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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: We describe a minimal model that is optimized for large networks and an efficient scheme for its parallelized numerical optimization on generic computing clusters.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.


Related in: MedlinePlus

Clustering of synaptic weights uncovers the synfire chain in the reconstructed connectivity matrix. a Connectivity is first clustered by the outgoing connections (columns), while trying to achieve minimal variability inside each group. The dendrogram at the top of the panel shows the hierarchy of the clusters with the relevant groups highlighted in different colors. The green cluster on the left is formed by the inhibitory neurons. The yellowcluster at the right consists of excitatory neurons that are not part of the synfire chain and so do not have strong outgoing connections. The clusters in the middle correspond to the links of the synfire chain, which coalesce as red squares in the matrix. b Clustering by incoming connections (rows) inside the yellowclusterof neurons reveals the last link of the chain. c Clustering by incoming connections inside the greencluster helps to identify the inhibitory neurons that are part of the synfire chain (thin red rectangles)
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Fig8: Clustering of synaptic weights uncovers the synfire chain in the reconstructed connectivity matrix. a Connectivity is first clustered by the outgoing connections (columns), while trying to achieve minimal variability inside each group. The dendrogram at the top of the panel shows the hierarchy of the clusters with the relevant groups highlighted in different colors. The green cluster on the left is formed by the inhibitory neurons. The yellowcluster at the right consists of excitatory neurons that are not part of the synfire chain and so do not have strong outgoing connections. The clusters in the middle correspond to the links of the synfire chain, which coalesce as red squares in the matrix. b Clustering by incoming connections (rows) inside the yellowclusterof neurons reveals the last link of the chain. c Clustering by incoming connections inside the greencluster helps to identify the inhibitory neurons that are part of the synfire chain (thin red rectangles)

Mentions: Initially, we grouped the neurons by using the outgoing synaptic weights as the measure of dissimilarity, as shown for the MLE-reconstructed connectivity in Fig. 8a. This clustering enabled us to tell excitatory and inhibitory neurons apart (smaller blueish group on the left, and larger reddish group on the right of the matrix). Additionally, in this figure, we can see eight big red squares, which represent the links of the synfire chain. In total, nine squares should be visible in the connectivity matrix for Nl=10 links, because the outgoing connections of the last link are not statistically different from those of the background neurons.Fig. 8


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

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

Clustering of synaptic weights uncovers the synfire chain in the reconstructed connectivity matrix. a Connectivity is first clustered by the outgoing connections (columns), while trying to achieve minimal variability inside each group. The dendrogram at the top of the panel shows the hierarchy of the clusters with the relevant groups highlighted in different colors. The green cluster on the left is formed by the inhibitory neurons. The yellowcluster at the right consists of excitatory neurons that are not part of the synfire chain and so do not have strong outgoing connections. The clusters in the middle correspond to the links of the synfire chain, which coalesce as red squares in the matrix. b Clustering by incoming connections (rows) inside the yellowclusterof neurons reveals the last link of the chain. c Clustering by incoming connections inside the greencluster helps to identify the inhibitory neurons that are part of the synfire chain (thin red rectangles)
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4493949&req=5

Fig8: Clustering of synaptic weights uncovers the synfire chain in the reconstructed connectivity matrix. a Connectivity is first clustered by the outgoing connections (columns), while trying to achieve minimal variability inside each group. The dendrogram at the top of the panel shows the hierarchy of the clusters with the relevant groups highlighted in different colors. The green cluster on the left is formed by the inhibitory neurons. The yellowcluster at the right consists of excitatory neurons that are not part of the synfire chain and so do not have strong outgoing connections. The clusters in the middle correspond to the links of the synfire chain, which coalesce as red squares in the matrix. b Clustering by incoming connections (rows) inside the yellowclusterof neurons reveals the last link of the chain. c Clustering by incoming connections inside the greencluster helps to identify the inhibitory neurons that are part of the synfire chain (thin red rectangles)
Mentions: Initially, we grouped the neurons by using the outgoing synaptic weights as the measure of dissimilarity, as shown for the MLE-reconstructed connectivity in Fig. 8a. This clustering enabled us to tell excitatory and inhibitory neurons apart (smaller blueish group on the left, and larger reddish group on the right of the matrix). Additionally, in this figure, we can see eight big red squares, which represent the links of the synfire chain. In total, nine squares should be visible in the connectivity matrix for Nl=10 links, because the outgoing connections of the last link are not statistically different from those of the background neurons.Fig. 8

Bottom Line: We describe a minimal model that is optimized for large networks and an efficient scheme for its parallelized numerical optimization on generic computing clusters.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.


Related in: MedlinePlus