This world is one scary place! Too bad we may soon need protection from those entrusted with our care. I fear Big Brother and Crew will eventually squash us all like bugs under a shoe. The more you know about what the tyrants of the governments of this world have planned for you, the better prepared you will be.
For instance: The Brain Machine Interface (BMI).
BMI is brought to us by The Defense Advanced Research Projects Agency (DARPA) the central research and development organization for the Department of Defense (DoD). According to DARPA it manages and directs selected basic and applied research and development projects for the DoD, and pursues research and technology where risk and payoff are both very high and where success may provide dramatic advances for traditional military roles and missions.
High on DARPA’s wish list: mind-controlled battle robots, airplanes flown by mere thought, and cyborg soldiers.
Scientists have already learned to steer a rat around a room by remote control. A rat with an electrode implanted in it’s brain! A monkey has been made to control a robotic arm. DARPA tells Duke researchers, “if a monkey can control a robotic arm with his brain, then we want soldiers to control machines of war with their thoughts.” Will they use mind control on us next?
Read an article by Carl Zimmer of Popular Science magazine. Carl says, “Some monkey business in a Duke University lab suggests we’ll soon be able to move artificial limbs, control robotic soldiers, and communicate across thousands of miles — using nothing but our thoughts.” Mind over Machine The above site is Carl Zimmer’s site. The original Popular Science article is here.
More about the Brain Machine Interface: The Ultimate Remote Control by Carl Zimmer.
An Excerpt: If you don’t need a cable to transmit signals from your brain, then you aren’t limited by a cable’s reach, either. You could send those signals through the Internet to a machine thousands of miles away. You could uplink them through a satellite to a rover prowling around on Mars. Consider the possibility of electrodes implanted in the language centers of the brain, wirelessly transmitting your inner voice thousands of miles away. You might choose instead to send them to someone standing nearby with electrodes implanted in his or her hearing centers. Telepathy, anyone? Or, if you take a bleaker view of the future, mind control? To read the full article click HERE
Frankensteins in the Pentagon: DARPA’s Creepy Bioengineering ProgramBy Cheryl Seal. Interestingly enough News Insider took down Cheryl Seal’s article but the above link still has a copy.
You can also read a little more about it at Space Plan at Duke on the Duke University site.
Also there is a good article at Science Daily about the Monkey Business going on at Duke. Read about it at Science Daily
The original press release from Duke on BMI research Read it at Duke’s BMI press release
DARPA already gave away $24 million to various brain-machine research efforts across the United States, the Duke group among them.BMI Research at Duke University
What they said about it: Duke University, Office of Sponsored Programs, Durham, N.C., is being awarded a $7,773,582 cost reimbursement no fee contract for research and development for Closed Loop Brain-Machine Interface for Augmenting Motor Performance in support of the Defense Advanced Research Projects Agency (DARPA) Defense Science Office (DSO) in the area of Brain-Machine Interfaces (BMI).
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There is more on this via TacticalWarfighterGear.com through their project horizon research.
LOGICAL EXTRACTION OF NEO-CORTEX STRUCTURE
I do not understand why the neocortex is a mystery to everyone. Its neuron net circuit is repeated throughout the cortex. It consists of excitatory and inhibitory neurons whose functions, each, have been known for decades. The neuron net circuit is repeated over layers whose axonal outputs feed on as inputs to other layers. The neurons of each layer, each receive axonal inputs from one or more sending layers and all that they can do is correlate the axonal input stimulus pattern with their axonal connection pattern from those inputs and produce an output frequency related to the resultant psps. Axonal growth toward a neuron is definitely the mechanism for permanent memory formation and it is just what is needed to implement conditioned reflex learning. This axonal growth must be under the control of the glial cells and must be a function of the signals surrounding the neurons.
