  A model of metabolic energy constraints is applied to a liquid state machine
in order to analyze its effects on network performance. It was found that, in
certain combinations of energy constraints, a significant increase in testing
accuracy emerged; an improvement of 4.25% was observed on a seizure detection
task using a digital liquid state machine while reducing overall reservoir
spiking activity by 6.9%. The accuracy improvements appear to be linked to the
energy constraints' impact on the reservoir's dynamics, as measured through
metrics such as the Lyapunov exponent and the separation of the reservoir.
