Improved Path Integration Using a Modified Weight Combination Method
Dynamic neural fields have been used extensively to model brain functions. These models coupled with the mechanisms of path integration have further been used to model idiothetic updates of hippocampal head and place representations, motor functions and have recently gained interest in the field of cognitive robotics. The sustained packet of activity of a neural field combined with a mechanism for moving this activity provides an elegant representation of state using a continuous attractor network. Path integration (PI) is dependent on the modulation of the collateral weights in the neural field. This modulation introduces an asymmetry in the activity packet, which causes a movement of the packet to a new location in the field. The following work provides an analysis of the PI mechanism, with respect to the speed of the packet movement and the robustness of the field under strong rotational inputs. This analysis illustrates challenges in controlling the activity packet size under strong rotational inputs, as well as a limited speed capability using the existing PI mechanism. As a result of this analysis, we propose a novel modification to the weight combination method to provide a higher speed capability and increased robustness of the field. The results of this proposed method are an increase in over two times the existing speed capability and a resistance of the field to break down under strong rotational inputs.
path integration dynamic neural fields trace learning stability