Emerging Areas of Cognitive Neuroscience and Neurotechnologies – Stalag Earth – Part II

 Cultocracy note :

The following article includes excerpts from a 2008 report into neuroscience produced by US government advisory body the National Research Council , the paper was produced for the military & intelligence communities .

The report outlines methods that can be used by military & intelligence agencies to further their inhumane criminal enterprises & the quest for full spectrum dominance of a given population . Although this report is from 2008 these technologies have been developed for decades in secret off the books ‘black projects’ . The research & experimentation is a continuation of the MK Ultra research from the 1960’s & 1970’s , which itself was borne out of Nazi research in the same fields during WWII .

The technology is advancing , neural implants have been superceded by synthetically engineered nano particles . Nano particles can be introduced using a wide range of vectors across a broad surface area of the population . The related neural imaging methods are constantly being perfected , the wavelengths are cloaked using frequency hopping multiplexing methods , they cannot be measured accurately . The deep state organisations involved are anxious to maintain a lead over their rivals , a new arms race has emerged , the people of the world are the common enemy .

The scientific methods need to be tested , rats & guinea pigs are of no use , human subjects are required . Be under no illusion , these technologies are currently being tested on targets across the globe , both individuals & whole populations , with disastrous results for the people involved . Many of the effects are silent & subtle and will only emerge over time , maybe this is part of the plan . They represent the deepest , darkest & most destructive elements of humanity , crimes against humanity is an understatement .

MK Ultra never went away , it simply went underground .


Emerging Areas of Cognitive Neuroscience and Neurotechnologies

There should be a significant role for computing in the analysis of neuroimaging data. It is somewhat remarkable that computing has not played a larger role in neuroimaging data analysis. In many ways, the field should be one of the most important scientific drivers for high-performance computing. Imaging data sets are among the largest sets of data produced by any scientific field.

In the case of magnetic imaging, the fields that are used to stimulate natural magnetic activity in the brain are already thought to be close to the limit of human safety.
There is potential for improved electrodes that could be inserted directly into the brain to do very precise monitoring of individual neurons, but it is extremely unlikely that such technology could be used for anything other than very small doses without significantly damaging the subject.

Finding 3-1. The global scientific computing community is approaching an era in which high-end computing will, in principle, be sufficient in capacity and computational power to model the human brain.

Proteomics and Genomics :

There are additional computational methodologies that will drive fundamental understanding of how the brain works. They will be concentrated in genomics and proteomics.

The ability by scientists to decode individual genomes letter for letter has become a powerful tool for biology and neuroscience.
Because of the simple four-letter alphabet of genomics, computers can be used to make quantitative predictions about the behavior and traits of individuals based just on their genome.

Gene expression can be analyzed at the single cell level to provide insights into how neurons and glial cells respond to different physiologic signals and also to characterize regional differences in the same types of cells.

High-throughput methods permit genome-wide searches to discover genes that are uniquely expressed in brain circuits and regions that control behavior in animal model systems. In situ hybridization then permits anatomic localization of the expressed genes.

However, not all genes that are expressed are translated into proteins. The study of proteins is known as proteomics, and it provides an even closer link to the understanding of the fundamental physical basis for processes in cells.

Implications of Proteomics and Genomics Research for Neuroscience :

Probably the most obvious and talked about impact that genomics research will have on neuroscience is in the area of genetic testing.
Such screening would allow the objective identification of the differential vulnerability of people to intense stress, sleep loss, drug effects, hypoxia, and dehydration.

Proteomics provides an extremely strong scientific framework for understanding the effect of neuroenhancing pharmaceuticals.

Genomics and proteomics are already making certain aspects of imaging significantly easier. One aspect of this is the creation of transgenic mice, which have been engineered to express different fluorescent proteins in different situations. This allows the visualization of specific cellular behaviors in live animals under realistic conditions. Such research holds tremendous value for the real-time imaging of neural activity in animals that could in turn be used to understand similar neural activity in people. It also serves as evidence of the power and potential of genetic engineering.

Finally, as knowledge of thes subjects grows, it may be possible to predict much more about individual abilities, capabilities, personality characteristics, and other traits from the genome; such information may be particularly useful to the intelligence community and the military.

