PT  - JOURNAL ARTICLE
AU  - Endo, Daisuke
AU  - Kobayashi, Ryota
AU  - Bartolo, Ramon
AU  - Averbeck, Bruno B.
AU  - Sugase-Miyamoto, Yasuko
AU  - Hayashi, Kazuko
AU  - Kawano, Kenji
AU  - Richmond, Barry J.
AU  - Shinomoto, Shigeru
TI  - CoNNECT: Convolutional Neural Network for Estimating synaptic Connectivity from spike Trains
AID  - 10.1101/2020.05.05.078089
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.05.05.078089
4099  - http://biorxiv.org/content/early/2020/05/05/2020.05.05.078089.short
4100  - http://biorxiv.org/content/early/2020/05/05/2020.05.05.078089.full
AB  - The recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians, because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from neuronal spike trains by applying the generalized linear model to cross-correlograms, GLMCC. Although the GLMCC algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another algorithm using a convolutional neural network for estimating synaptic connectivity from spike trains, CoNNECT. After adaptation to very large amounts of simulated data, this algorithm robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new algorithm, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys.Competing Interest StatementThe authors have declared no competing interest.