  The human brain is capable of learning, memorizing, and regenerating a
panoply of temporal patterns. A neuro-dynamical mechanism called conceptors
offers a method for controlling the dynamics of a recurrent neural network by
which a variety of temporal patterns can be learned and recalled. However,
conceptors are matrices whose size scales quadratically with the number of
neurons in the recurrent neural network, hence they quickly become impractical.
In the work reported in this thesis, a variation of conceptors is introduced,
called diagonal conceptors, which are diagonal matrices, thus reducing the
computational cost drastically. It will be shown that diagonal conceptors
achieve the same accuracy as conceptors, but are slightly more unstable. This
instability can be improved, but requires further research. Nevertheless,
diagonal conceptors show to be a promising practical alternative to the
standard full matrix conceptors.
