Calcium dynamics serve asbridge between neuronal activity and synaptic plasticity, orchestrating the biochemical cascades thatdeterminesynaptic strengthening (LTP) or weakening (LTD)[1].Extending thework of Graupner and Brunel [2],Chindemiand colleagues recently introduced a data-constrained model of plasticity based on postsynaptic calcium dynamics in the neocortex[3].Themodel has been developed for NEURON simulationscapturing diverse plasticity dynamics with a single parameter set across pyramidal cell-types. In this work, we translatedChindemi’smodel to a spiking neural network by implementing a point neuron model andaunified synapse, testing it across various calcium-concentration scenarios.
Methods We developed our model using NESTML[4], an open-source language integrated with NEST[5]simulator, enablingthe application of our models to diverse neural networks. The implemented neuron was built upon the existingHill-Tononi(HT)model, which already incorporates detailed NMDA and AMPA conductance dynamics[6].As inChindemi, the synapse was instead based ontheTsodyks-Markram(TM)stochastic synapse model[7], allowing to manipulatevesicle release probability.Following pairedpre-and post-synaptic activitycalcium-dependent processesinfluencesynaptic efficacyat both sides.Our implementation extends these established components to create a comprehensive framework that captures therelationship between calcium dynamics and synaptic plasticity whilemaintainingcomputational efficiency for network-scale simulations. Results We firstvalidatedour model for the TM stochastic synapsepaired withHTmodificationstoaccount forcalcium currentspostsynaptic neuron.Then, we connected two neurons and stimulated either the pre-or post-synapticneuron directly, creatingrespectively NMDA andVDCC calcium currents.Next, we testedthepaired activation of pre-and post-synaptic neurons at varying time intervals.The results of these simulationsare comparable withthe ones ofChindemiet al.Finally, we adjusted LTD and LTP thresholds to match calcium signal properties of pyramidal neurons across different cortical layers. Our simpler point neuron model successfully replicatedfindingsobtained with multicompartmentalmodelswhilemaintainingcomputational efficiency. Discussion Our workimplementscalcium-dependent plasticity into an efficientmodel for spiking neurons. Wevalidatedthat our point neuron approach reproduces the complex calcium dynamics and plasticity outcomes across different stimulation patterns. Bymaintainingthe ability to capture layer-specific plasticity with adjusted LTP/LTD thresholds, we preserve biological accuracy while reducing computational demands.Our efficient implementation of calcium-dependent plasticitypossibly enableslarge-scale spiking neural network simulations to study how synaptic mechanisms affect network functionality.
Acknowledgements The work of AA, AP,CAS, andFDSin this research is supported by Horizon Europe Program for Research and Innovation under Grant Agreement No.101147319 (EBRAINS2.0)andEBRAINS-Italy (European BrainReseArchINfrastructureS-Italy),granted by the Italian National Recovery and Resilience Plan (NRRP), M4C2, funded by the EuropeanUnion –NextGenerationEU(Project IR0000011, CUP B51E22000150006).
