This project aims to develop new statistical tools for understanding how synapses between neurons change over time. Synaptic changes are the basis for flexible information processing, learning, memory, and recovery from injury in the brain, but, since neural activity is highly variable, studying synaptic changes in behaving animals is a major challenge. The research described in this proposal will address this challenge by developing novel statistical methods that infer changes in synaptic strength based on neural spiking activity. In parallel with the research plan, this project will also implement a set of tutorials and workshops to improve the training of undergraduate and graduate students in neural data analysis. These resources aim to improve the reliability and reproducibility of neuroscience research and better prepare experimental neuroscientists to contribute to and benefit from large-scale data and code sharing.
The proposed research will develop a statistical model-based framework that allows time-varying synaptic weights to be inferred from spiking data alone and apply these methods to characterize plasticity on multiple timescales in vivo. Through close collaboration with experimentalists, the models will first be validated and refined on isolated, well studied neural systems with known connectivity. The models will then be extended to address the additional sources of variability in larger populations of neurons with unknown connectivity. This framework will be applied to determine 1) how synaptic plasticity interacts with brain state and 2) how short- and long-term plasticity interact in vivo. By using multi-electrode spike recordings, the statistical tools developed here will allow synaptic plasticity to be quantified on a larger scale than previously possible and will allow new comparisons of plasticity across cell types, brain areas, and behaviors. By shedding light on how synapses change over time, this work may ultimately lead to a better understanding of disease and injury and advance the development of neurally-inspired technologies.
