  The synaptic connectivity of cortex is plastic, with experience shaping the
ongoing interactions between neurons. Theoretical studies of spike
timing-dependent plasticity (STDP) have focused on either just pairs of neurons
or large-scale simulations where analytic insight is lacking. A simple account
for how fast spike time correlations affect both micro- and macroscopic network
structure remains lacking. We develop a low-dimensional mean field theory
showing how STDP gives rise to strongly coupled assemblies of neurons with
shared stimulus preferences, with the connectivity actively reinforced by spike
train correlations during spontaneous dynamics. Furthermore, the stimulus
coding by cell assemblies is actively maintained by these internally generated
spiking correlations, suggesting a new role for noise correlations in neural
coding. Assembly formation has often been associated with firing rate-based
plasticity schemes; our theory provides an alternative and complementary
framework, where temporal correlations and STDP form and actively maintain
learned structure in cortical networks.
