Abstract
Synapses are submicron structures that connect and enable communication between neurons. Many forms of learning are thought to be encoded by synaptic plasticity, wherein the strength of specific synapses is regulated by modulating expression of neurotransmitter receptors. For instance, regulation of AMPA-type glutamate receptors is a central mechanism controlling the formation and recollection of long-term memories. A critical step in understanding how synaptic plasticity drives behavior is thus to directly observe, i.e., image, fluorescently labeled synapses in living tissue. However, due to their small size and incredible density - with one ~0.5 μm diameter synapse every cubic micron - accurately detecting individual synapses and segmenting each from its closely abutting neighbors is challenging. To overcome this, we trained a convolutional neural network to simultaneously detect and separate densely labeled synapses. These tools significantly increased the accuracy and scale of synapse detection, enabling segmentation of hundreds of thousands of individual synapses imaged in living mice.