Repairing lesions via kernel adaptive inverse control in a biomimetic model of sensorimotor cortex
In this paper we propose a kernel adaptive filtering (KAF) approach to repairing lesions via microstimulation in a biomimetic spiking neural network of sensorimotor cortex. The fundamental challenge of designing neuroprosthetics and brain machine interfaces (BMIs) is the decoding of electrical activity of neurons and behavior. For injured or damaged brain, intracranial stimulation has the potential to modulate neural activity to match meaningful and natural response or behavior. In order to optimize the microstimulation sequences, we construct an inverse model of the target system. However, to obtain sufficient learning data, the neural system must be stimulated or probed extensively. For real brains, this is especially challenging and often unfeasible. Here, we demonstrate that by applying KAF to a biomimetic brain and realistic virtual musculoskeletal model, we can repair simulated lesion and drive a virtual arm to perform the correct motor task.