No abstract

Insects, despite relatively small brains, can perform complex navigation tasks such as memorising a visual route. The exact format of visual memory encoded by neural systems during route learning and following is still unclear. Here we propose that interconnections between Kenyon cells in the Mushroom Body (MB) could encode spatio-temporal memory of visual motion experienced when moving along a route. In our implementation, visual motion is sensed using an event-based camera mounted on a robot, and learned by a biologically constrained spiking neural network model, based on simplified MB architecture and using modified leaky integrate-and-fire neurons. In contrast to previous image- matching models where all memories are stored in parallel, the continu- ous visual flow is inherently sequential. Our results show that the model can distinguish learned from unlearned route segments, with some toler- ance to internal and external noise, including small displacements. The neural response can also explain observed behaviour taken to support sequential memory in ant experiments. However, obtaining comparable robustness to insect navigation might require the addition of biomimetic pre-processing of the input stream, and determination of the appropriate motor strategy to exploit the memory output.

Abstract. In this paper, a three-dimensional (3D) visualization method of poplar seedlings was proposed, combined with the ToF camera and digital camera. Firstly, the average distance density method was proposed and employed to process outlier of leaves. Secondly, in order to improve the speed of data fitting, the local section tangent plane normal angle method was introduced to filter redundant points and retain the necessary data, and then the control points were adopted to fit the leaf surface based on Non-Uniform Rational B-Splines (NURBS) fitting algorithm. At the same time, the trunk was fitted based on NURBS according to the control points of the trunk, and the other branches were fitted using iterative method. Finally, the three-dimensional visualization of poplar seedlings was realized. A large number of experiments were carried out, including the normal growth poplar seedlings and the water shortages poplar seedlings. The results demonstrated that the poplar seedlings visualization method proposed in this paper can quickly and accurately reflect the growth states of the poplar seedlings, especially for water-shortage detection.