Inferring directional connectivity from point process data of multiple elements is desired in various scientific fields such as neuroscience, geography, economics, etc . Here, we propose an inference procedure for this goal based on the kinetic Ising model. The procedure is composed of two steps: (1) determination of the time-bin size for transforming the point-process data to discrete time binary data and (2) screening of relevant couplings from the estimated networks. For these, we develop simple methods based on information theory and computational statistics. Applications to data from artificial andin vitro neuronal networks show that the proposed procedure performs fairly well when identifying relevant couplings, including the discrimination of their signs, with low computational cost. These results highlight the potential utility of the kinetic Ising modelto analyze real interacting systems with event occurrences .
Recent remarkable advances in the experimental techniques have provided a background for inferring neuronal couplings from point process data that includes a great number of neurons . Here, we propose a systematic procedure for pre- and post-processing generic point process data in an objective manner, to handle data in the framework of a binary simple statistical model, the Ising or generalized McCulloch--Pitts model. The procedure involves two steps: (1) determining time-bin size for transforming the point-process data into discrete-time binary data and (2) screening relevant couplings from the estimated couplings. For the first step, we decide the optimal time-bin size by introducing the null hypothesis that all neurons would fire independently, then choosing a time-bin size so that the null hypothesis is rejected with the most strict criterion. The likelihood associated with the null hypothesis is analytically evaluated and used for the rejection process. For the second post-processing step, after a certain estimator of coupling is obtained based on the pre-processed dataset, the estimate is compared with many other estimates derived from datasets obtained by randomizing the original dataset in the time direction. We accept the original estimate as relevant only if its absolute value is sufficiently larger than them of randomized datasets. These manipulations suppress false positive couplings induced by statistical noise. We apply this inference procedure to spiking data from synthetic and in vitro neuronal networks. The results show that the proposed procedure identifies the presence/absence of synaptic couplings fairly well including their signs, for the synthetic and experimental data. In particular, the results support that we can infer the physical connections of underlying systems in favorable situations, even when using the simple statistical model .
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Inferring directional connectivity
Recent remarkable advances in the experimental techniques have provided a background for inferring neuronal couplings
R
of multiple elements is desired in various scientific fields such as neuroscience, geography, economics, etc
that includes a great number of neurons
R
an inference procedure for this goal based on the kinetic Ising
a systematic procedure for pre- and post-processing generic point process data in an objective manner, to handle data in the framework of a binary simple statistical model, the Ising or generalized McCulloch--Pitts
R
is composed of
involves
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determination of the
determining
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to discrete time
into discrete-time
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of
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networks. For these, we develop simple methods based on information theory and computational statistics. Applications to data from artificial and
D
in vitro
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neuronal networks
couplings. For the first step, we decide the optimal time-bin size by introducing the null hypothesis that all neurons would fire independently, then choosing a time-bin size so that the null hypothesis is rejected with the most strict criterion. The likelihood associated with the null hypothesis is analytically evaluated and used for the rejection process. For the second post-processing step, after a certain estimator of coupling is obtained based on the pre-processed dataset, the estimate is compared with many other estimates derived from datasets obtained by randomizing the original dataset in the time direction. We accept the original estimate as relevant only if its absolute value is sufficiently larger than them of randomized datasets. These manipulations suppress false positive couplings induced by statistical noise. We apply this inference procedure to spiking data from synthetic and in vitro neuronal networks. The results
R
performs fairly well when identifying relevant couplings, including the discrimination of
identifies the presence/absence of synaptic couplings fairly well including
R
with low computational cost. These results highlight the potential utility of the kinetic Ising modelto analyze real interacting systems with event occurrences
for the synthetic and experimental data. In particular, the results support that we can infer the physical connections of underlying systems in favorable situations, even when using the simple statistical model