The goal of our research is to understand the algorithms the brain uses to learn.
A fundamental feature of our neural circuits is their plasticity, or ability to change. How does the brain use this plasticity to tune its own performance? What are the learning rules that determine whether a neural circuit changes in response to a given experience, and which specific neurons or synapses are altered?
We know that the patterns of activity in the pre- and postsynaptic neurons can control the induction of synaptic plasticity, and that coincident or nearly coincident activity is often a potent trigger for plasticity. However, this has not brought us to the point of being able to predict which synapses within a circuit will change and which will not change in a behaving animal undergoing learning, or how different experiences induce different patterns of changes within a given circuit. Our goal is to develop this more sophisticated understanding of the neural learning rules operating in vivo.