|Credit: Technology Review|
Boyden notes that over the past 20 years there has been a slew of technologies that have enabled the observation or perturbation of information in the brain.
Take functional MRI, for example, which measures blood flow changes associated with brain activity. FMRI technology is being explored for purposes as diverse as lie detection, prediction of human decision making, and assessment of language recovery after stroke.
And implanted electrical stimulators, which enable control of neural circuit activity, are borne by hundreds of thousands of people to treat conditions such as deafness, Parkinson's disease, and obsessive-compulsive disorder. In addition, new methods, such as the use of light to activate or silence specific neurons in the brain, are being widely utilized by researchers to reveal insights into how to control neural circuits to achieve therapeutically useful changes in brain dynamics. "We are entering a neurotechnology renaissance," says Boyden, "in which the toolbox for understanding the brain and engineering its functions is expanding in both scope and power at an unprecedented rate."
This toolbox has grown to the point where the strategic utilization of multiple neurotechnologies in conjunction with one another, as a system, may yield fundamental new capabilities, both scientific and clinical, beyond what they can offer alone. For example, consider a system that reads out activity from a brain circuit, computes a strategy for controlling the circuit so it enters a desired state or performs a specific computation, and then delivers information into the brain to achieve this control strategy. Such a system would enable brain computations to be guided by predefined goals set by the patient or clinician, or adaptively steered in response to the circumstances of the patient's environment or the instantaneous state of the patient's brain.Looking ahead to the future, Boyden admits that we'll need to be careful:
Some examples of this kind of "brain coprocessor" technology are under active development, such as systems that perturb the epileptic brain when a seizure is electrically observed, and prosthetics for amputees that record nerves to control artificial limbs and stimulate nerves to provide sensory feedback. Looking down the line, such system architectures might be capable of very advanced functions--providing just-in-time information to the brain of a patient with dementia to augment cognition, or sculpting the risk-taking profile of an addiction patient in the presence of stimuli that prompt cravings.
Of course, giving machines the authority to serve as proactive human coprocessors, and allowing them to capture our attention with their computed priorities, has to be considered carefully, as anyone who has lost hours due to interruption by a slew of social-network updates or search-engine alerts can attest. How can we give the human brain access to increasingly proactive coprocessing technologies without losing sight of our overarching goals? One idea is to develop and deploy metrics that allow us to evaluate the IQ of a human plus a coprocessor, working together--evaluating the performance of collaborating natural and artificial intelligences in a broad battery of problem-solving contexts. After all, humans with Internet-based brain coprocessors (e.g., laptops running Web browsers) may be more distractible if the goals include long, focused writing tasks, but they may be better at synthesizing data broadly from disparate sources; a given brain coprocessor configuration may be good for some problems but bad for others. Thinking of emerging computational technologies as brain coprocessors forces us to think about them in terms of the impacts they have on the brain, positive and negative, and importantly provides a framework for thoughtfully engineering their direct, as well as their emergent, effects.More.