# Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks

arXiv: https://arxiv.org/abs/1606.07372 · HTML: https://mlsanity.com/paper/1606.07372/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2016.

## Built on by (most cited first, 2 of 2)

- [Deep and shallow data science for multi-scale optical neuroscience](https://mlsanity.com/paper/2402.08811/) (2024)
- [Automatic Neuronal Activity Segmentation in Fast Four Dimensional Spatio-Temporal Fluorescence Imaging using Bayesian Approach](https://mlsanity.com/paper/2512.19032/) (2025)

## Abstract (arXiv)

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.