Master thesis presentation Kelly Ma, MPCAS

Abstract: In recent years, significant progress has been achieved in the field of artificial neural networks (ANNs). However, their performance still falls short when compared to their biological counterparts. As a result, there is growing interest in Spiking neuron networks (SNNs), which are known for their biological plausibility and efficiency, aiming to combine the advantages of both existing ANNs and biological networks. Therefore, this project focuses on the development of an SNN that can reliably identify mouse scratching behavior. The test animals were recorded using an event camera, and after the data was preprocessed and converted into frames, a data set was created. Among the different training methods tested, the SNN trained with the gradient surrogate method yielded the best result, achieving a final accuracy of approximately 97\% on the test dataset. However, it should also be noted that the preprocessing process actually has a significant impact on overall performance. Taking this into consideration, the actual accuracy would be around 83\%. This result obtained indicate that a SNN is capable of detecting scratching behavior despite the limitations imposed by the small size of the available data, thus showcasing the potential of SNNs in real-life applications.

Password: 686668

Supervisor: HuanYu Xiang
Examiner: Giovanni Volpe
Opponent: Isac Nordin