Correlations in binary networks with oscillating input

Experiments showed that excess synchronous spike events are lockedto the phase of LFP beta-oscillations more strongly than spikes notpart of such events [Denker et al. 2011, Cereb. Cortex]. To identify themechanisms by which correlations depend on the phase of the LFP,which primarily reflects input activity, we examine a balanced networkof homogeneously connected binary model neurons [Ginzburg etal. 1994, PRE] receiving input from a sinusoidal perturbation. TheGlauber dynamics of the network is simulated and approximated bymean-field theory. Treating the periodic input in linear response theory,the cyclostationary first two moments are analytically computed.They agree with their simulated counterparts over a wide parameterrange. The zero-time lag correlations consist of two terms, one dueto the modulated susceptibility (via the external input and networkfeedback) and one due to the time-varying autocorrelations. For someparameters, this leads to resonant correlations and non-resonant meanactivities. Our results can help to answer the salient question how oscillationsin mesoscopic signals and spike correlations interact.Supported by the Helmholtz foundation (VH-NG-1028, SMHB); EUGrant 604102 (HBP). Simulations with NEST (nest-simulator.org)