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Abstract
Progress in decoding neural signals has enabled the development of interfaces that translate cortical brain activities into commands for operating robotic arms and other devices. The electrical stimulation of sensory areas provides a means to create artificial sensory information about the state of a device. Taken together, neural activity recording and microstimulation techniques allow us to embed a portion of the central nervous system within a closed-loop system, whose behavior emerges from the combined dynamical properties of its neural and artificial components. In this study we asked if it is possible to concurrently regulate this bidirectional brain-machine interaction so as to shape a desired dynamical behavior of the combined system. To this end, we followed a well-known biological pathway. In vertebrates, the communications between brain and limb mechanics are mediated by the spinal cord, which combines brain instructions with sensory information and organizes coordinated patterns of muscle forces driving the limbs along dynamically stable trajectories. We report the creation and testing of the first neural interface that emulates this sensory-motor interaction. The interface organizes a bidirectional communication between sensory and motor areas of the brain of anaesthetized rats and an external dynamical object with programmable properties. The system includes (a) a motor interface decoding signals from a motor cortical area, and (b) a sensory interface encoding the state of the external object into electrical stimuli to a somatosensory area. The interactions between brain activities and the state of the external object generate a family of trajectories converging upon a selected equilibrium point from arbitrary starting locations. Thus, the bidirectional interface establishes the possibility to specify not only a particular movement trajectory but an entire family of motions, which includes the prescribed reactions to unexpected perturbations.
Highlights
In a recent demonstration [1], Schwartz and coworkers decoded neural activities from the motor area of a monkey’s cerebral cortex to control the movement of a robotic arm
Current methodologies are based on decoding the motor intent from the recorded neural activity and transforming the extracted information into motor commands to control external devices as robotic arms
We developed a novel computational approach, based on the concept of programming dynamical behaviors trough the bi-directional sensory-motor interaction between the brain and the connected external device
Summary
In a recent demonstration [1], Schwartz and coworkers decoded neural activities from the motor area of a monkey’s cerebral cortex to control the movement of a robotic arm. The monkey learned to activate the recorded neurons and to guide the arm for transporting food to the mouth This is an undisputed milestone in Neural Engineering, highlighting the potential of neural interfaces (NIs) as a means to restore a connection with the world for people with severe paralysis. In addition to their clinical impact, NIs have the potential to revolutionize our ways to study the nervous system, by connecting live neural populations with external devices, both physical and simulated This constitutes a leap forward with respect to current paradigms, in which physiological experiments and computational analyses are carried out separately. We aimed at emulating the operation of the spinal cord, as the prime biological interface between the brain and the musculoskeletal apparatus
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