What a Spiking Network Learns to Forget
TL;DR for operators Making a classifier more resistant to corrupted or manipulated inputs can reduce its accuracy on ordinary ones. On CIFAR-10, Spiking Conditional Information Bottleneck (SCIB) raises accuracy under a specified iterative adversarial attack from 0.78% to 47.58%, while clean accuracy falls from 92.94% to 89.69% (four timesteps; PGD at 4/255, Section 5.4, Table 2).1 SCIB achieves this by training a spike-based neural network to retain information useful for classification while compressing additional input variation within each class, without changing its neuronal dynamics. It also leads 15 of 18 tested environmental-noise configurations, though robustness gains vary by disturbance. On CIFAR-100, clean accuracy drops by 9.07 percentage points in a four-timestep comparison. The reported 24.9% inference-energy reduction is an analytical estimate, not a hardware measurement. For edge-AI operators, the decision is whether improved robustness against expected disturbances justifies the clean-accuracy loss and whether projected computational savings translate into actual deployment benefits. ...