Advances in neuroscience ,nanotechnology and neurotechnology have necessitated discussions on the ways that such developments could be used as weapons in contexts of national security, intelligence, and defense. The concept of neuroweapons elucidates operational issues associated with human testing on a wide demographic with varieties of brain-machine interface algorithm to improve efficiency in data analysis of acquired neural data and efficiency in this data's decoding as basis for feedback. While Brain Computer Interface and Virtual Reality 5 sense interaction is most usually considered for medical application, many neurotechnologies may also be viably engaged as weapons. Such “neuroweapons” are obviously of great interest in to national security, intelligence and defense (NSID) endeavors, given both the substantial threat that these technologies pose to the defense integrity of the US and its allies, and the viability of these approaches in the US NSID armamentarium. A 2008 report entitled Emerging Cognitive Neuroscience and Related Technologies for Emergent Neurophysiological and Cognitive/Neural Science Research
summarized the state of neuroscience as relevant to the 1) potential utility for defense and intelligence applications, 2) pace of progress, 3) present limitations, and 4) threat value of such science.
First we must ask ourselves
what is a neuroweapon?
A weapon is defined as “a means of contending against another “ and “…something used to injure, defeat, or destroy”. Both definitions apply to neurotechnologies used as weapons in intelligence and/or defense scenarios. Neurotechnology can support intelligence activities by targeting information and technology infrastructures, to either enhance or deter accurate intelligence assessment, the ability to efficiently handle amassed, complex data, and human tactical or strategic efforts. The objectives for neuroweapons in a traditional defense context (e.g., combat) may be achieved by altering (i.e., either augmenting or degrading) functions of the nervous system, so as to affect cognitive, emotional and/or motor activity and capability (e.g., perception, judgment, morale, pain tolerance, or physical abilities and stamina). Many technologies (e.g., neurotropic drugs; interventional neurostimulatory devices) can be employed to produce these effects.
As implements that target, measure, interact with, or simulate nervous system function and processes, the use of neurotechnololgies as weapons are by no means a new innovation, per se.Sensory stimuli have been applied as neuroweapons: some to directly transmit excessively intense amounts of energy to be transduced by a sensory modality (e.g., sonic weaponry to incapacitate the enemy), while others cause harm by exceeding the thresholds and limits of tolerable experience by acting at the level of conscious perception
The distribution of emotionally-provocative propaganda as psychological warfare could be considered to be an indirect form of neuroweapon
Expansive consideration is important to evaluate the historicity, operational utility, and practical implications of neurotechnology-as-weapons and applications of emergent technologies on cognition and mental decompensation.. The former approaches (e.g., cognitive and computational neuroscience; neuropharmacology) could be used for more indirect (yet still neurocentric) applications, including the dis -enablement and/or enhancing of human efforts by simulating brain functions, and the classification and detection of human cognitive, emotional and motivational states to augment intelligence, counterintelligence,
Those neurotechnologies that can enhance the capabilities of the intelligence community may also be used as weapons in that they provide “…a means of contending against another” (2). Certain neurotechnologies may be particularly well suited to affect performance in, and of the intelligence community. The tasks of both human analysts and the technologies they use are becoming evermore reciprocal and inter-dependent. Without technology to pre-process and sort large quantities of complicated information, human analysts could not obtain a cohesive picture from which to draw necessary inferences about the capabilities and intentions of (friendly, neutral or hostile) intelligence targets. Neurotechnologies can be developed to manage the increasingly significant problem of the sheer volume of cyber-based communications that has threatened intelligence systems with inundation. The widespread and inexpensive use of sophisticated communication technology (e.g., social media), and difficulty of allocating resources to gather intelligence-focal “signals” over evermore increasing, non-relevant “noise” has made more coherent collection and interpretation of intelligence information a priority .
The principal neurotechnologies that can be used to bypass such "noise" are human-machine systems that are either employed singularly, or linked to networked hierarchies of sophisticated BMIs, to mediate access to, and manipulation of signal detection, processing and/or integration. Neurotechnologic innovations that are capable of processing high volume, complex datasets with “a continuous set of values and a complex set of connections,” based on an understanding of neural networks as more than mere binary switches. An analog circuit approach would address current “modeling and simulation challenges”,physiomimetic bio-computing of this is uniquely valuable .
Information systems could conceivably be conjoined so that neural mechanisms for assigning and/or detecting salience (i.e., processes involving cortical and limbic networks) may be either augmented or modeled into neurotechnologic devices for rapid and accurate detection of valid (i.e., signal vs. noise) information within visual (e.g., field sensor, satellite and UAV-obtained images) and/or auditory aspects (e.g., narratives, codes) of human (HUMINT) or signal intelligence (SIGINT. Such computational cognitive frameworks may “borrow” human capabilities, not by mimicking processes in the brain (which may not be sufficiently well understood to begin with), by modeling conceptual components of idealized neurally-modeled systems that are linked in ways that enable performance of similar — if not more rapid and advanced — neuro-cognitive functions. Moreover, Neurally-coupled hybrid systems could be developed that link computational interfaces to human neuronal activity, so as to optimize Bayesian-like predispositions to certain types of stimuli (18). This would limit input datasets to more critical features, and thereby allow more efficient (i.e., rapid and accurate) detection, observation, orientation (and decisions) by the human user. Conjoinment and reciprocity could be used to enhance the feature-detection and intelligence capacities of both (the machine and human) systems.
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