Precise action potential timing is crucial in sensory acuity and motor control. Convergence of many synaptic inputs is thought to provide a means of decreasing spike-timing variability (“jitter”), but its effectiveness has never been tested in real neurons. We used the dynamic-clamp technique in mouse auditory brain stem slices to examine how convergence controls spike timing. We tested the roles of several synaptic properties that are influenced by ongoing activity in vivo: the number of active inputs ( N), their total synaptic conductance ( Gtot), and their timing, which can resemble an alpha or a Gaussian distribution. Jitter was reduced most with large N, up to a factor of over 20. Variability in N is likely to occur in vivo, but this added little jitter. Jitter reduction also depended on the timing of inputs: alpha-distributed inputs were more effective than Gaussian-distributed inputs. Furthermore, the two distributions differed in their sensitivity to synaptic conductance: for Gaussian-distributed inputs, jitter was most reduced when Gtot was 2–3 times threshold, whereas alpha-distributed inputs showed continued jitter reduction with higher Gtot. However, very high Gtot caused the postsynaptic cell to fire multiple times, particularly when the input jitter outlasted the cell's refractory period, which interfered with jitter reduction. Gtot also greatly affected the response latency, which could influence downstream computations. Changes in Gtot are likely to arise in vivo through activity-dependent changes in synaptic strength. High rates of postsynaptic activity increased the number of synaptic inputs required to evoke a postsynaptic response. Despite this, jitter was still effectively reduced, particularly in cases when this increased threshold eliminated secondary spikes. Thus these studies provide insight into how specific features of converging inputs control spike timing.
Activity-Dependent Downscaling of Subthreshold Synaptic Inputs during Slow-Wave-Sleep-like Activity In Vivo