English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

C. elegans shows chemotaxis using klinokinesis where the worm senses theconcentration based on a single concentration sensor to compute theconcentration gradient to perform foraging through gradient ascent/descenttowards the target concentration followed by contour tracking. The biomimeticimplementation requires complex neurons with multiple ion channel dynamics aswell as interneurons for control. While this is a key capability of autonomousrobots, its implementation on energy-efficient neuromorphic hardware likeIntel's Loihi requires adaptation of the network to hardware-specificconstraints, which has not been achieved. In this paper, we demonstrate theadaptation of chemotaxis based on klinokinesis to Loihi by implementingnecessary neuronal dynamics with only LIF neurons as well as a completespike-based implementation of all functions e.g. Heaviside function andsubtractions. Our results show that Loihi implementation is equivalent to thesoftware counterpart on Python in terms of performance - both during foragingand contour tracking. The Loihi results are also resilient in noisyenvironments. Thus, we demonstrate a successful adaptation of chemotaxis onLoihi - which can now be combined with the rich array of SNN blocks for SNNbased complex robotic control.

Simplified Klinokinesis using Spiking Neural Networks for Resource-Constrained Navigation on the Neuromorphic Processor Loihi