Computational modelling of synaptic plasticity: a review of models, parameter estimation using deep learning, and stochasticity

Abstract

It is imperative to understand the human memory formation and impairment to treat dementia effectively. There is ample scientific evidence that memory formation is strongly correlated to synaptic connections. Synaptic plasticity reflects the strength of these connections and is strongly related to memory formation and impairment. The complexity in the signalling pathways and interactions among proteins demands a systemic approach to study synaptic plasticity. Hence systems biology approaches are used in computational neuroscience. In this paper, we review the key computational models related to synaptic plasticity, the use of deep learning in parameter estimation, and the incorporation of epistemic stochasticity in the models.