TY - JOUR
T1 - Implementing goal-directed foraging decisions of a simpler nervous system in simulation
AU - Brown, Jeffrey W.
AU - Caetano-Anollés, Derek
AU - Catanho, Marianne
AU - Gribkova, Ekaterina
AU - Ryckman, Nathaniel
AU - Tian, Kun
AU - Voloshin, Mikhail
AU - Gillette, Rhanor
N1 - This work was supported, in early stages, by the National Science Foundation Grant IOB 04-47358 and the National Institutes of Health Grant R21 DA023445. Acknowledgements: Mikhail Voloshin implemented a first Cyberslug version in this lab in 1999, programming in C++ and using a perceptron learning mechanism. We thank Mark Nelson (University of Illinois at Urbana-Champaign) for introduction to NetLogo. We recognize intellectual and collegial contributions of the Microsoft Research/University of Washington Summer Institutes on Intelligent Systems.
Received November 20, 2017; accepted February 6, 2018; First published February 16, 2018. The authors declare no competing financial interests. Author contributions: J.W.B., D.C.-A., M.C., E.G., N.R., K.T., M.V., and R.G. designed research; J.W.B., D.C.-A., M.C., E.G., N.R., K.T., M.V., and R.G. performed research; J.W.B, E.G., and R.G. analyzed data analyzed data; J.W.B., E.G., and R.G. wrote the paper. This work was supported, in early stages, by the National Science Foundation Grant IOB 04-47358 and the National Institutes of Health Grant R21 DA023445. Acknowledgements: Mikhail Voloshin implemented a first Cyberslug version in this lab in 1999, programming in C++ and using a perceptron learning mechanism. We thank Mark Nelson (University of Illinois at Urbana-Champaign) for introduction to NetLogo. We recognize intellectual and collegial contributions of the Microsoft Research/University of Washington Summer Institutes on Intelligent Systems. Correspondence should be addressed to Rhanor Gillette, Department of Molecular and Integrative Physiology, 407 Goodwin Avenue, 524 Burrill Hall, University of Illinois at Urbana-Champaign, Urbana, IL 61801, E-mail: [email protected]. DOI:http://dx.doi.org/10.1523/ENEURO.0400-17.2018 Copyright © 2018 Brown et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Economic decisions arise from evaluation of alternative actions in contexts of motivation and memory. In the predatory sea-slug Pleurobranchaea the economic decisions of foraging are found to occur by the workings of a simple, affectively controlled homeostat with learning abilities. Here, the neuronal circuit relations for approach-avoidance choice of Pleurobranchaea are expressed and tested in the foraging simulation Cyberslug. Choice is organized around appetitive state as a moment-to-moment integration of sensation, motivation (satiation/hunger), and memory. Appetitive state controls a switch for approach vs. avoidance turn responses to sensation. Sensory stimuli are separately integrated for incentive value into appetitive state, and for prey location (stimulus place) into mapping motor response. Learning interacts with satiation to regulate prey choice affectively. The virtual predator realistically reproduces the decisions of the real one in varying circumstances and satisfies optimal foraging criteria. The basic relations are open to experimental embellishment toward enhanced neural and behavioral complexity in simulation, as was the ancestral bilaterian nervous system in evolution.
AB - Economic decisions arise from evaluation of alternative actions in contexts of motivation and memory. In the predatory sea-slug Pleurobranchaea the economic decisions of foraging are found to occur by the workings of a simple, affectively controlled homeostat with learning abilities. Here, the neuronal circuit relations for approach-avoidance choice of Pleurobranchaea are expressed and tested in the foraging simulation Cyberslug. Choice is organized around appetitive state as a moment-to-moment integration of sensation, motivation (satiation/hunger), and memory. Appetitive state controls a switch for approach vs. avoidance turn responses to sensation. Sensory stimuli are separately integrated for incentive value into appetitive state, and for prey location (stimulus place) into mapping motor response. Learning interacts with satiation to regulate prey choice affectively. The virtual predator realistically reproduces the decisions of the real one in varying circumstances and satisfies optimal foraging criteria. The basic relations are open to experimental embellishment toward enhanced neural and behavioral complexity in simulation, as was the ancestral bilaterian nervous system in evolution.
KW - Decision
KW - Homeostasis
KW - Learning
KW - Motivation
KW - Pleurobranchaea
KW - Simulation
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U2 - 10.1523/ENEURO.0400-17.2018
DO - 10.1523/ENEURO.0400-17.2018
M3 - Article
C2 - 29503862
AN - SCOPUS:85043382433
SN - 2373-2822
VL - 5
JO - eNeuro
JF - eNeuro
IS - 1
M1 - e0400-17.2018
ER -