Using Force Sensors and Neural Models to Encode Tactile Stimuli as Spike-based Responses.

Tactile sensors will augment the next generation of prosthetic limbs. However, currently available sensors do not produce biologically-compatible output. This work seeks to illustrate that a force sensor combined with a bi-phasic, neural spiking algorithm, or spiking-sensor, can produce spiking patterns similar to that of the slowly adapting type I (SAI) mechanoreceptor. Experiments were conducted where first spike latency and inter-spike interval, in response to a rapidly delivered (100 ms) sustained displacement (1.1, 1.3, 1.5 mm for 5 s), were compared between the spiking-sensor and SAI recording. The results indicated that the predicted spike times were similar, in magnitude and increasing linear trend, to those observed with the SAI. Over the three displacements, average dynamic ISIs were 7.3, 4.2, 3.8 ms for the spiking-sensor and 6.2, 6.9, 4.1 ms for the SAI, while average static ISIs were 69.0, 45.2, 35.1 ms and 159.9, 69.6, 38.8 ms. The predicted first spike latencies (74.3, 73.9, 96.3 ms) lagged in comparison to those observed for the SAI (26.8, 31.7, 28.8 ms), which may be due to both the different applied force ramp-ups and the SAI’s exquisite dynamic sensitivity range and rapid response time.

触觉传感器将用于增强下一代假肢。然而，目前可用的传感器无法产生生物兼容性的输出。这项工作旨在说明，一个力传感器与一个双相神经脉冲算法（即脉冲传感器）相结合，能够产生与慢适应I型（SAI）机械感受器相似的脉冲模式。进行了一些实验，在对快速施加（100毫秒）的持续位移（1.1、1.3、1.5毫米，持续5秒）做出反应时，对脉冲传感器和SAI记录的首次脉冲潜伏期和脉冲间隔进行了比较。结果表明，预测的脉冲时间在量级和递增的线性趋势上与SAI所观察到的相似。在这三种位移情况下，脉冲传感器的平均动态脉冲间隔分别为7.3、4.2、3.8毫秒，SAI的则为6.2、6.9、4.1毫秒，而平均静态脉冲间隔分别为69.0、45.2、35.1毫秒和159.9、69.6、38.8毫秒。预测的首次脉冲潜伏期（74.3、73.9、96.3毫秒）相较于SAI所观察到的（26.8、31.7、28.8毫秒）有所滞后，这可能是由于施加力的上升速率不同以及SAI出色的动态敏感度范围和快速响应时间所致。