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Convergent Temperature Representations in Artificial and Biological Neural Networks.

  • Haesemeyer M Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA. Electronic address: haesemeyer@fas.harvard.edu.
  • Schier AF Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Stem Cell Institute, Cambridge, MA 02138, USA; Biozentrum, University of Basel, 4056 Basel, Switzerland.
  • Engert F Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA. Electronic address: florian@mcb.harvard.edu.
  • 2019-08-05
Published in:
  • Neuron. - 2019
C. elegans
artificial neural network
comparative computation
computation
representation
thermosensation
zebrafish
Animals
Brain
Caenorhabditis elegans
Cerebellum
Hot Temperature
Nerve Net
Neural Networks, Computer
Reinforcement, Psychology
Spatial Navigation
Supervised Machine Learning
Thermosensing
Zebrafish
English Discoveries in biological neural networks (BNNs) shaped artificial neural networks (ANNs) and computational parallels between ANNs and BNNs have recently been discovered. However, it is unclear to what extent discoveries in ANNs can give insight into BNN function. Here, we designed and trained an ANN to perform heat gradient navigation and found striking similarities in computation and heat representation to a known zebrafish BNN. This included shared ON- and OFF-type representations of absolute temperature and rates of change. Importantly, ANN function critically relied on zebrafish-like units. We furthermore used the accessibility of the ANN to discover a new temperature-responsive cell type in the zebrafish cerebellum. Finally, constraining the ANN by the C. elegans motor repertoire retuned sensory representations indicating that our approach generalizes. Together, these results emphasize convergence of ANNs and BNNs on stereotypical representations and that ANNs form a powerful tool to understand their biological counterparts.
Language
  • English
Open access status
bronze
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Persistent URL
https://folia.unifr.ch/global/documents/73870
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