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**FAQ: Neural Net, Interference Net?**

**Q:***
You are talking about nerve nets, why didn't you talk about Neural Networks?
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**A:**
In the first time (1993...1997) I tried to do so. Permanent misunderstandings demanded a different name. The terminus "Neural Net" (or Neuronal Net) was occopied and burned by a different meaning and knowledge, especially learning plays the important rule.

**Q:***
What is the problem with Neural Nets?
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**A:**
The greatest challenge is, that any smallest change of the delay structure of a net destroys its function or behaviour completely (think about FIR-filters). Most of neural nets were 1993 "artificial", delays played no rule in the pioneering works. With a mismatched delay structure a model has nothing to do with the origin. So lots of interesting works disappeared in the gigantic mass of "Artificial Neural Net" (ANN-) papers, ignoring the delay structures.

**Q:***
What was the reason to introduce the name "Interference Network"?
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**A:**
An average pyramidal neuron in human cortex has 7400 synapses (input terminals), and our brain has 40 billion pyramidal neurons (the number varies in different works). The permanent state for a living nerve cell is, that all thousands synapses get unsynchronous fire, if the cell sleeps. To bring the cell to excitement (spike delivery), we need a delay-cleaned, synchronous fire at thousands of synapses. Spikes are very short - partially in the submillisecond range, while smallest nerve fibres are very slow.

The construction of such circuits and the finding of conditions, restrictions and cases opened the field of interference network research.

If a neuron in nerve system gets permanent fire from all directions, the situation is comparable with radio broadcast interferences until the 60th of the last century. Driving over land on Medium Wave (MW) people heared sometimes only noises, whisper or whistling in the car-radio.

Two places in a nerve net (for example a position of skin with a place in brain) can only communicate, if they overlay the "interferencial noise", coming from other communications. How to do this?

Thinking about synchronous fire and noise, we find optic-like communication principles (projections). We have to deal with cross- and self-interferences. Interference network research will help to understand such processes better. And we need to understand interference networks to call the right questions to nerve systems.

**Q:***
What have Interference Networks to do with Acoustic Cameras, with Radar, Sonar or optical lense systems?
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**A:**
Experts in optics speak a very different language compared with experts for Radar or Sonar. They all can not understand the neuro-surgeon. But the knowledge of all fields is the same. They all talk about probability waves, interference integrals and interference circuits.

The Acoustic Camera was the first and simplest demonstration how interference nets work, how to transform time series into images and vice versa.

**Q:***
Wave field theories are Fourier-driven. Why can't I find Fourier approaches in your papers?
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**A:**
To overlay two waves in space-time, in the one-dimensional case we use convolution or cross-correlation. If we have very long time functions (lets say with 1 million samples each) the calculation is very time consuming. Using sinoidal waves, a shorter way is, to transform the time series into frequency series using a Fourier transformation. Finally you have to add the Fourier coefficients instead to multiply the partial products of convolutions. And you can reduce the problem size, if you use for example only 1024 coefficients. So "sinoidal field theories" are married with Fourier-transformation.

In case of interference networks correct delays play the most important rule. And the spike-like nature of time functions is the worst case for Fourier analysis (the spectrum of a spike is white noise).
And be it as it is - with Fourier analysis it is nearly impossible, to avoid strong delay mistakes. Last not least educational aims inspired me, to avoid the frequency domain and to stay consequently in time domain. Things are complicate enough. And lots of other scientists will do some further works.

Or as Albert Einstein said: "Any intelligent fool can make things bigger, more complex, and more violent!"

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Thanks to the Daniel Herfert group for this short interview.
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