Plasticity of global brain dynamics: tunable neural integration

In the last few years, powerful experimental techniques that can monitor the activity of many neurons at once have generated a new field of neuroscience — ‘neural dynamics’ — which uses sophisticated mathematical models to describe how brain activity evolves on a moment-to-moment basis. Research on how neural dynamics supports various computations has generally been conducted in a stable environment. We will study how the brain adapts, or ‘tunes,’ its dynamics to a changing environment. This distinction is crucial: If the brain cannot learn to alter its computations based on new or unexpected information, the animal is unlikely to survive. We will study how the neural dynamics controlling simple eye-movement behaviors in zebra fish and mice can be adaptively tuned to improve visual perception. Clear vision depends on the ability to hold the eyes stably on a given location when studying a feature of a stationary object or to move the eyes to accurately track moving objects. The ability to accurately hold the eyes at a given position is performed by a brain circuit called the ‘oculomotor integrator.’ We will study how the dynamics of this circuit are tuned by creating a virtual environment that simulates what an animal would see if, due to an improperly tuned oculomotor integrator, it could not accurately control the position of its eyes. We then will monitor and manipulate neurons throughout the eye-movement control regions to see how these networks adjust, or re-tune, their neural dynamics to adapt to the new environment. Finally, we will combine our data with detailed descriptions of the brain’s wiring and apply new mathematical techniques to analyze how the observed neural signals are processed and transformed by these brain circuits. By analyzing and comparing these two organisms, we can make general theories about how the brain adapts its activity to modify actions.