Project Summary Understanding how the brain stores and accesses information is one of the fundamental goals of neuroscience. This question has been addressed across many scales, spanning detailed genetic analyses of expression patterns of key molecules supporting neuronal communication to functional recordings of intact brains in behaving animals. Over the past 50 years, synaptic plasticity has emerged as a leading cellular pathway underlying learning and memory. During synaptic plasticity, dynamic regulation of AMPA-type glutamate receptors (AMPARs) bidirectionally tunes synaptic strength, leading to long-lasting changes in the efficacy of chemical communication between neurons. Synaptic plasticity is active in nearly every region of the brain and plays a role in diverse processes, from innate fear behaviors to high-level cognition and memory. Despite the prevalence and importance of synaptic plasticity, we still lack basic knowledge regarding how information is distributed in synapses across the brain during learning and behavior, and even less regarding which specific synaptic changes are necessary for long-term memory, mainly due to technical difficulties arising from the immensely complex nature of synaptic networks. Here, we present a suite of novel methodologies that breaks through these barriers. Our approach leverages transgenic labeling of endogenous synaptic proteins and in vivo two-photon microscopy to enable visualization of synaptic plasticity in real time in behaving mice. Using deep network learning, we will develop algorithms to automatically detect and track how the strength of millions of individual synapses changes during learning. This will enable exploration of circuit-specific learning mechanisms within discrete cell types and specific presynaptic inputs. Ultimately, this pioneering approach has the potential to provide an unprecedented view of synapses in behaving animals, enabling new discoveries regarding how dynamic regulation of synaptic strength encodes learning and memory.