001     169074
005     20240610143728.0
024 7 _ |a 10.5061/DRYAD.BK3J9KDD1
|2 doi
024 7 _ |a 10.5061/dryad.bk3j9kdd1
|2 doi
037 _ _ |a DZNE-2022-01781
041 _ _ |a English
100 1 _ |a Tavosanis, Gaia
|0 P:(DE-2719)2810271
|b 0
|u dzne
245 _ _ |a Dataset: APL synapses distribution on PN and KC meshes, primary data, calcium imaging macros and python scripts from Prisco et al.
260 _ _ |c 2021
|b Dryad
336 7 _ |a MISC
|2 BibTeX
336 7 _ |a Dataset
|b dataset
|m dataset
|0 PUB:(DE-HGF)32
|s 1718023036_5321
|2 PUB:(DE-HGF)
336 7 _ |a Chart or Table
|0 26
|2 EndNote
336 7 _ |a Dataset
|2 DataCite
336 7 _ |a DATA_SET
|2 ORCID
336 7 _ |a ResearchData
|2 DINI
520 _ _ |a To identify and memorize discrete but similar environmental inputs, the brain needs to distinguish between subtle differences of activity patterns in defined neuronal populations. The Kenyon cells of the Drosophila adult mushroom body (MB) respond sparsely to complex olfactory input, a property that is thought to support stimuli discrimination in the MB. To understand how this property emerges, we investigated the role of the inhibitory anterior paired lateral neuron (APL) in the input circuit of the MB, the calyx. Within the calyx, presynaptic boutons of projection neurons (PNs) form large synaptic microglomeruli (MGs) with dendrites of postsynaptic Kenyon cells (KCs). Combining EM data analysis and in vivo calcium imaging, we show that APL, via inhibitory and reciprocal synapses targeting both PN boutons and KC dendrites, normalizes odour-evoked representations in MGs of the calyx. APL response scales with the PN input strength and is regionalized around PN input distribution. Our data indicate that the formation of a sparse code by the Kenyon cells requires APL-driven normalization of their MG postsynaptic responses. This work provides experimental insights on how inhibition shapes sensory information representation in a higher brain centre, thereby supporting stimuli discrimination and allowing for efficient associative memory formation.
536 _ _ |a 351 - Brain Function (POF4-351)
|0 G:(DE-HGF)POF4-351
|c POF4-351
|f POF IV
|x 0
588 _ _ |a Dataset connected to DataCite
700 1 _ |a Prisco, Luigi
|0 P:(DE-2719)2812229
|b 1
|e Last author
|u dzne
773 _ _ |a 10.5061/dryad.bk3j9kdd1
909 C O |p VDB
|o oai:pub.dzne.de:169074
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 0
|6 P:(DE-2719)2810271
910 1 _ |a Deutsches Zentrum für Neurodegenerative Erkrankungen
|0 I:(DE-588)1065079516
|k DZNE
|b 1
|6 P:(DE-2719)2812229
913 1 _ |a DE-HGF
|b Gesundheit
|l Neurodegenerative Diseases
|1 G:(DE-HGF)POF4-350
|0 G:(DE-HGF)POF4-351
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Brain Function
|x 0
914 1 _ |y 2021
920 1 _ |0 I:(DE-2719)1013018
|k AG Tavosanis
|l Dynamics of neuronal circuits
|x 0
980 _ _ |a dataset
980 _ _ |a VDB
980 _ _ |a I:(DE-2719)1013018
980 _ _ |a UNRESTRICTED


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