The cortex is known to be able to do pattern recognition and the correlation between an axonal input stimulus and an axonal input connection pattern is just what is needed to do pattern recognition. However, pattern recognition needs normalized correlations and a means to compare these correlations so that the largest correlation is recognized by the neurons. Without normalization, the psps relative values would not be bounded properly and could not be used to determine the best pattern match. In order to get psps to be compared so that the maximum psp neuron would fire, the inhibitory neuron is needed. By having a group of excitatory neurons feed an inhibitory neuron that feeds back inhibitory axonal signals to those excitatory neurons, one is able to have the psps of the excitatory neurons compared, with the neuron with the largest psps firing before the other do as the inhibitory signal decays after each excitatory stimulus, thus inhibiting the other excitatory neurons with the smaller psps. This inhibitory neuron is needed in order to achieve psp comparisons, no question about it. For a meaningful comparison, the psps must be normalized. As unlikely as it may seem possible, it comes out that the inhibitory connections growing by the same rules as excitatory connections, grow to a value which accomplishes the normalization. That is, as the excitatory axon pattern grows via conditioned reflex rules, the inhibitory axon to each excitatory neuron grows to a value equal to the square root of the sum of the squares of the excitatory connections. This can be shown by a mathematical analysis of a group of mutually inhibiting neurons under conditioned reflex learning. This normalization does not require the neurons to behave different from as known for decades, but rather requires that they interact with an inhibitory neuron as described.
Thus, by simply having the inhibitory neurons receive from neighboring excitatory neuron with large connection strengths where if the excitatory neuron fires, the inhibitory neuron fires and by allowing the inhibitory axonal signals be included with the excitatory axonal input signals to the inputs to those excitatory neurons, the neo-cortex is able to do normalized conditioned reflex pattern recognition as its basic function.
If one thinks about it, layers of mutually inhibiting groups of neurons are all that are needed to explain the neo-cortex functions. The layers of neurons are able to exhibit conditioned reflex behavior between sub-patterns, generating new learned behaviors as observed by the human. With layer to layer feedback, multi-stable behavior of layers of neurons results, forming short term memory patterns that become part of the stimulus to other neurons. With normalized correlations, there is always an axonal input stimulus pattern that will excite every excitatory neuron.
The only way to prove this cortex model is to build a simulator, modeling large nets of neurons and observing human behaviors. Most certainly we will never be able to measure the neuron nets of the cortex due to their small sizes. This means, that projects must be formed that do these simulations and do not waste R&D efforts to try to measure properties of the cortex as the main means to understand the cortex. Certainly the area to area connection scheme is needed, but it likely can be varied, still with intelligence being exhibited. Trials will be needed to determine the initial connection strengths when initiating the simulator. These connections will need to be simple such as non-zero between corresponding neurons of the mutually inhibiting groups.
Axon growth toward pulsing neurons is the likely mechanism for memory alteration. Having neuron axons back away from neurons has no physical basis and it is well known that the number of axons increases with age in the human. Certainly axon connection strengths never become proportional to axon pulsing frequencies, otherwise the nets of neurons will never exhibit permanent past memories, but rather be a function of recent events, only. Glial cells are likely participants to axonal growth control. It is likely that they will inhibit axonal growth physically, unless a chemical falls below a concentration. In particular, this would be when the excitatory stimulus (chemically emitted to a neuron by axons to that neuron) to a cell, falls below a critical level, where the correlation between stimulus and connection pattern falls below a limit. The result of such a rule is that learning would only occur if stimulus patterns are new and don’t match the connection patterns on neurons. The psychological effect would be a curiosity behavior, observed in humans. Also, it would result in old age reduction of ability to learn, also observed in humans.
Progress in understanding how the brain works has been basically non-existent over the last 40 years due to limits in measurement. Progress requires simulation to work out the missing details. I predict that simulation will dominate the future efforts of researchers.
Also, I predict that special purpose hardware will dominate the approach. Using conventional computers to simulate nets of neurons in real-time will go out of style very soon.
Simulation permits an evolution process to arrive upon a successful brain understanding. If a logical conclusion is wrong, simulation will eliminate it. If it is right, simulation will verify it.