DISTRIBUTED HUMAN-MACHINE SYSTEMS :

Advances in neurophysiological and cognitive science research have fueled a surge of research aimed at more effectively combining human and machine capabilities.

For the sake of convenience the committee has organized this section into four discussion areas:
• Brain-machine interfaces.
• Robotic prostheses and orthotics.
• Cognitive and sensory prostheses.
• Software and robotic assistants.

Brain-Machine Interfaces :

The basis of brain-machine interfaces (BMI) is the capture of various forms of dynamically varying energy emissions from the working brain by means of functional neuroimaging devices. These devices include the electroencephalograph (EEG) and the magnetoencephalograph (MEG) for the detection of the electrical energy of working neurons.

Functional near-infrared spectroscopy (fNIRS), which uses light to measure the hemodynamic response of functional regions of the brain, and functional magnetic resonance imaging (fMRI), which uses powerful magnetic fields to detect magnetic resonance differences in blood in different areas of the brain and allows them to be correlated to differences in neuronal activity (i.e., oxygen consumption). Positron emission tomography (PET) uses a gamma ray detector that locates and records bioactive radioactive assays injected into the blood, thereby measuring the metabolism of neurons in the functional regions of the brain.

The brain is so remarkably flexible that people can, after just a few hours of feedback training, learn to activate and deactivate functional regions and to vary the brain’s electrical distribution, metabolic activity, and brain wave patterns.

A BMI takes advantage of neuroplasticity to activate and control electronic or mechanical devices (implants).

EEG and MEG scanners can record oscillation signals from the whole brain or functionally specific regions and activate a device when the subject specifically controls this activity.
Evoked potentials recorded by EEG, especially the positive deflecting waveform that occurs approximately 300 msec following an evoked potential (P300 wave), have been used to activate and even operate communications equipment .

The blood-oxygenation-level-dependent (BOLD) magnetic resonance (MR) signal and NIRs instruments measuring cortical blood flow have also been used as a BMI .
Reinforcement learning and other algorithmic techniques have been used to rapidly identify the neural signatures of intentional actions and train BMI machine learning subsystems .

Cognitive and Sensory Prostheses :

Almost 50 years ago, the concept of “man–computer symbiosis” was introduced: “The hope is that, in not too many years, human brains and computing machines will be coupled together very tightly and that the resulting partnership will think as no human brain has ever thought and process data in a way not approached by the information-handling machines we know today”.
The idea was something significantly more than a brain-machine interface—the goal was not merely to control external devices by interfacing them with the brain but to augment human cognitive and sensory abilities, directly improving human performance.

One domain where these concepts are being applied is “sensory substitution.” Specific areas of the brain (e.g., the visual cortex) receive information from specific sensory organs (e.g., the eyes) in the form of pulses carried by afferent nerves.

Sensory interfaces to an external system can consist of precisely positioned large magnetic fields (Kupers et al., 2006) or surgically implanted devices .

Because perception takes place in the brain, not at the end organ (Bach-y-Rita, 1972; Bach-y-Rita et al., 2003), the brain can reinterpret signals from specific nervous pathways (e.g., from tactile receptors) with appropriate sensory-motor feedback.
Among the widely publicized examples of sensory substitution include projects designed to allow people to see with their ears (Motluk, 2005) and with their tongue (Bach-y-Rita et al.,1998; Nelson, 2006).

The Defense Advanced Research Projects Agency’s (DARPA’s) Augmented Cognition (AugCog) program was a research effort focused on appropriately exploiting and integrating all channels of communication from agents to humans (e.g., visual, auditory, tactile) and, conversely, sensing and interpreting a wide range of physiological measures of the human being in real time so they can be used to tune assistive behavior and thus enhance joint human-machine performance.

Making up in part for a lack of “experience” on which much of human expertise is based, intelligent systems are increasingly using the Internet, now the largest repository of knowledge on the planet, to learn.


Related :

  1. Emerging Cognitive Neuroscience – Stalag Earth Part 1
  2. Emerging Cognitive Neuroscience and Related Technologies (PDF)
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This entry was posted in Psychology, Psychotronic Warfare, Science, Targeted Individuals, Uncategorized. Bookmark the permalink.

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