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Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks
Noah Apthorpe · Alexander Riordan · Robert Aguilar · Jan Homann · Yi Gu · David Tank · H. Sebastian Seung

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #37

Calcium imaging is an important technique for monitoring the activity of thousands of neurons simultaneously. As calcium imaging datasets grow in size, automated detection of individual neurons is becoming important. Here we apply a supervised learning approach to this problem and show that convolutional networks can achieve near-human accuracy and superhuman speed. Accuracy is superior to the popular PCA/ICA method based on precision and recall relative to ground truth annotation by a human expert. These results suggest that convolutional networks are an efficient and flexible tool for the analysis of large-scale calcium imaging data.

Author Information

Noah Apthorpe (Princeton University)
Alexander Riordan (Princeton University)
Robert Aguilar (Princeton University)
Jan Homann (Princeton University)
Yi Gu (Princeton University)
David Tank (Princeton University)
H. Sebastian Seung (Princeton University)

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