Machine coprocessors for the brain

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We are entering a neurotechnology renaissance, in which the toolbox for understanding the brain and engineering its functions is expanding in both scope and power at an unprecedented rate. According to Ed Boyden, an Assistant Professor, Biological Engineering, and Brain and Cognitive Sciences at the MIT Media Lab talk at emTech 2010.

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.

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.

Given the ever-increasing number of brain readout and control technologies available, a generalized brain coprocessor architecture could be enabled by defining common interfaces governing how component technologies talk to one another, as well as an “operating system” that defines how the overall system works as a unified whole–analogous to the way personal computers govern the interaction of their component hard drives, memories, processors, and displays. Such a brain coprocessor platform could facilitate innovation by enabling neuroengineers to focus on neural prosthetics at an algorithmic level, much as a computer programmer can work on a computer at a conceptual level without having to plan the fate of every individual bit. In addition, if new technologies come along, e.g., a new kind of neural recording technology, they could be incorporated into a system, and in principle rapidly coupled to existing computation and perturbation methods, without requiring the heavy readaptation of those other components.

Developing such brain coprocessor architectures would take some work–in particular, it would require technologies standardized enough, or perhaps open enough, to be interoperable in a variety of combinations.

In the future, the computational module of a brain coprocessor may be powerful enough to assist in high-level human cognition or complex decision making. If we relax the definition of brain coprocessor just a bit, so as not to require direct physical access to the brain, many consumer technologies being developed today are converging upon brain coprocessor-like architectures.

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

Further Reading

Brain-Machine Interfaces and Non-pharmacological Enhancement is discussed at the University of Pennsylvania

Three lines of research are paving the way for nonpharmacologic brain enhancement. The first is brain stimulation, either by implanted devices or transcranial magnetic stimulators. The second line of research on nonpharmacologic brain enhancement involves surgery to remove or disconnect specific structures within the brain. The third line of research is on brain-machine interfaces (BMIs). Here the goals are primarily to enable information from the world to be transduced into neural activity and to enable neural activity to be transduced into information that is externally useful for communication or robotic control. Some BMIs are already in clinical use. The most common BMI is the cochlear implant, which transduces sound waves into electrical patterns that can be sensed by auditory neurons in order to restore hearing in some deaf individuals. The full potential of BMIs has only begun to be explored, primarily in research with nonhuman subjects. Memory augmentation, as well as perceptual and motor prostheses, is under study.

Future Minds: Transhumanism, Cognitive Enhancement and the Nature of Persons, Susan Schneider, University of Pennsylvania, 2008 (15 pages)

12 page pdf from 2009 Present and Future Developments in Cognitive Enhancement Technologies by Arthur Saniotis, The University of Adelaide

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