Eleni Vasilaki

TL;DR: A model of spike timing–dependent plasticity (STDP) in which synaptic changes depend on presynaptic spike arrival and the postsynaptic membrane potential, filtered with two different time constants is created and found that the plasticity rule led not only to development of localized receptive fields but also to connectivity patterns that reflect the neural code.

TL;DR: A mathematical model is presented that describes the induction of long-term potentiation and depression (LTP/LTD) during the early phase of synaptic plasticity, the setting of synaptic tags, a trigger process for protein synthesis, and a slow transition leading to synaptic consolidation during the late phase ofaptic plasticity.

TL;DR: The theoretical approach shows how learning new behaviors can be linked to reward-modulated plasticity at the level of single synapses and makes predictions about the voltage and spike-timing dependence of synaptic plasticity and the influence of neuromodulators such as dopamine.

TL;DR: It is demonstrated that single solid-state TiO2 memristors can exhibit non-associative plasticity phenomena observed in biological synapses, supported by their metastable memory state transition properties.

TL;DR: In this paper, the case for memristors as a potential solution for the implementation of power-efficient in-memory computing, deep learning accelerators, and spiking neural networks is discussed.