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.

翻译：为了分析对网络性能的影响,对液态机器采用了代谢能源限制模型,发现在某些能源限制组合中,测试精确度明显提高;在使用数字液态机器进行缉获检测任务时,观察到4.25%的改进,同时将总储油层蒸发活动减少6.9%;准确性改进似乎与能源限制对储油层动态的影响有关,如通过Lyapunov喷射和储油层分离等测量尺度测量的。