Fang Lei is a PhD Scholar at the University of Lincoln. As part of the STEP2DYNA project working on Work Package 1 and 2, Fang completed a 4 month secondment to Guangzhou University in China.
Recently, Fang Lei attended the IEEE World Congress on Computational Intelligence (WCCI) 2020. Originally due to take place in Glasgow between 19th and 24th July 2020, the conference was moved online due to Covid-19.
The WCCI is the world’s largest technical event on computational intelligence and features three conferences from the IEEE Computational Intelligence Society (CIS), the 2020 International Joint Conference on Neural Networks (IJCNN 2020), the 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2020), and the 2020 IEEE Conference on Evolutionary Computation (IEEE CEC 2020).
IEEE WCCI 2020 covered topics in the field of neural networks, from biological networks to artificial computation and was attended by more than 2,350 record attendees from over 75 countries. The event schedule included:
- Public lecture by Yoshua Bengio on the topic of artificial neural networks and deep learning 2.0
- 4 Plenary Speeches by world-renowned scholars: Barbara Hammer, Kay Chen Tan, Carlos Coello Coello, and Jim Bezdek
- 15 Keynotes by top-notch researchers, 5 per Conference
- 4 cutting-edge Panel sessions
- 36 Tutorials
- 10 Workshops
- 170 Special Sessions including 61 for IJCNN, 42 For IEEE CEC, 37 For FUZZ-IEEE, and 30 Cross-Disciplinary sessions
- 13 Challenging and contemporary competitions
Fang Lei presented her research Competition between ON and OFF Neural Pathways Enhancing Collision Selectivity which has been published by the IEEE:
F. Lei, Z. Peng, V. Cutsuridis, M. Liu, Y. Zhang and S. Yue, “Competition between ON and OFF Neural Pathways Enhancing Collision Selectivity,” 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, United Kingdom, 2020, pp. 1-8, doi: 10.1109/IJCNN48605.2020.9207131.
The LGMD1 neuron of locusts shows strong looming-sensitive property for both light and dark objects. Although a few LGMD1 models have been proposed, they are not reliable to inhibit the translating motion under certain conditions compare to the biological LGMD1 in the locust. To address this issue, we propose a bio-plausible model to enhance the collision-selectivity by inhibiting the translating motion. The proposed model contains three parts, the retina to lamina layer for receiving luminance change signals, the lamina to medulla layer for extracting motion cues via ON and OFF pathways separately, the medulla to lobula layer for eliminating translational excitation with neural competition. We tested the model by synthetic stimuli and real physical stimuli. The experimental results demonstrate that the proposed LGMD1 model has a strong preference for objects in direct collision course-it can detect looming objects in different conditions while completely ignoring translating objects.
When asked about her experience of the conference, Fang Lei said…
“Although I hoped to attend the conference physically, I was still excited as it was my first time attending the international conference. It started at midnight in China due to the time difference but I was eager to share my research with academic peers and share this experience with them.
I was asked to present at the conference and did so via a pre-uploaded video. I presented within the visual system session of IJCNN regular sessions. The presentation went very smoothly and we discussed problems with presenting papers by asking and answering questions.
I found the conference to be an interesting and meaningful experience for me. It was good to be able to spread our work to peers and gain knowledge of the work others are doing. The only thing I wish was that the conference was face-to-face.”