The intelligence community (IC) faces the challenging task of analyzing extremely large amounts of information on cognitive neuroscience and neurotechnology, deciding which of that information has national security implications, and then assigning priorities for decision makers. It is also challenged to keep pace with rapid scientific advances that can only be understood through close and continuing collaboration with experts from the scientific community, from the corporate world, and from academia. The situation will become more complex as the volume of information continues to grow.

As requested by the Defense Intelligence Agency s Defense Warning Office, this book from the National Research Council:

. reviews the current state of today's work in neurophysiology and cognitive/neural science
. selects the manners in which this work could be of interest to national security professionals
. identifies trends for future warfighting applications that may warrant continued analysis and tracking by the intelligence community, and
. illustrates the ways in which neurophysiological and cognitive/neural research conducted in selected countries may affect committee assessments.

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Cognitive neuroscience and its related technologies are advancing rapidly, but the IC has only a small number of intelligence analysts with the scientific competence needed to fully grasp the significance of the advances. Not only is the pace of progress swift and interest in research high around the world, but the advances are also spreading to new areas of research, including computational biology and distributed human–machine systems with potential for military and intelligence applications. Cognitive neuroscience and neurotechnology comprise a multifaceted discipline that is flourishing on many fronts. Important research is taking place in detection of deception, neuropsychopharmacology, functional neuroimaging, computational biology, and distributed human-machine systems, among other areas. Accompanying this research are the ethical and cultural implications and considerations that will continue to emerge and will require serious thought and actions. The IC also confronts massive amounts of pseudoscientific information and journalistic oversimplification related to cognitive neuroscience. Further, important research outside of the United States in cognitive neuroscience is only just beginning and this makes it almost impossible to attempt to accurately assess the research at this point in time.

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Computational Biology Applied to Cognition, Functional Neuroimaging, Genomics, and Proteomics

Computing, which is pervasive today in the fields of neuroscience and cognition, is, broadly speaking, used there for two main purposes—analysis and modeling. It is used to analyze the enormous amounts of data from genome sequencing, ribonucleic acid (RNA) expression arrays, proteomics, and neuroimaging and to correlate them with experimental results so as to eventually understand the biology of the nervous system and of cognition. In the second purpose, modeling, it is used to express a hypothesis in concrete mathematical terms. The model is then simulated in an attempt to validate the hypothesis and/or make a prediction. Mathematical models of various dynamical qualities can be constructed and used to make predictions. Mathematical models have been used, for example, to correlate sleep and performance by measuring both and using the relationship to make a prediction. The distinction between modeling and analysis is not always clear because many types of data analysis make basic assumptions about the data fitting a specific model.

The larger issue is whether a cognitive system can be constructed in the next two decades that, while not precisely mimicking a human brain, could perform some similar tasks, especially in a particular environment. Success would be determined not by how closely the system resembled the brain in its mechanisms of action, but by the degree to which the system performed specific cognitive tasks the same way as a typical human operator. This search for what is known as artificial intelligence has for many decades been a goal of computing efforts.

Perhaps most revolutionary would be an intelligent machine that uses the Internet to train itself. Currently, the Internet is by far the closest we have come to a total database of knowledge. One can imagine an intelligent system that continuously monitors and processes not only accumulated knowledge but also public and nonpublic information on current events. Modern search engines do that in a way but serve more to catalog knowledge than to come to intelligent conclusions. However, if a system that reasoned like a human being could be achieved, there would be no limit to augmenting its capabilities. Many efforts, large and small, to reach this goal have not yet succeeded.

Key Finding (Finding 3-6). As high-performance computing becomes less expensive and more available, a country could become a world leader in cognitive neurosciences through sustained investment in the nurture of local talent and the construction of required infrastructure. Keys to allowing breakthroughs will be the development of software-based models and algorithms where much of the world is now on par with or ahead of the United States. Given the proliferation of highly skilled software researchers around the world and the relatively low cost of establishing and sustaining the necessary organizational infrastructure in many other countries, the United States cannot expect to easily maintain its technical superiority.

Key Recommendation (Recommendation 3-1). The intelligence community, in collaboration with outside experts, should develop the capability to monitor international progress and investments in computational neuroscience. Particular attention should be given to countries where software research and development is relatively inexpensive and where there exists a sizeable workforce with the appropriate education and skills.