Selective detection of abrupt input changes by integration of spike-frequency adaptation and synaptic depression in a computational network model.

Short-term synaptic depression (STD) and spike-frequency adaptation (SFA) are two basic physiological cortical mechanisms for reducing the system's excitability under repetitive stimulation. The computational implications of each one of these mechanisms on information processing have been studied in detail, but not so the dynamics arising from their combination in a realistic biological scenario. We show here, both experimentally with intracellular recordings from cortical slices of the ferret and computationally using a biologically realistic model of a feedforward cortical network, that STD combined with presynaptic SFA results in the resensitization of cortical synaptic efficacies in the course of sustained stimulation. This fundamental effect is then shown in the computational model to have important implications for the network response to time-varying inputs. The main findings are: (1) the addition of SFA to the model endowed with STD improves the network sensitivity to the degree of synchrony in the incoming inputs; (2) presynaptic SFA, whether slow or fast, combined with STD results in postsynaptic neurons responding briskly to abrupt changes in the presynaptic input current and ignoring sustained stimulation, much more effectively than either SFA or STD alone; (3) for slow presynaptic SFA postsynaptic responses to strong inputs decrease inversely to the input, whereas for weak input current to presynaptic neurons transient postsynaptic responses are strongly facilitated, thus enhancing the system's sensitivity for subtle changes in weak presynaptic inputs. Taken together, these results suggest that in systems designed to respond to temporal aspects of the input, SFA and STD might constitute two necessary, linked elements whose simultaneous interplay is important for the performance of the system.