Introduction to Mind Matter Interaction or MMI -- part 1

Mind Matter Interaction
1

Mind matter interaction is the idea that focused mental intention can affect physical events at a distance. MMI also refers to the study and development of theories and technology based on an influence of mind over material events. In contrast, Man-Machine Interaction or Brain-Machine Interaction are very different than Mind-Matter Interaction. Though the terms seem similar, Man-Machine Interaction requires physical measurement devices connected to a person to provide signals for use. There is no direct interaction of mind with a physical device.

Mind-Matter Interaction can refer to a broad range of experimental designs, such as affecting the roll of dice by mental intention or the interference pattern in a dual-slit optical experiment. Micro PK is the alteration of probabilities in measurements at the molecular or atomic level by mental intention. This usually refers to influencing the production of ones or zeros when an entropy source is sampled to produce random bits. MMI based on hardware random number generation is a primary focus because devices are relatively easy to reproduce and use in a wide variety of experiments and applications.

A fundamental component of MMI work is the hardware generator meant to respond to an influence of mind. This is sometimes called a random event generator or REG. However, a REG is just a hardware random number generator with more or less defective randomness statistics. A more advanced concept for an MMI generator is a MindEnabled® device (MED), specifically developed to be more responsive to mental influence. A MED is superior to a REG in a number of ways:

  • Theoretical modeling shows certain entropy sources are more or less responsive to mental influence. Avalanche noise in a Zener junction is usually the least responsive and thermal noise is typically more responsive. The entropy source in a MED is carefully modeled and compared with other generators to achieve the most responsive basic design.
  • REGs produce biased data that is usually “corrected” by deterministic postprocessing. That is, it is mixed with pseudorandom sequences, which only obscure the defects in entropy of the raw sequences. MEDs use only entropic bits – without deterministic postprocessing – to produce the final output. Their statistical properties are fully modeled and are unbiased by design and verified by direct testing.
  • MEDs include two types of nondeterministic processing shown theoretically and by direct testing to produce more responsive results.
  • Some generators (and indeed some online providers) use a true random generator to seed a pseudorandom generator and then produce a large number of output bits. The true entropy of each output bit is the number of input entropy bits divided by the number of output bits generated between each seeding. This may be called a REG or MMI generator, but it is extremely inferior to a MED that provides effectively full entropy in every output bit.

Early Shot Noise Based REG

Figure 1 is a block diagram of an early PEAR REG design using the Elgenco noise module, Model 3602A-15124. (Note, Elgenco is now defunct.) The entropy source is likely a Zener diode or reverse-biased base-emitter junction operating in shot noise mode (reverse breakdown less than about 5-6 volts).

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Fig. 1

Each version of PEAR REGs typically included a high-pass filter at 1 KHz and a low-pass filter around 40 KHz (provides -1 db at 20KHz), effective bandwidth = 61.26 KHz. That yields a noise voltage of 495 µV rms, or 2.97 mVp-p. Total external gain (outside the noise module) of 2 to 4 thousand converts the signal into more or less square waves for random bit sampling.

Ring Oscillator-Based MMI Generator

A MMI generator, Model MED100Kx8 (Figure 2), uses an Intel EP3C5U256C8N FPGA (large chip in center of board).

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Fig. 2

The entropy source consists of 8 generators operating at 128 Mbps for a total generation rate of 1.024 Gbps. Each generator consists of a number of rings with 141 data taps XOred together to produce 8 outputs for bias amplification. 10,201 bits, on average, are presented to random walk bias amplifiers for each internal generator stream. The resulting 8 outputs at 12,547.79 bps each are combined in a parallel in serial out combiner. The resulting total output rate is 100.38 Kbps.

2

Maybe part 2 will go into more detail, but could you please elaborate a bit more on how the oscillator rings work (you’re saying above there’s 8 in the one FPGA chip right?) and what the “data taps” are?

3

A ring oscillator is a set of buffers or gates connected in series with the output of the last gate connected to the input of the first gate. The second condition is that there is an odd number of inverting gates included in the ring. This last condition is necessary for the ring to oscillate. The oscillation frequency is 1/(2 times the gate delay times the number of gates in the ring) where the gate delay is the time it takes an electronic signal to propagate through the gate. An entropy source is constructed using a ring oscillator with its output sampled (latched periodically) by an independent sampling signal, typically at a lower frequency than the ring oscillator frequency. Noise from components in the gate transistors, mostly thermal noise, produces indeterminacy or jitter in the rise and fall times of the ring oscillator signal. That indeterminacy translates into a degree of true randomness of the sampled signal, which is taken as the random output signal. The amount of entropy or unpredictability of the output signal can be calculated from knowledge of the ring gate parameters – which are typically measured – and the number of gates in the ring, plus the frequency of the sampling signal. The equations are laid out in my papers and patents.

Each gate adds a certain amount of jitter to the total jitter in the ring. The amount increases in proportion to the square root of the number of gates in the ring since each gate contributes an independent random component to the total signal. It is possible to extract a larger amount of entropy by taking signals from more than a single output from the ring. Each connection to a gate output is a signal tap. A number of signal taps are first combined by XOring them together, and then latching them with the sampling signal. Two design constraints must be observed when using multiple signal taps: 1) the signal taps XOred together must be separated in the ring far enough so the noise signals are effectively independent, and 2) the number of signal taps combined must not be so many that the XOr output is above the operating frequency of the XOr gate. (Note, the maximum combined XOr output frequency is approximately the ring oscillator frequency times the number of taps combined.)

The entropy sources of the MindEnabled Devices are constructed of many ring oscillators, each of which include several signal taps. The 8 separate entropy sources are the result of combining many signal taps, each of which provide a certain amount of entropy. The final amount of entropy in each of the entropy sources is nearly indistinguishable from a perfect 1.0. The actual entropy is obtained from a combination of measurements and calculations using a theoretical model that shows how entropy combines through XOr gates. (Equations provided in my papers.)