  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.
