论文标题
能量限制改善液态机器的性能
Energy Constraints Improve Liquid State Machine Performance
论文作者
论文摘要
代谢能量限制的模型应用于液态机器，以分析其对网络性能的影响。已经发现，在能量限制的某些组合中，测试准确性显着提高。在使用数字液态机器的癫痫发作检测任务上观察到4.25％的提高，同时将整体储层尖峰活动降低了6.9％。准确性的提高似乎与能量约束对储层动力学的影响有关，如通过Lyapunov指数和储层分离等指标来衡量。
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.
