Abstract
It is widely accepted that in the brain processes related to learning and memory there are changes at the level of synapses. Synapses have the ability to change their strength depending on the stimuli, which is called activity-dependent synaptic plasticity. To date, many mathematical models describing activity-dependent synaptic plasticity have been introduced. However, the remaining question is whether these rules apply in general to the whole brain or only to individual areas or even just to individual types of cells. Here, we decided to test whether the well-known rule of Spike-Timing Dependent Plasticity (STDP) extended by metaplasticity (meta-STDP) supports long-term stability of major synaptic inputs to hippocampal CA1 pyramidal neurons. For this reason, we have coupled synaptic models equipped with a previously established meta-STDP rule to a biophysically realistic computational model of the hippocampal CA1 pyramidal cell with a simplified dendritic tree. Our simulations show that the meta-STDP rule is able to keep synaptic weights stable during ongoing spontaneous input activity as it happens in the hippocampus in vivo. This is functionally advantageous as neurons should not change their weights during the ongoing activity of neural circuits in vivo. However, they should maintain their ability to display plastic changes in the case of significantly different or “meaningful” inputs. Thus, our study is the first step before we attempt to simulate different stimulation protocols which induce changes in synaptic weights in vivo.
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Tomko, M., Jedlička, P., Beňušková, L. (2020). Meta-STDP Rule Stabilizes Synaptic Weights Under in Vivo-like Ongoing Spontaneous Activity in a Computational Model of CA1 Pyramidal Cell. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12397. Springer, Cham. https://doi.org/10.1007/978-3-030-61616-8_54
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