Speaker
Description
Neurons exhibit intrinsic sources of stochasticity which impact on their spiking behavior. These sources include fluctuations in ion channel gating and diffusion, as well as stochastic release of neurotransmitters. As a result, even when responding to identical inputs, there can be significant variability in spike timing.
The Galves–Löcherbach (GL) model [1] is a stochastic neuron model that was proposed to capture the effect of these sources of intrinsic noise on neuronal spiking activity. It models the neuron as a stochastic point process with spiking probability that depends on its membrane potential. After a spike, the neuron's membrane potential is instantaneously reset to 0.
In this work, we present an implementation of the GL model in NEST simulator [2] using the domain specific language NESTML [3]. Additionally, we implemented a version with short-term plasticity dependent on residual calcium [4]. In this case, when a neuron spikes the residual calcium concentration within the cell increases by one unit, and a postsynaptic potential is given that depends linearly on the spiking neuron's calcium concentration. Between successive spikes, the membrane potential and calcium concentration of the neuron decrease at a constant rate.
Further simulations are necessary to validate the proof of concept implementation with respect to theory and detailed benchmarking and optimisation. On the theoretical side the NESTML specification facilitates the comparison to other stochastic neuron models. The implementation of the GL model in NEST will provide researchers with a powerful tool for investigating the large-scale spiking neural networks dynamics in an efficient manner.
References
[1] Galves, A., & Löcherbach, E. (2013). Infinite Systems of Interacting Chains with Memory of Variable Length-A Stochastic Model for Biological Neural Nets. Journal of Statistical Physics, 151(5), 896–921. https://doi.org/10.1007/S10955-013-0733-9
[2] Sinha, A., de Schepper, R., Pronold, J., Mitchell, J., Mørk, H., Nagendra Babu, P., Eppler, J. M., Lober, M., Linssen, C., Terhorst, D., Benelhedi, M. A., Morrison, A., Wybo, W., Trensch, G., Deepu, R., Haug, N., Kurth, A., Vennemo, S. B., Graber, S., … Plesser, H. E. (2023). NEST 3.4. https://doi.org/10.5281/ZENODO.6867800
[3] Linssen, C.A.P., Babu, P.N., He, J., Eppler, J.M., Rumpe, B. and Morrison, A. (2022). NESTML 5.1.0. Zenodo. doi:10.5281/zenodo.7071624.
[4] Galves, A., Löcherbach, E., Pouzat, C., & Presutti, E. (2020). A System of Interacting Neurons with Short Term Synaptic Facilitation. Journal of Statistical Physics, 178(4), 869–892. https://doi.org/10.1007/S10955-019-02467-1
Acknowledgements
This software was partly developed in the Human Brain Project, funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements No. 720270, No. 785907 and No. 945539 (Human Brain Project SGA1, SGA2 and SGA3). This work was produced as part of the activities of FAPESP Research, Dissemination and Innovation Center for Neuromathematics (grant 2013/07699-0, S. Paulo Research Foundation). Part of the work was conducted while Diesmann, Pouzat, and Roque participated in the thematic program “Random Processes in the Brain” at the Institut Henri Poincaré supported by UAR 839 CNRS-Sorbonne Université and LabEx CARMIN (ANR-10-LABX-59-01).
| Preferred form of presentation | Poster & advertising flash talk |
|---|---|
| Topic area | models and applications |
| Keywords | stochastic, short-term plasticity, nestml, neuron model |
| Speaker time zone | UTC+2 |
| I agree to the copyright and license terms | Yes |
| I agree to the declaration of honor | Yes |
