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Title: An Adaptive Frequency Central Pattern Generator for Synthetic Nervous Systems
Generators (CPGs) are being used for controlling the complicated gaits and timing required for stable walking. Traditionally, these models are precisely designed for oscillation at a set of specific frequencies and phase relationships, which while easier to design is not conducive to robust and stable walking. In recent years, work has been done on designing adaptive models of CPGs. These CPGs are able to exhibit complex behaviors such as learning the resonant dynamics of a system to improve walking stability, as well as using mathematical learning rules to learn arbitrary signals and embed their relationships within the system. This work explores the possibility of implementing an adaptive frequency CPG with a similar behavior to these systems, using conductance-based models of dynamic non-spiking neurons connected as a synthetic nervous system (SNS).  more » « less
Award ID(s):
1704436
PAR ID:
10076340
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Lecture notes in computer science
Volume:
10928
ISSN:
1611-3349
Page Range / eLocation ID:
361-364
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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