Sensory neuron population expansion enhances odour tracking through relaxed projection neuron adaptation
Takagi, S.; Abuin, L.; Stupski, S. D.; Arguello, J. R.; Prieto-Godino, L. L.; Stern, D. L.; Cruchet, S.; Alvarez-Ocana, R.; Wienecke, C. F. R.; van Breugel, F.; Auer, T. O.; Benton, R.
Show abstract
The evolutionary expansion of sensory neuron populations detecting important environmental cues is widespread, but functionally enigmatic. We investigated this phenomenon through comparison of homologous neural pathways of Drosophila melanogaster and its close relative Drosophila sechellia, an extreme specialist for Morinda citrifolia noni fruit. D. sechellia has evolved species-specific expansions in select, noni-detecting olfactory sensory neuron (OSN) populations, through multigenic changes. Activation and inhibition of defined proportions of neurons demonstrate that OSN population increases contribute to stronger, more persistent, noni-odor tracking behavior. These sensory neuron expansions result in increased synaptic connections with their projection neuron (PN) partners, which are conserved in number between species. Surprisingly, having more OSNs does not lead to greater odor-evoked PN sensitivity or reliability. Rather, pathways with increased sensory pooling exhibit reduced PN adaptation, likely through weakened lateral inhibition. Our work reveals an unexpected functional impact of sensory neuron expansions to explain ecologically-relevant, species-specific behavior.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- A cortical circuit mechanism for coding and updating task structural knowledge in inference-based decision-making 98%
- Potentiation of active locomotor state by spinal-projecting serotonergic neurons 98%
- Ventral frontostriatal circuitry mediates the computation of reinforcement from symbolic gains and losses 97%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.