P158 A unified model for estimating short- and long-term synaptic plasticity from stimulation-induced spiking activity
Arash Rezaei1,2, Mojtaba Madadi Asl3,4,Milad Lankarany*1,2,5
1Krembil Brain Institute, University Health Network, Toronto, ON, Canada
2Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
3School of Biological Sciences,Institute for Research in Fundamental Sciences (IPM),Tehran,Iran
4Pasargad Institute for Advanced Innovative Solutions (PIAIS),Tehran,Iran
5Center for Advancing Neurotechnological Innovation to Application (CRANIA), Toronto, ON, Canada
*Email:milad.lankarany@uhn.ca
Introduction
Abnormal brain activity is the hallmark of several brain disorders such as Parkinson’s disease, essential tremor, and epilepsy [1,2]. Stimulation-induced reshaping of the brain’s networks through neuroplasticity may disrupt neural activity as well as synaptic connectivity and potentially restore healthy brain dynamics. Synaptic plasticity has been the target of invasive therapies, such as deep brain stimulation [3-5]. Mathematical frameworks were able to estimate short-term [6,7] and long-term [8] synaptic dynamics separately. However, the characterization of both short and long-term synaptic plasticity from spiking activity is crucial for understanding the underlying mechanisms and optimization of spatio-temporal patterns of stimulation.
Methods
We developed a novel synapse model to integrate both short- and long-term plasticity into a unified framework wherein the postsynaptic neuron behaves according to both plasticity mechanisms. In the proposed model, the postsynaptic neuron is notably driven by the STP synaptic current and LTP synaptic weight at each step. To induce short- and long-term synaptic responses, presynaptic spike trains were applied for durations of a few hundred milliseconds (STP experiment) and hundreds of seconds (LTP experiment), respectively, to a single postsynaptic neuron. For the STP experiment, a single presynaptic spike train was used, whereas the LTP experiment involved 1000 presynaptic inputs. For both experiments depressing STP synapses were utilized.
Results
Our results demonstrated that in the STP experiment, the unified model produced the same transient fluctuations in the membrane potential of the postsynaptic neuron as observed in the STP-only model. This is evident when comparing Fig. 1.A and B as we see the same pattern of behavior in the postsynaptic membrane potential. In the LTP experiment, we observed similar long-term distribution of the synaptic weights as in the model with only long-term synapses (Fig. 1.C). However, the depression was more pronounced in the unified model due to the concurrent influence of STP and LTP on the postsynaptic neuron. The number of synapses with lower weights increases with the addition of the depressive STP mechanism compared to the LTP-only model.
Discussion
These findings suggest that the integration of STP and STDP within a single synaptic framework can effectively capture both transient and long-lasting plasticity effects. Furthermore, such uniform modeling of STP and LTP enables the incorporation of various combinations of synaptic settings into a population of neurons. This can potentially enhance the biological plausibility and flexibility of the current stimulation-induced neural models.
Figure 1. Fig. 1. Results of the STP and LTP experiments. A) Input spike train, neural and synaptic behavior of a model with only STP after stimulation. B) Behavior of the unified model with both STP and LTP after stimulation. The postsynaptic neuron was stimulated for 1200 ms with a 20 Hz firing rate and a depressing STP synapse (Red lines: postsynaptic membrane potential, Blue dotted lines: STP synaptic c
Acknowledgements
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References
1.https://doi.org/10.1016/j.neuron.2006.09.020
2.https://doi.org/10.1371/journal.pcbi.1002124
3.https://doi.org/10.1002/ana.23663
4.https://doi.org/10.1002/mds.25923
5.https://doi.org/10.1016/j.brs.2016.03.014
6.https://doi.org/10.1371/journal.pcbi.1008013
7.https://doi.org/10.1371/journal.pone.0273699
8.https://doi.org/10.1162/neco_a_00883
9.https://doi.org/10.7554/eLife.47314
