KIP publications

 
year 2020
author(s) Chang Zhao, Yves F. Widmer, Soeren Diegelmann, Mihai A. Petrovici, Simon G. Sprecher, Walter Senn
title Predictive olfactory learning in Drosophila
KIP-Nummer HD-KIP 20-44
KIP-Gruppe(n) F9
document type Paper
Keywords drosophila, olfaction, associative learning, predictive plasticity
doi doi: https://doi.org/10.1101/2019.12.29.890533
Abstract (en)

Olfactory learning and conditioning in the fruit fly is typically modelled by correlation-based associative synaptic plasticity. It was shown that the conditioning of an odor-evoked response by a shock depends on the connections from Kenyon cells (KC) to mushroom body output neurons (MBONs). Although on the behavioral level conditioning is recognized to be predictive, it remains unclear how MBONs form predictions of aversive or appetitive values (valences) of odors on the circuit level. We present behavioral experiments that are not well explained by associative plasticity between conditioned and unconditioned stimuli, and we suggest two alternative models for how predictions can be formed. In error-driven predictive plasticity, dopaminergic neurons (DANs) represent the error between the predictive odor value and the shock strength. In target-driven predictive plasticity, the DANs represent the target for the predictive MBON activity. Predictive plasticity in KC-to-MBON synapses can also explain trace-conditioning, the valence-dependent sign switch in plasticity, and the observed novelty-familiarity representation. The model offer a framework to dissect MBON circuits and interpret DAN activity during olfactory learning.

bibtex
@article{zhao2019predictive,
  author   = {Chang Zhao, Yves F. Widmer, Soeren Diegelmann, Mihai A. Petrovici, Simon G. Sprecher, Walter Senn},
  title    = {Predictive olfactory learning in Drosophila},
  journal  = {bioRxiv},
  year     = {2019},
  volume   = {},
  pages    = {},
  doi      = {doi: https://doi.org/10.1101/2019.12.29.890533},
  url      = {https://www.biorxiv.org/content/10.1101/2019.12.29.890533v1}
}
URL bioRxiv
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