Adaptation towards scale-free dynamics improves cortical stimulus discrimination at the cost of reduced detection
Fig 6
A network model with short-term depression reproduces the discrimination-detection trade-off.
(A) 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) 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. (C) 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. (D) 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. (E) A ‘foreground’ stimulus (red) is applied at two times: during the transient and later during the steady-state. (F) 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). (G, H) Consistent with our experiments, discrimination of foreground stimuli was inversely proportional to δ while detection was proportional to δ.