A model investigation of short-term synaptic plasticity tuned via Unc13 isoforms.
Springer, M.; Sigrist, S. J.; Nawrot, M. P.
Show abstract
Short-term synaptic plasticity (STP) is a fundamental mechanism of neural computation supporting a variety of nervous system functions from sensory adaptation and gain control to working memory and decision making. At the presynaptic release site, an interplay between distinct (M)Unc13 protein isoforms is suggested to orchestrate depressing and facilitating components of STP. In this study, we introduce a modification of the well-established TsodyksMarkram Model (TMM) for STP. We constrain our model by in vivo intracellular recordings in the olfactory system of the fruit fly, where previous work suggested Unc13A to provide a phasic, depressing and Unc13B a tonic, facilitating release component. A combination of a facilitating and a depressing model component indeed allowed for accurate model fits. Differential knock-down experiments of the Unc13A and Unc13B gene variants provide biological model interpretation, linking the protein-specific molecular mechanisms to synaptic function and STP. Our mathematical formulation of protein-dependent STP can be readily and efficiently used to design biologically realistic spiking neural network models that feature different genetically defined synapse types.
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
- Synaptic cleft geometry modulates NMDAR opening probability by tuning neurotransmitter residence time 94%
- Computational modeling predicts acidic microdomains in the glutamatergic synaptic cleft 94%
- The development of cooperative channels explains the maturation of hair cell’s mechanotransduction 94%
Similar papers in this journal
- Deciphering how interneuron specific 3 cells control oriens lacunosum-moleculare cells to contribute to circuit function 96%
- Development and Binocular Matching of Orientation Selectivity in Visual Cortex: A Computational Model 92%
- Model-based detection of putative synaptic connections from spike recordings with latency and type constraints 92%
Similar papers in this journal
"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.