C. elegans shows chemotaxis using klinokinesis where the worm senses the concentration based on a single concentration sensor to compute the concentration gradient to perform foraging through gradient ascent/descent towards the target concentration followed by contour tracking. The biomimetic implementation requires complex neurons with multiple ion channel dynamics as well as interneurons for control. While this is a key capability of autonomous robots, its implementation on energy-efficient neuromorphic hardware like Intel's Loihi requires adaptation of the network to hardware-specific constraints, which has not been achieved. In this paper, we demonstrate the adaptation of chemotaxis based on klinokinesis to Loihi by implementing necessary neuronal dynamics with only LIF neurons as well as a complete spike-based implementation of all functions e.g. Heaviside function and subtractions. Our results show that Loihi implementation is equivalent to the software counterpart on Python in terms of performance - both during foraging and contour tracking. The Loihi results are also resilient in noisy environments. Thus, we demonstrate a successful adaptation of chemotaxis on Loihi - which can now be combined with the rich array of SNN blocks for SNN based complex robotic control.

中文翻译：

---

使用尖峰神经网络在神经形态处理器Loihi上进行资源受限导航的简化Klinokinesis

秀丽隐杆线虫显示使用克林克病的趋化性，其中蠕虫基于单个浓度传感器感测浓度以计算浓度梯度，以通过梯度上升/下降朝向目标浓度执行觅食，然后进行轮廓跟踪。仿生实施需要具有多个离子通道动力学的复杂神经元以及用于控制的中间神经元。尽管这是自主机器人的一项关键功能，但要在节能的神经形态硬件（如英特尔的Loihi）上实现该技术，则需要使网络适应硬件特定的约束，但这尚未实现。在本文中，我们通过仅用LIF神经元执行必要的神经元动力学以及所有功能（例如Heaviside功能和减法）的完全基于尖峰的实现，就证明了基于klinokinesis对Loihi趋化的适应。我们的结果表明，就觅食和轮廓跟踪而言，Loihi的实现在性能方面与Python上的软件等效。Loihi结果在嘈杂的环境中也具有弹性。因此，我们展示了对Loihi的趋化性的成功适应-现在可以与基于SNN的复杂机器人控制的SNN块的丰富数组结合使用。Loihi结果在嘈杂的环境中也具有弹性。因此，我们展示了对Loihi的趋化性的成功适应-现在可以与基于SNN的复杂机器人控制的SNN块的丰富数组结合使用。Loihi结果在嘈杂的环境中也具有弹性。因此，我们展示了对Loihi的趋化性的成功适应-现在可以与基于SNN的复杂机器人控制的SNN块的丰富数组结合使用。