- Visuomotor Adaptation in Reaching
- Learning in neural circuits: applied optogenetics in nongenetic models
- Brain Reorganization and Plasticity to Accelerate Injury Recovery
- Variation as Neural Code
Even simple behaviors such as goal-directed reaching exhibit rapid and robust adaptation in response to changes in sensory feedback. These forms of learning have been extensively characterized at the behavioral level, and a variety of models have been developed to provide an intuitive understanding of these phenomena. Yet despite this progress, very little is known about how the underlying neural circuits change with learning or how sensory feedback drives these changes. This project addresses these questions, focusing on the rapid learning that occurs in response to shifted visual feedback of the arm (“visual-shift adaptation”). It has been shown that when visual feedback of the arm is displaced from the true position, for example with prism glasses, compensatory shifts are observed in visual localization (where things “look” to be) and proprioceptive localization (where the arm “feels” to be). These shifts bring the two senses back into alignment. In order to uncover the physiological mechanism behind this process, this work will investigate how vision and proprioception are normally integrated in the brain (“sensory integration”) and how that process changes with visual-shift adaptation (“sensory recalibration”). The activity of large neuronal populations will be simultaneously recorded, permitting direct comparison to existing neural models of sensory integration and recalibration.
2. Learning in neural circuits: applied optogenetics in nongenetic models (ARRA "Grand Opportunities" award, NINDS)
The use of optogenetic tools to selectively manipulate neural systems in behaving animals has the potential to revolutionize our understanding of the brain. These tools have been used in coarse fashion in non-genetic organisms, but a great deal of work is required to refine the tools to make them readily useable for the study of neural circuits and how these circuits change during learning. We propose to carry out the necessary refinement, building the tools and technology needed for applied optogenetics across three widely-used non-genetic species. This project includes a targeted program of viral development and testing and the technology development for combined optical, physiological and behavioral experiments.
3. Brain Reorganization and Plasticity to Accelerate Injury Recovery: Multi-scale and Multi-modal Models Enabled by Next Generation Neurotechnology
A key feature of sensorimotor cortical circuits is their ability to flexibly and adaptively integrate information from a variety of sources in order to perform the complex computations required for movement planning and control. The goal of this project is to develop and causally test models of how this process works. We will accomplish this goal by combining high-density cortical recordings with the patterned cortical write-in of novel “auxiliary” sensory signals.
Human and non-human primates are able to reach to visually presented targets with great accuracy and ease. Nonetheless, this fundamentally important behavior exhibits significant variability. The variability of reaching has been studied by many groups, and it plays a central role in many current theories of motor control and motor learning. Yet very disparate views have emerged regarding the origin and import of this variability. Movement variability has been seen as the product of sensory noise, as a readout of planning noise, and as the undesirable result of an unreliable motor periphery. Our hypothesis is that neural variability arises at every stage of neural processing, and that the brain not only takes such variability into account, but may even exploit it certain contexts. To address these issues, we are studying the relationship between behavioral and neural variability in visually guided reaching. This includes a detailed analysis of behavioral variability and the simultaneous recording of populations of neurons in two connected areas within the cortical sensorimotor circuit, the dorsal premotor (PMd) and primary motor (M1) cortices.