A network model with short-term depression reproduces the discrimination-detection trade-off.

<p>(<b>A</b>) The model network is driven by a slowly varying ‘background’ stimulus that turns on at t = 500 timesteps. We interpret 1 time step to be approximately 1 ms. (<b>B</b>) Avalanches with a tendency for very large sizes occur during the transient following stimulus onset (blue). Smaller avalanches occur during the baseline period (green), which includes the later time period labeled ‘steady-state’ (orange). Avalanches are overlaid from 80 repetitions of the same background stimulus. (<b>C</b>) Synapse strength (averaged over all except the input synapses) drops at stimulus onset and fluctuates due to short term depression. Gray lines indicate single trials (n = 80); black line is the cross-trial average. (<b>D</b>) Avalanches are distributed according to a power-law during the baseline (green) and have a high likelihood of very large avalanches during the transient (blue). Inset: cumulative distributions of the same data reveal that δ>0 for the transient. (<b>E</b>) A ‘foreground’ stimulus (red) is applied at two times: during the transient and later during the steady-state. (<b>F</b>) Raster of model spikes (from all neurons, subsampled) including 80 trials, broken into four blocks of 20, each with a different intensity of foreground stimulus. Response was defined as the spike count during the transient (blue) or the steady-state (orange). (<b>G, H</b>) Consistent with our experiments, discrimination of foreground stimuli was inversely proportional to δ while detection was proportional to δ.