Before revision:  In rate models of spiking activity, the effective spiking noise suppresses chaos, severely impairing classification performance. Chaotic rate networks without effective spiking noise also show the transient performance boost. The transitions to chaos in the two models do not coincide and have qualitatively different causes. Our theory mechanistically explains these observations. These findings have several implications. 1) Discrete state networksreach optimal performance with weaker synapses; implying lower energetic costs for synaptic transmission. 2) The classification mechanism is robust to noise, compatible with fluctuations in biophysical systems . 3) Optimal performance is reached after only a single activation per participating neuron ;
After revision:  In rate models of spiking activity, the definition of rate chaos, identify two reasonable definitions, and show that for neither of them the binary network dynamics crosses a transition to rate chaos . 3) Optimal performance is reached after only a single activation per participating neuron ;