TY - JOUR
T1 - Online learning for orientation estimation during translation in an insect ring attractor network
AU - Robinson, Brian S.
AU - Norman-Tenazas, Raphael
AU - Cervantes, Martha
AU - Symonette, Danilo
AU - Johnson, Erik C.
AU - Joyce, Justin
AU - Rivlin, Patricia K.
AU - Hwang, Grace
AU - Zhang, Kechen
AU - Gray-Roncal, William
N1 - Funding Information:
This project received funding under DARPA Grant HR00111990038 and internal grants from the Johns Hopkins University Applied Physics Laboratory. KZ is also supported by NIH Grant U01NS111695.
Funding Information:
We would like to thank Michael Wolmetz and Joan Hoffmann for their insightful review and discussion in developing these experiments. We additionally would like to thank Kensei Suzuki, Lauren Diaz, Andres Perez-Doval, and Sonia Albert for their assistance with robotic translation, and the support of the JHU/APL CIRCUIT and ASPIRE programs. This material is based upon work supported by (while GH was serving at) the National Science Foundation. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - Insect neural systems are a promising source of inspiration for new navigation algorithms, especially on low size, weight, and power platforms. There have been unprecedented recent neuroscience breakthroughs with Drosophila in behavioral and neural imaging experiments as well as the mapping of detailed connectivity of neural structures. General mechanisms for learning orientation in the central complex (CX) of Drosophila have been investigated previously; however, it is unclear how these underlying mechanisms extend to cases where there is translation through an environment (beyond only rotation), which is critical for navigation in robotic systems. Here, we develop a CX neural connectivity-constrained model that performs sensor fusion, as well as unsupervised learning of visual features for path integration; we demonstrate the viability of this circuit for use in robotic systems in simulated and physical environments. Furthermore, we propose a theoretical understanding of how distributed online unsupervised network weight modification can be leveraged for learning in a trajectory through an environment by minimizing orientation estimation error. Overall, our results may enable a new class of CX-derived low power robotic navigation algorithms and lead to testable predictions to inform future neuroscience experiments.
AB - Insect neural systems are a promising source of inspiration for new navigation algorithms, especially on low size, weight, and power platforms. There have been unprecedented recent neuroscience breakthroughs with Drosophila in behavioral and neural imaging experiments as well as the mapping of detailed connectivity of neural structures. General mechanisms for learning orientation in the central complex (CX) of Drosophila have been investigated previously; however, it is unclear how these underlying mechanisms extend to cases where there is translation through an environment (beyond only rotation), which is critical for navigation in robotic systems. Here, we develop a CX neural connectivity-constrained model that performs sensor fusion, as well as unsupervised learning of visual features for path integration; we demonstrate the viability of this circuit for use in robotic systems in simulated and physical environments. Furthermore, we propose a theoretical understanding of how distributed online unsupervised network weight modification can be leveraged for learning in a trajectory through an environment by minimizing orientation estimation error. Overall, our results may enable a new class of CX-derived low power robotic navigation algorithms and lead to testable predictions to inform future neuroscience experiments.
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U2 - 10.1038/s41598-022-05798-4
DO - 10.1038/s41598-022-05798-4
M3 - Article
C2 - 35217679
AN - SCOPUS:85125532629
SN - 2045-2322
VL - 12
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 3210
ER -