A major goal facing organisms acting in the natural world is the selection of appropriate actions based on sensory information and prior knowledge accumulated through previous experience. Understanding how neural networks process information and control such actions requires a breakthrough both in large scale chronic data collection methods during behavioral tasks and in the development of new analysis and modeling tools that will be able to capture the dynamics and organization of such neural networks. To address key questions in the context of sensory-motor control and learning we propose a multidisciplinary approach that will synergize the expertise of the four groups involved in cellular and systems neuroscience, machine learning, signal processing, control theory and modeling. Our goal is to establish an empirically-grounded systems-level model explaining the interaction and integration within the sensory-motor system during behavioral tasks, which is consistent with the experimental data, and which provides concrete predictions for future experiments. More specifically, we intend to further our understanding on two main fronts. First, study cell type specific components that participate in the movement command and in sensory-motor error prediction. We hypothesize that layer 2-3 neurons subserve different roles from layer 5 neurons, and may be more strongly involved in error estimation rather than in control. Second, we intend to investigate whether and how the sensory and motor ends change in order to adapt to the new learned task. Here again we expect differences between the different cortical layers. Such a framework will not only provide new insights into the specific investigated system, but could be transferrable more generally to probe the structure and functionality of complex biological networks. In addition, the unique analysis methods developed and the deep understanding of biological sensory-motor systems may contribute invaluably to fields such as robotics and network control, and to the development of new prosthetics approaches within the field of Brain Computer Interfaces.
The goals of this research proposal are expected to provide novel insight on sensory-motor control as well as structural and functional plasticity processes of the cortical network during sensory-motor learning. Deep understanding of sensory-motor systems will aid in development of new treatment modalities for diseases that impair motor function, such as Parkinson's and Huntington's diseases.
| Yair, Or; Talmon, Ronen; Coifman, Ronald R et al. (2017) Reconstruction of normal forms by learning informed observation geometries from data. Proc Natl Acad Sci U S A 114:E7865-E7874 |
