Reconstruction of Excitatory Neuronal Connectivity via Metric Score Pooling and Regularization


Chenyang Tao, Wei Lin, Jianfeng Feng ;
Proceedings of the Neural Connectomics Workshop at ECML 2014, PMLR 46:57-66, 2015.


Unravelling the causal link of neuronal pairs has considerable impacts in neuroscience, yet it still remains a major challenge. Recent investigations in the literature show that the Generalized Transfer Entropy (GTE), derived from information theory, has a great capability of reconstructing the underlying connectomics. In this work, we first generalize the GTE to a measure called Csiszar’s Transfer Entropy (CTE). With a proper choice of the convex function, the CTE outperforms the GTE in connectomic reconstruction, especially in the synchronized bursting regime where the GTE was reported to have poor sensitivity. Akin to the ensemble learning approach, we then pool various measures to achieve cutting edge neuronal network connectomic reconstruction performance. As a final step emphasize the importance of introducing regularization schemes in the network reconstruction.

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