Before revision:  This is a general effect in high dimensional chaotic systems, and not specific to binary networks : We also demonstrate it in a continuous 'rate' network, a spiking LIF network, and an LSTM network. For binary and LIF networks , classification performance peaks rapidly within one activation per participating neuron, demonstrating fast event-based computation that may be exploited by biological neural systems, for which we propose testable predictions .
After revision:  This is a general effect in high dimensional chaotic systems, and not specific to binary networks aids classification in reservoir computing and optimal performance is reached within about a single activation per neuron; a fast mechanism for computation that we demonstrate also in spiking networks. A generalization of this mechanism extends to rate networks in their respective chaotic regimes .