  Information inflow into a computational system is by a sequence of
information items. Cognitive computing, i.e. performing transformations along
that sequence, requires to represent item information as well as sequential
information. Among the most elementary operations is bundling, i.e. adding
items, leading to 'memory states', i.e. bundles, from which information can be
retrieved. If the bundling operation used is associative, e.g. ordinary
vector-addition, sequential information can not be represented without imposing
additional algebraic structure. A simple stochastic binary bundling rule
inspired by the stochastic summation of neuronal activities allows the
resulting memory state to represent both, item information as well as
sequential information as long as it is non-associative. The memory state
resulting from bundling together an arbitrary number of items is
non-homogeneous and has a degree of sparseness, which is controlled by the
activation threshold in summation. The bundling operation proposed allows to
build a filter in the temporal as well as in the items' domain, which can be
used to navigate the continuous inflow of information.
