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Automatic neuron detection in calcium imaging data using convolutional networks

Author(s): Apthorpe, NJ; Riordan, AJ; Aguilar, RE; Homann, J; Gu, Y; et al

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Abstract: 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.
Publication Date: 5-Dec-2016
Electronic Publication Date: 2016
Citation: Apthorpe, NJ, Riordan, AJ, Aguilar, RE, Homann, J, Gu, Y, Tank, DW, Seung, HS. (2016). Automatic neuron detection in calcium imaging data using convolutional networks. 3278 - 3286
Pages: 3278 - 3286
Type of Material: Conference Article
Journal/Proceeding Title: Advances in Neural Information Processing Systems
Version: Author's manuscript

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