A Dynamic Data-Driven Approach to Closed-Loop Neuroprosthetics Based on Multiscale Biomimetic Brain Models

Neuroprostheses or brain-machine interfaces is an emerging field which decodes signals recorded from the brain. These techniques can be used to enable people with paralysis to control a robotic arm. Closed-loop neuroprosthetics move a step further, to encoding, allowing natural limb use by writing information back into the brain via neurostimulation so that a person can feel what they are touching. This technology, which would have seemed like science fiction not many years ago, is already being tested in humans and has the potential to improve the lives of millions of people with paralysis. Additional research is ongoing to also look at applications to other brain disorders including playing information into the brain to bring about memory restoration in patients with amnesia. At the same time, the field of computational neuroscience has advanced tremendously in terms of using experimental neuroscience data to build explicit multiscale models of brain dynamics. These biologically realistic models help us to understand how changes at the molecular and cellular levels effect alterations in dynamics at local networks and brain areas. At the highest levels, they allow us to connect neural activity to theories of behavior, memory and cognition. The recent introduction of large neuroscience projects in US and EU -- Brain Research through Advancing Innovative Neurotechnologies (BRAIN) and Human Brain Project (HBP) respectively -- will provide an opportunity to rapidly gather new and more accurate data to incorporate into the multiscale models.Combining neuroprosthetics with biomimetic brain models has the potential to significantly leverage the system's performance. In our paradigm, biological brain circuits interact directly with biomimetic brain simulations, thereby employing biological mechanisms of co-adaptation and learning to achieve the functional task in a biological manner. Since both networks will speak the same spike language, activity from the real brain can be seamlessly decoded by the model, and the simulated neural patterns can be used directly to stimulate the brain. The far-reaching applications of this approach are numerous, but here we will focus on two of them. Firstly, on biomimetic brainmachine interfaces for people with motor paralysis due to spinal cord injury. Signals from the patient's brain motor regions can be used by the biomimetic model to generate naturalistic motor signals that enable fine control of a prosthetic limb. Tactile feedback from the prosthetic limb sensors can be readily converted by the biomimetic model into neural activity patterns, which provide a natural tactile sensation when played back into the brain via electrical neurostimulation. In the second scenario, the biomimetic model is used to replace and/or rehabilitate a damaged brain region, for example, by traumatic brain injury. In this case the biomimetic model can be interconnected to the remaining brain areas and tuned to reproduce the subject's healthy neural activity. It can then provide neuroprosthetic stimulation to replace information transmitted among brain areas and restore normal function. Stimulating the damaged region could also lead to rehabilitation via synaptic plasticity, potentially leading to longterm recovery of the affected area. The dynamic data-driven application system (DDDAS) approach described uses High Performance Computing (HPC) in order to simulate the multiscale neural system and complex dynamics in real time. Continuous adaptation of the model parameters, based both on the neural activity from the real brain and the sensory inputs from the environment, is crucial to achieve optimal performance of the closed-loop neuroprosthetic system.