Memory contents are believed to be stored in the efficiency of synapses in recurrent networks of the
brain. In prefrontal cortex it was found that short and long term memory is accompanied with persistent spike
rates [1,2] indicating that reentrant activities in recurrent networks reflect the content of synaptically encoded
memories [3]. It is, however, not clear which mechanisms enable synapses to sequentially accumulate
information from the stream of patterned inputs which under natural conditions enter as perturbations of the
ongoing neuronal activities. For successful incremental learning only novel input should alter specific synaptic
efficacies while previous memories should be preserved as long as network capacity is not exhausted. In other
words, synaptic learning should realise a palimpsest property with erasing the oldest memories first.
Here we demonstrate that synaptic modifications which sensitively depend on /temporal changes /of pre- and
the post-synaptic neural activity can enable such incremental learning in recurrent neuronal
networks. We investigated a realistic rate based model and found that for robust incremental learning in a
setting with sequentially presented input patterns specific adaptation mechanisms of STDP are required that
go beyond the observed synaptic changes for sequences of pre- and post-synaptic spikes [4]. Our predicted
pre- and post-synaptic adaptation of synaptic changes in response to respective rate changes are
experimentally testable and --if confirmed-- would suggest that STDP provides an unsupervised learning
mechanism particularly well suited for incremental memory acquisition by circumventing the stability-plasticity
dilemma.
