Master thesis

 
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Titel
Neuromorphic implementation of a two-component synaptic learning rule for preventing catastrophic forgetting
description
Catastrophic forgetting describes the phenomenon that occurs when learning of new concepts interferes with pre-existing memories. It poses a challenge not only for biological learning systems - i.e., brains - but also for machine learning models and for applications of learning neuromorphic hardware. Recently, a novel learning rule was proposed to tackle this challenge by using two interacting dynamic variables for each synapse in a neural network. Interestingly, this synaptic learning rule could be combined with different other learning paradigms to improve their performance. This Masterís project is aimed at investigating this learning rule and its applicability to the spiking neuromorphic systems developed in the Electronic Vision(s) group, along with the evaluation of its advantages in different learning situations.
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