DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images.

ABSTRACT: Neuronal synapses transmit electrochemical signals between cells through the coordinated action of presynaptic vesicles, ion channels, scaffolding and adapter proteins, and membrane receptors. In situ structural characterization of numerous synaptic proteins simultaneously through multiplexed imaging facilitates a bottom-up approach to synapse classification and phenotypic description. Objective automation of efficient and reliable synapse detection within these datasets is essential for the high-throughput investigation of synaptic features. Convolutional neural networks can solve this generalized problem of synapse detection, however, these architectures require large numbers of training examples to optimize their thousands of parameters. We propose DoGNet, a neural network architecture

SUBMITTER: Kulikov V 

PROVIDER: S-EPMC6533009 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature