Speaker
Description
Nitric Oxide (NO) is an essential molecule involved in the synaptic plasticity of many areas of the brain and in neurovascular coupling. NO is known to be present in the cerebellum, both in the Granular and the Molecular layers and it is thought to have an enabling function in plasticity mechanisms. NO plasticity dependency has been investigated mainly in experimental studies, and few mathematical models replicate its function on simple networks, but it has not been included in in silico simulations of large spiking neural networks (SNN).
In this project, we aim to create a Python module for simulating NO diffusion and integrate it in a NEST simulation of a cerebellar micro-circuit to test its effect on plasticity between parallel fibres (pf) and Purkinje cells (PC). In each pf-PC synapse, we place sources of NO that receive stimuli from the pf and produce NO accordingly. NO diffusion is then simulated in the network, and its concentration is evaluated in each synapse. We then modified the STDP learning rule at the pf-PC synapse level, by implementing a dependency on the [NO] concentration values, and we assessed the spatiality and the effects of the plasticity enabling, following different stimulation protocols.
This computational model presents itself as a useful and simple tool to simulate the functional role of Nitric Oxide in Neural Networks and its functional role.
Acknowledgements
This research has received funding from the European Union’sHorizon2020 Framework Programme for Research and Innovation under Specific Grant Agreement no. 785907 (Human Brain Project SGA2) and by the HBP PartneringProject (CerebNEST).
References
[1] Alberto Antonietti, Claudia Casellato, Jesús A Garrido, Niceto R Luque, Francisco Naveros, Eduardo Ros, Egidio D’Angelo, and Alessandra Pedrocchi. Spiking neural network with distributed plasticity reproduces cerebellar learning in eye blink conditioning paradigms. IEEE Transactions on Biomedical Engineering, 63(1):210–219,
2015.
[2] Guy Bouvier, David Higgins, Maria Spolidoro, Damien Carrel, Benjamin Mathieu, Clément Léna, Stéphane Dieudonné, Boris Barbour, Nicolas Brunel, and Mariano Casado. Burst dependent bidirectional plasticity in the cerebellum is driven by presynaptic nmda receptors. Cell Reports, 15(1):104–116, 2016.
[3] John Garthwaite. No as a multimodal transmitter in the brain: discovery and current status. British Journal of Pharmacology, 176(2):197–211, 2019.
[4] Eric R Kandel, James H Schwartz, Thomas M Jessell, Steven Siegelbaum, A James Hudspeth, Sarah Mack, et al. Principles of neural science, volume 4. McGraw-hill New York, 2000.
[5] Varda Lev-Ram, Scott T Wong, Daniel R Storm, and Roger Y Tsien. A new form of cerebellar long-term potentiation is postsynaptic and depends on nitric oxide but not camp. Proceedings of the National Academy of Sciences, 99(12):8389–8393, 2002.
[6] Hideaki Ogasawara, Tomokazu Doi, Kenji Doya, and Mitsuo Kawato. Nitric oxide regulates input specificity of long-term depression and context
dependence of cerebellar learning. PLoS Computational
Biology, 3(1):e179, 2007.
[7] Harel Z Shouval, Samuel S-HWang, and GayleM Wittenberg. Spike timing dependent plasticity: a consequence of more fundamental learning rules. Frontiers in computational neuroscience, 4:19, 2010.
[8] Alessandra Trapani, Alberto Antonietti, Giovanni Naldi, Egidio D’Angelo, and Alessandra Pedrocchi. Production and diffusion model of nitric oxide for bioinspired spiking neural networks. In 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), pages 457–460. IEEE, 2021.
| Preferred form of presentation | Talk (& optional poster) |
|---|---|
| Topic area | models and applications |
| Keywords | Nitric Oxide, plasticity, cerebellum |
| Speaker time zone | UTC+1 |
| I agree to the copyright and license terms | Yes |
| I agree to the declaration of honor | Yes |
