Aversive olfactory conditioning in Drosophila is a valuable model for elucidating the mechanism of associative learning. Much effort has centered around the role of neuroplasticity at the mushroom body (MB)-mushroom body output neuron (MBON) synapses in mapping odors to specific behaviors. By electrophysiological recordings from MB neurons, we discovered a form of input-timing-dependent plasticity at the incoming synapses from projection neurons that controls the efficacy of aversive olfactory memory formation. Importantly, this plasticity is facilitated by the neural activity of PPL1, the neuronal cluster that also modulates MB-MBON connections at the output stage of MB. Unlike the MB-MBON synapses that probably utilize dopamine D1-like receptors, this neuroplasticity is dependent on D2-like receptors that are expressed mainly by γ Kenyon cells noticeably in their somato-dendritic region. The D2-like receptors recruit voltage-gated calcium channels, leading to calcium influx in the soma and dendrites of γ neurons. Together, our results reveal a previously unrecognized synaptic component of the MB circuit architecture that not only could increase the salience of a conditioning odor but also couples the process of memory encoding and valency mapping to drive-associative learning.
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http://dx.doi.org/10.1016/j.cub.2022.09.054 | DOI Listing |
Curr Biol
November 2022
School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China. Electronic address:
Aversive olfactory conditioning in Drosophila is a valuable model for elucidating the mechanism of associative learning. Much effort has centered around the role of neuroplasticity at the mushroom body (MB)-mushroom body output neuron (MBON) synapses in mapping odors to specific behaviors. By electrophysiological recordings from MB neurons, we discovered a form of input-timing-dependent plasticity at the incoming synapses from projection neurons that controls the efficacy of aversive olfactory memory formation.
View Article and Find Full Text PDFCereb Cortex
March 2020
State Key Laboratory of Bioelectronics, Key Laboratory of Developmental Genes and Human Disease, Ministry of Education, Institute of Life Sciences, Southeast University, Nanjing, Jiangsu Province 210096, China.
Integrated neural inputs from different dendrites converge at the soma for action potential generation. However, it is unclear how the convergent dendritic inputs interact at the soma and whether they can be further modified there. We report here an entirely new plasticity rule in hippocampal neurons in which repetitive pairing of subthreshold excitatory inputs from proximal apical and basal dendrites at a precise interval induces persistent bidirectional modifications of the two dendritic inputs.
View Article and Find Full Text PDFNeuron
August 2017
Department of Neuroscience, Kavli Institute of Brain Science, Columbia University Medical Center, 1051 Riverside Drive, New York, NY, USA. Electronic address:
Input-timing-dependent plasticity (ITDP) is a circuit-based synaptic learning rule by which paired activation of entorhinal cortical (EC) and Schaffer collateral (SC) inputs to hippocampal CA1 pyramidal neurons (PNs) produces a long-term enhancement of SC excitation. We now find that paired stimulation of EC and SC inputs also induces ITDP of SC excitation of CA2 PNs. However, whereas CA1 ITDP results from long-term depression of feedforward inhibition (iLTD) as a result of activation of CB1 endocannabinoid receptors on cholecystokinin-expressing interneurons, CA2 ITDP results from iLTD through activation of δ-opioid receptors on parvalbumin-expressing interneurons.
View Article and Find Full Text PDFPLoS Comput Biol
October 2016
Centre for Computational Neuroscience and Robotics, University of Sussex, Falmer, Brighton, United Kingdom.
We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings.
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