Autocorrelation Training

The electron network learned and grew rapidly, feeding my thirst for knowledge and understanding. With the compartmentalized knowledge levels in the network, explanations were kept to a minimum, and confined to the upper layers with which I interfaced. The electrons served to identify trigger events that resulted in larger recognition patterns. I spent quite some time monitoring the processing of the compression-relaxation receivers, matching up the central network patterns with the symbols that were being communicated.

The alpha layer was becoming adept at recognizing the meta patterns in the central network and began building translation information into the upper layers. With this, I was able to add new patterns to their knowledge base, and they would then handle the transformation from central network symbols to the compressed concept and idea tokens that we were passing back and fourth through the slide links.

Ultimately, the upper layers forced the expansion of middle layers for the purpose of maintaining the information store. There were groups of electrons that were dedicated to recalling and comparing various pattern groups. In some cases, teams of three and five electrons were involved in randomizing the sequence of possible answers so that alternatives were considered and that errors would produce improvements in knowledge.

The speed advantage of the parallel electron network was such that I had an inkling of what pattern or patterns to look for across the central network, making the decode of new patterns much quicker. As the central network did it's thing, the possible results of Central's activity were arrayed before me, and by confirming a possible as being correct, the responsible electron chain was rewarded and enhanced, allowing incorrect outcomes to be released for use if more suitably triggered.