Efficient LSTM Training with Eligibility Traces

Training recurrent neural networks is predominantly achieved via backpropagation through time (BPTT). However, this algorithm is not an optimal solution from both a biological and computational perspective. A more efficient and biologically plausible alternative for BPTT is e-prop. We investigate the applicability of e-prop to long short-term memorys (LSTMs), for both supervised and reinforcement learning (RL) tasks. We show that e-prop is a suitable optimization algorithm for LSTMs by comparing it to BPTT on two benchmarks for supervised learning. This proves that e-prop can achieve learning even for problems with long sequences of several hundred timesteps. We introduce extensions that improve the performance of e-prop, which can partially be applied to other network architectures. With the help of these extensions we show that, under certain conditions, e-prop can outperform BPTT for one of the two benchmarks for supervised learning. Finally, we deliver a proof of concept for the integration of e-prop to RL in the domain of deep recurrent Q-learning

Optimal eye movement strategies : a comparison of neurosurgeons gaze patterns when using a surgical microscope

Background Previous studies have consistently demonstrated gaze behaviour differences related to expertise during various surgical procedures. In micro-neurosurgery, however, there is a lack of evidence of empirically demonstrated individual differences associated with visual attention. It is unknown exactly how neurosurgeons see a stereoscopic magnified view in the context of micro-neurosurgery and what this implies for medical training. Method We report on an investigation of the eye movement patterns in micro-neurosurgery using a state-of-the-art eye tracker. We studied the eye movements of nine neurosurgeons while performing cutting and suturing tasks under a surgical microscope. Eye-movement characteristics, such as fixation (focus level) and saccade (visual search pattern), were analysed. Results The results show a strong relationship between the level of microsurgical skill and the gaze pattern, whereas more expertise is associated with greater eye control, stability, and focusing in eye behaviour. For example, in the cutting task, well-trained surgeons increased their fixation durations on the operating field twice as much as the novices (expert, 848 ms; novice, 402 ms). Conclusions Maintaining steady visual attention on the target (fixation), as well as being able to quickly make eye jumps from one target to another (saccades) are two important elements for the success of neurosurgery. The captured gaze patterns can be used to improve medical education, as part of an assessment system or in a gaze-training application.Peer reviewe

Eye Tracking Data Collection Protocol for VR for Remotely Located Subjects using Blockchain and Smart Contracts

Eye tracking data collection in the virtual reality context is typically carried out in laboratory settings, which usually limits the number of participants or consumes at least several months of research time. In addition, under laboratory settings, subjects may not behave naturally due to being recorded in an uncomfortable environment. In this work, we propose a proof-of-concept eye tracking data collection protocol and its implementation to collect eye tracking data from remotely located subjects, particularly for virtual reality using Ethereum blockchain and smart contracts. With the proposed protocol, data collectors can collect high quality eye tracking data from a large number of human subjects with heterogeneous socio-demographic characteristics. The quality and the amount of data can be helpful for various tasks in data-driven human-computer interaction and artificial intelligence.Comment: 2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR). Authors' copy, refer to the doi for more informatio