"People spent more time analyzing their data to extract activity traces than actually collecting it," says Dmitri Chklovskii, who leads the neuroscience group at the Center for Computational Biology (CCB) at the Flatiron Institute in New York City.
A breakthrough software tool called CaImAn automates this arduous process using a combination of standard computational methods and machine-learning techniques. In a paper published in the journal eLife in January, the software's creators demonstrate that CaImAn achieves near-human accuracy in detecting the locations of active neurons based on calcium imaging data.
CaImAn is the product of an effort initiated by Chklovskii within his group at CCB. He brought on Eftychios Pnevmatikakis and later Andrea Giovannucci to spearhead the project. Their aim was to help tackle the enormous datasets produced by a method called calcium imaging.