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line, and not to cross the threshold to a living being, because the current ethical maturity

of humans is not sufficient for this. “Conscious machines” are also in principle uncontrol­

lable and risky. Fortunately, however, we are relatively far away from this in bioinformatic

modelling because of a number of breakthroughs that are still necessary. Nevertheless, it

is advisable to take great care already during the design phase (for example, of increas­

ingly powerful Internet tools or increasingly autonomous weapon systems) to ensure that

the design prevents the worst-case scenario (the greatest accident that can be assumed),

namely the autonomous machine with consciousness or superior intelligence that begins

to control or kill humans, from the outset.

Conclusion

• Our brain is given the ability to process information very well due to its modular

design. Our genome encodes different proteins that lead to different activating

and inhibiting nerve cell connections (synapses) in numerous different nerve

cells, depending on the cell type. Nerve cell associations thus have new proper­

ties (emergence). In particular, our brain is particularly good at recognizing pat­

terns. Human nerve cell associations are trained in the same way as neuronal

networks in computers (see previous chapter). Training and practice strengthen

or erase synapses. Practice thus optimizes learning success over time. There are

so many nerve connections in the associative regions of our cerebrum that it

becomes advantageous to process information in an integrated rather than local­

ized manner. Interference patterns similar to a hologram are created.

• We describe with our own current simulations that environmental stimuli, but

also one’s own position as well as one’s own actions can be encoded in a holo­

gram for all neurons participating in the pattern equally and simultaneously. Such

new emergent effects in our particularly complex brain presumably underlie our

consciousness (“fulguration” according to Konrad Lorenz). However, bioinfor­

matics already makes important contributions to neurobiology by decoding and

describing coded molecular signals at all levels. First of all, this concerns genetic

factors of neuronal maturation and diseases, which can be elucidated with the

help of the OMIM database, genome and transcriptome analyses. At the level of

the nerve cell, protein structures, in particular receptors and their activation, can

be described in detail using protein structure analyses, molecular dynamics and

databases (e.g. DrumPID, PDB database), as well as underlying cellular net­

works, protein-protein interactions and signalling cascades involved.

• Brain blueprints, so-called connectomes, are already available for C. elegans and

are being intensively developed for other model organisms and humans. A connec­

tome contains computer-readable information on how each nerve cell is linked to

another and which receptors and ion channels play a role in this process. Suitable

programming languages allow the direct simulation of information processing in

the brain, especially for C. elegans. Numerous special software are available for

clinical evaluations (EEG, computer tomograms) (“medical informatics”), but also

for neurobiological experiments (e.g. a neuronal activity detection tool).

15.4  Possible Objectives