Information encoding in an oscillatory network

Information encoding in a globally coupled network is studied. When the network is in an oscillatory state, the network activities are dominated by the intrinsic oscillatory current and the stimulus is poorly encoded. However, when the amplitude of the input signal is large, the input can still be well read from the population rate and the temporal correlation between spike trains. The underlying reason is that there exists a competition between the intrinsic correlation caused by the oscillatory current and the external correlation caused by the input signal. With small input signal, the rate code performs better than the temporal correlation code. Our results provide insights into the effects of network dynamics on neuronal computations.