Co-adaptive Learning for Sensorimotor Therapy
Ranu Jung
“The most important trend in recent technological developments may be that technology is increasingly integrated with biological systems. Many of the critical advances that are emerging can be attributed to the interactions between the biological systems and the technology. The integration of technology with biology makes us more productive in the workplace, makes medical devices more effective, and makes our entertainment systems more engaging. Our lives change as biology and technology merge to form biohybrid systems. ……. Some of the key developments in biohybrid systems have been in opening lines of communication between the engineered and the biological systems.” From “Merging Technology with Biology” inBiohybrid Systems: Nerves, Interfaces, and Machines, ed. Ranu Jung, 2011 Wiley-VCH Verlag GmbH & Co. KGaA.
In 2007, a workshop was held under the National Academies Keck Futures Initiative, “Smart Prosthetics: Exploring Assistive Devices for the Body and Mind.” Some of the initial challenges identified were the feasibility of developing smart prosthetic systems that surpass the modality of replacing lost function and go to those that promote repair of neural function by harnessing the activity dependent plasticity in the nervous system.
The paragraphs above present the broad challenges for developing a paradigm of co-adaptive learning for sensorimotor therapy directed at promoting repair or recovery after neurotrauma or neurological disability by enhancing plasticity in the nervous system. The therapy may also help promote healthy aging and prevent or postpone neurological decline.
Perhaps the biggest success story for use of technology to replace lost sensory function has been that of the cochlear implant, By the end of 2010, over 200,000 people had received a cochlear implant with more than 40,000 adults and 28,000 children in the US alone. While initially this implant was designed as a sensory prosthesis to replace lost auditory function, more recently the ability of cochlear implants to promote brain plasticity has become evident.
The enriched sensory environment provided by the device can help to promote plasticity, especially in the developing brain. With existing cochlear implant technology, the system settings are set by a therapist and then manually adjusted periodically to accommodate the changes in the biological system. If the device were to automatically adjust its settings, could speech comprehension improve? That is, could performance be improved if the systems were co-adaptive?
Neural stimulation devices are also being used in other situations to promote plasticity in a more explicit manner. In the weeks/months after traumatic injury such as a stroke or incomplete spinal cord injury, current rehabilitation practice seeks to engage mechanisms of activity-dependent plasticity to maximize functional gains, Electrical activation of sensorimotor circuits can produce activity in structures targeted for adaptation. In this situation, the device is less concerned with the specific task at hand (e.g. taking a step or picking up a cup) and is more concerned with promoting the plasticity required for device-independent function. For this application, once again existing technology requires manual adjustment by a therapist. If the device (stimulator or robot) were to automatically adjust its settings, could motor learning improve?
Challenges:
A primary challenge is to design biohybridsystems that can access and capture the biosignatures of the living system through limited spatiotemporal sampling and interface with the nervous system through sparse inputs. Given the sparseness of any existing or foreseeable interface, we must maximize our ability to interpret data and our ability to alter activity patterns.
A second challenge is to make the biohybrid system co-adaptive. To promote plasticity, the challenge is to influence the core bio-chemical machinery in a desired manner. In this design, it is important to recognize that interfaces that influence the nervous system at one scale (e.g. molecular) and location effect changes across other scales and locations. This ill-defined objective in promoting biological plasticity presents major challenges to endowing the technology with effective and efficientadaptive capabilities.