1,546 research outputs found
Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks
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.Comment: 9 pages, 5 figures, 2 ancillary files; minor changes for camera-ready
version. appears in Advances in Neural Information Processing Systems 29
(NIPS 2016
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