with a misclassification error rate of less than 1 % under ideal conditions. We show that the error rate remains low in a series of example cases under progressively less ideal conditions. Finally, we successfully reconstruct the connectivity of a hidden synfire chain that is embedded in a random network, which requires clustering of the network connectivity to reveal the synfire groups. Our results demonstrate how synaptic connectivity could potentially be inferred from large-scale parallel spike train recordings.

No MeSH data available.


Schematic representation of the method validation setting. A test neural network is set up using known ground truth connectivity matrix (left panel) and its dynamics is numerically simulated. The emerging spiking activity of the neurons (middle panel) is recorded and fed into the reconstruction procedure. The resulting connectivity matrix (right panel) is then compared to the original one to assess the performance of the proposed technique. Insets show a zoom-in of the connectivity matrix, as indicated
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Fig2: Schematic representation of the method validation setting. A test neural network is set up using known ground truth connectivity matrix (left panel) and its dynamics is numerically simulated. The emerging spiking activity of the neurons (middle panel) is recorded and fed into the reconstruction procedure. The resulting connectivity matrix (right panel) is then compared to the original one to assess the performance of the proposed technique. Insets show a zoom-in of the connectivity matrix, as indicated

Mentions: We quantified the effectiveness of our suggested method by performing a series of experiments as illustrated in Fig. 2. In these experiments we simulated neuronal networks with known (ground truth) connectivity, and reconstructed the synaptic weight matrix along with the model parameters of these networks on the basis of the recorded spike times. In this way, estimation results could be readily compared to the original connectivity matrix and model parameters. All simulations presented in this section were carried out with the NEural Simulation Tool (NEST) (Gewaltig and Diesmann 2007) and reconstructions were performed using the CPU implementation of the MLE optimizer as described in Section 2. Although the connectivity is sparse in all experiments considered below, we generally use MLE optimization here; only in Section 3.3, which describes the most difficult of the experiments, we also use regularization in order to demonstrate that our computational framework can handle regularized optimization.Fig. 2


Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity.

Zaytsev YV, Morrison A, Deger M - J Comput Neurosci (2015)

Schematic representation of the method validation setting. A test neural network is set up using known ground truth connectivity matrix (left panel) and its dynamics is numerically simulated. The emerging spiking activity of the neurons (middle panel) is recorded and fed into the reconstruction procedure. The resulting connectivity matrix (right panel) is then compared to the original one to assess the performance of the proposed technique. Insets show a zoom-in of the connectivity matrix, as indicated
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4493949&req=5

Fig2: Schematic representation of the method validation setting. A test neural network is set up using known ground truth connectivity matrix (left panel) and its dynamics is numerically simulated. The emerging spiking activity of the neurons (middle panel) is recorded and fed into the reconstruction procedure. The resulting connectivity matrix (right panel) is then compared to the original one to assess the performance of the proposed technique. Insets show a zoom-in of the connectivity matrix, as indicated
Mentions: We quantified the effectiveness of our suggested method by performing a series of experiments as illustrated in Fig. 2. In these experiments we simulated neuronal networks with known (ground truth) connectivity, and reconstructed the synaptic weight matrix along with the model parameters of these networks on the basis of the recorded spike times. In this way, estimation results could be readily compared to the original connectivity matrix and model parameters. All simulations presented in this section were carried out with the NEural Simulation Tool (NEST) (Gewaltig and Diesmann 2007) and reconstructions were performed using the CPU implementation of the MLE optimizer as described in Section 2. Although the connectivity is sparse in all experiments considered below, we generally use MLE optimization here; only in Section 3.3, which describes the most difficult of the experiments, we also use regularization in order to demonstrate that our computational framework can handle regularized optimization.Fig. 2

Bottom Line: Previous methods, including those of the same class, did not allow recurrent networks of that order of magnitude to be reconstructed due to prohibitive computational cost and numerical instabilities.Finally, we successfully reconstruct the connectivity of a hidden synfire chain that is embedded in a random network, which requires clustering of the network connectivity to reveal the synfire groups.Our results demonstrate how synaptic connectivity could potentially be inferred from large-scale parallel spike train recordings.

View Article: PubMed Central - PubMed

Affiliation: Simulation Laboratory Neuroscience - Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich Research Center, Jülich, Germany, yury@zaytsev.net.

ABSTRACT
Dynamics and function of neuronal networks are determined by their synaptic connectivity. Current experimental methods to analyze synaptic network structure on the cellular level, however, cover only small fractions of functional neuronal circuits, typically without a simultaneous record of neuronal spiking activity. Here we present a method for the reconstruction of large recurrent neuronal networks from thousands of parallel spike train recordings. We employ maximum likelihood estimation of a generalized linear model of the spiking activity in continuous time. For this model the point process likelihood is concave, such that a global optimum of the parameters can be obtained by gradient ascent. Previous methods, including those of the same class, did not allow recurrent networks of that order of magnitude to be reconstructed due to prohibitive computational cost and numerical instabilities. We describe a minimal model that is optimized for large networks and an efficient scheme for its parallelized numerical optimization on generic computing clusters. For a simulated balanced random network of 1000 neurons, synaptic connectivity is recovered with a misclassification error rate of less than 1 % under ideal conditions. We show that the error rate remains low in a series of example cases under progressively less ideal conditions. Finally, we successfully reconstruct the connectivity of a hidden synfire chain that is embedded in a random network, which requires clustering of the network connectivity to reveal the synfire groups. Our results demonstrate how synaptic connectivity could potentially be inferred from large-scale parallel spike train recordings.

No MeSH data available.