Localized stimulation and recording in the spinal cord with microelectrode arrays

Brain Computer Interfaces in Rehabilitation Medicine

One innovation currently influencing physical medicine and rehabilitation is brain-computer interface (BCI) technology. BCI systems used for motor control record neural activity associated with thoughts, perceptions, and motor intent; decode brain signals into commands for output devices; and perform the user's intended action through an output device. BCI systems used for sensory augmentation transduce environmental stimuli into neural signals interpretable by the central nervous system. Both types of systems have potential for reducing disability by facilitating a user's interaction with the environment. Investigational BCI systems are being used in the rehabilitation setting both as neuroprostheses to replace lost function and as potential plasticity-enhancing therapy tools aimed at accelerating neurorecovery. Populations benefitting from motor and somatosensory BCI systems include those with spinal cord injury, motor neuron disease, limb amputation, and stroke. This article discusses the basic components of BCI for rehabilitation, including recording systems and locations, signal processing and translation algorithms, and external devices controlled through BCI commands. An overview of applications in motor and sensory restoration is provided, along with ethical questions and user perspectives regarding BCI technology.

Proprioceptive Feedback through a Neuromorphic Muscle Spindle Model

Connecting biologically inspired neural simulations to physical or simulated embodiments can be useful both in robotics, for the development of a new kind of bio-inspired controllers, and in neuroscience, to test detailed brain models in complete action-perception loops. The aim of this work is to develop a fully spike-based, biologically inspired mechanism for the translation of proprioceptive feedback. The translation is achieved by implementing a computational model of neural activity of type Ia and type II afferent fibers of muscle spindles, the primary source of proprioceptive information, which, in mammals is regulated through fusimotor activation and provides necessary adjustments during voluntary muscle contractions. As such, both static and dynamic γ-motoneurons activities are taken into account in the proposed model. Information from the actual proprioceptive sensors (i.e., motor encoders) is then used to simulate the spindle contraction and relaxation, and therefore drive the neural activity. To assess the feasibility of this approach, the model is implemented on the NEST spiking neural network simulator and on the SpiNNaker neuromorphic hardware platform and tested on simulated and physical robotic platforms. The results demonstrate that the model can be used in both simulated and real-time robotic applications to translate encoder values into a biologically plausible neural activity. Thus, this model provides a completely spike-based building block, suitable for neuromorphic platforms, that will enable the development of sensory-motor closed loops which could include neural simulations of areas of the central nervous system or of low-level reflexes.