Speaker
Description
This study translates the model of Chindemi et al. on calcium-dependent neocortical plasticity into a spiking neural network framework. Building on their work, we implemented a computationally efficient model comprising a point neuron and synapse model, using NESTML. Our approach combines the Hill-Tononi (HT) neuron, which features detailed NMDA and AMPA conductance dynamics, with the Tsodyks-Markram (TM) stochastic synapse, which controls vesicle release probability. We extended these components to create a comprehensive framework that captures the relationship between calcium dynamics and spike-timing dependent plasticity while maintaining computational efficiency for large-scale network simulations. Both our model and Chindemi’s rely on the assumption that calcium-dependent processes following paired pre- and post-synaptic activity influence synaptic efficacy on both sides of the synapse: by modifying the maximum AMPA conductance (GAMPA) at the post-synaptic site and the release probability (USE) at the synapse. We validated our implementation through a series of experiments: first confirming the functionality of the TM synapse model paired with HT neuron modifications to account for calcium currents, then testing isolated pre- and post-synaptic activations, generating NMDA and VDCC calcium currents respectively. Finally, we examined paired pre-post stimulation at varying time intervals. Our results successfully replicate Chindemi’s findings obtained with more complex multicompartmental models, also assessing plasticity outcomes according to the distance of the synaptic input to the soma, as experimental evidence shown by P. J. Sjöström and M. Haüsser. This work bridges neuronal activity patterns and synaptic modifications underlying learning and memory.
References
[1] R. S. Zucker, “Calcium- and activity-dependent synaptic plasticity”, Current Opinion in Neurobiology, 1999. [2] G. Chindemi et al, “A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex”, Nature Communications, 2022. [3] M. Graupner and N. Brunel, “Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location”, PNAS, 2012. [4] S. Hill and G. Tononi, “Modeling Sleep and Wakefulness in the Thalamocortical System”, Journal Of Neurophysiology, 2005. [5] M.V. Tsodyks and H. Markram, “The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability”, PNAS, 1997. [6] P. J. Sjöström and M. Haüsser, “A Cooperative Switch Determines the Sign of Synaptic Plasticity in Distal Dendrites of Neocortical Pyramidal Neurons”, Neuron, 2006.
Acknowledgements
The work of AA, AP, CAS, and FDS in this research is supported by Horizon Europe Program for Research and Innovation under Grant Agreement No.101147319 (EBRAINS 2.0) and EBRAINS-Italy (European Brain ReseArch INfrastructureS-Italy), granted by the Italian National Recovery and Resilience Plan (NRRP), M4C2, funded by the EuropeanUnion –NextGenerationEU (Project IR0000011, CUP B51E22000150006, EBRAINS-Italy).
| Preferred form of presentation | Talk (& optional poster) |
|---|---|
| Topic area | Models and applications |
| Keywords | Synaptic Plasticity, Calcium Dynamics, STDP, Spiking Neural Network |
| Speaker time zone | UTC+1 |
| I agree to the copyright and license terms | Yes |
| I agree to the declaration of honor | Yes |
