  We present a computational model based on the CRISP theory (Content
Representation, Intrinsic Sequences, and Pattern completion) of the hippocampus
that allows to continuously store pattern sequences online in a one-shot
fashion. Rather than storing a sequence in CA3, CA3 provides a pre-trained
sequence that is hetero-associated with the input sequence, which allows the
system to perform one-shot learning. Plasticity on a short time scale therefore
only happens in the incoming and outgoing connections of CA3. Stored sequences
can later be recalled from a single cue pattern. We identify the pattern
separation performed by subregion DG to be necessary for storing sequences that
contain correlated patterns. A design principle of the model is that we use a
single learning rule named Hebbiand-escent to train all parts of the system.
Hebbian-descent has an inherent forgetting mechanism that allows the system to
continuously memorize new patterns while forgetting early stored ones. The
model shows a plausible behavior when noisy and new patterns are presented and
has a rather high capacity of about 40% in terms of the number of neurons in
CA3. One notable property of our model is that it is capable of
`boot-strapping' (improving) itself without external input in a process we
refer to as `dreaming'. Besides artificially generated input sequences we also
show that the model works with sequences of encoded handwritten digits or
natural images. To our knowledge this is the first model of the hippocampus
that allows to store correlated pattern sequences online in a one-shot fashion
without a consolidation process, which can instantaneously be recalled later.
