  Persistent activity is postulated to drive neural network plasticity and
learning. To investigate its underlying cellular mechanisms, we developed a
biophysically tractable model that explains the emergence, sustenance, and
eventual termination of short-term persistent activity. Using the model, we
reproduced the features of reverberating activity that were observed in small
(50-100 cells) networks of cultured hippocampal neurons, such as the appearance
of polysynaptic current clusters, the typical inter-cluster intervals, the
typical duration of reverberation, and the response to changes in
extra-cellular ionic composition. The model relies on action
potential-triggered residual presynaptic calcium, which we suggest plays an
important role in sustaining reverberations. We show that reverberatory
activity is maintained by enhanced asynchronous transmitter release from
pre-synaptic terminals, which in itself depends on the dynamics of residual
presynaptic calcium. Hence, asynchronous release, rather than being a "synaptic
noise", can play an important role in network dynamics. Additionally, we found
that a fast timescale synaptic depression is responsible for oscillatory
network activation during reverberations, whereas the onset of a slow timescale
depression leads to the termination of reverberation. The simplicity of our
model enabled a number of predictions that were confirmed by additional
analyses of experimental manipulations.
