This is joint work with Rob Aguilar, Noah Apthorpe, Hussein Nagree and Cyril Zhang, advised by Sebastian Seung.
We consider a dataset of 2-photon microscopy image sequences recorded through a cranial window in GCaMP transgenic mice, showing V1 neuronal activation patterns over time (through increased GFP fluorescence). Analysis of this data requires distinguishing neurons in the image focal plane from other objects, e.g. blood vessels, dendritic branches, and neurons fluorescing above and below the focal plane. The task of neuron identification and boundary detection is currently performed manually but is potentially amenable to automation through various machine learning approaches, such as SVMs or convolutional neural nets. We find that both approaches yield comparable results, and perform well enough to considerably speed up neuron labelling.