University of Lincoln Masters researcher Mu Hua, recently completed a 12 month secondment for ULTRACEPT at partner Guangzhou University in China.
During my one-year secondment at Guangzhou University, my research was based on the previous works on the locusts LGMD (Lobula Giant Movement Detector) neural networks for collision perception, including the LGMD1 from Prof Shigang Yue and LGMD2 from Dr Qinbing Fu. My work was mainly focused on improving the LGMDs neural network’s ability for ultra-fast approaching objects.
Benefiting from thousands of decades’ evolution, locusts have been equipped with a vision system that improves their success rate of evading their natural predators in the blink of an eye. Taking inspiration from nature through the computational models of LGMDs in locust’s visual pathways has had a positive impact on addressing these problems. However, it is still challenging for current LGMD neural networks to accurately and reliably recognize the imminent collision when the approaching object is ultra-fast (see Fig. 1). The green dashed line is the threshold we set to indicate whether collision is happening or not; the Blue curve is the current LGMD1 responses to the ultra-fast objects. The neuron fires spikes and generates a ‘false alert’ while the approaching black ball is far away.
Since the refractoriness, namely the refractory period which is a common mechanism within plenty of creatures’ neuron systems, is able to assist together with other sorts of mechanisms to help stabilize a neuron. It is then introduced to previous works of LGMDs neural networks for further improvement. On the left in Figure 2, we show a comparison between our new proposed LGMD1 neural network and the previous one from Shigang Yue. On the right, we demonstrate the comparison between our proposed method of LGMD2 and the previous one from Qinbing Fu.
To better understand the refractoriness mechanism and explain the rationality of integrating the LGMDs neural network, we sought guidance from professors, Prof. Jigen Peng and Prof. Huang, and our outstanding colleagues. Their inference from the perspective of mathematics supported the proposed method (see Fig.3).
During my secondment, I obtained knowledge on both bio-plausible neural networks and coding and gained much experience in setting up experiments and analysing the experimental results. Many thanks to the ULTRACEPT project for supporting my research at Guangzhou University, and to my host, Prof. Jigen Peng for kindly providing me access to his well-equipped lab.
