How do brain circuits balance the need for plasticity and stability?
With billions of neurons interconnected by many billions of synapses, your brain is the most complex object in the known universe ~ far more complex than any human-built machine. Also unlike most machines your brain is constantly changing in order to adapt to a fluid environment, to store memories, and to become better able to process the kinds of sensory information it receives. It is thus quite remarkable that your brain (unlike, say, an MBTA train) works as well and as consistently as it does without any external forces to “tune it up”. Somehow our brains manage to preserve the integrity of the neural circuits that subserve behaviors over our entire lifetimes, while at the same time allowing plastic mechanisms to shape and fine-tune their function.
The Turrigiano lab studies the plasticity mechanisms that allow our brains to “tune themselves up” and remain both plastic and stable. Over the last two decades we uncovered a family of homeostatic plasticity mechanisms, including Synaptic Scaling and Intrinsic Homeostatic Plasticity, that allow neurons to adjust their own excitability to maintain stable average firing rates and preserve information flow in the face of outside perturbations. We are especially interested in understanding how homeostatic mechanisms operate within complex circuits, where they interact with classical forms of synaptic plasticity such as LTP/LTD to allow experience-dependent circuit refinement and learning. We are exploring the role of these homeostatic mechanisms in information storage and integrity during learning, by asking how these processes break down when homeostatic plasticity is impaired. Recently we found that behavioral states such as sleep and wake profoundly modulate the induction of homeostatic plasticity, and we now wish to understand how and why these brain states can orchestrate the induction of synaptic plasticity within brain circuits. Finally, we wish to understand how neurological disorders might arise from failures of self-tuning, and to determine how we might reinstate homeostatic mechanisms to improve neural circuit function.