Emergent mechanisms of evidence integration in recurrent neural networks
Fig 2
Classification accuracy over time.
Classification accuracy of supervised network under different noise levels and for different network sizes (number of hidden units). Dynamic noise (left) vs static noise (right). With dynamic noise we observe an increase in accuracy with the number of time steps the network has to classify the stimulus. For higher noise levels the network requires a longer integration time before the maximal performance is achieved.