Neural dynamics for landmark orientation and angular path integration

Non-sensory inputs to angular path integration.

Non-sensory (cognitive) inputs can play a powerful role in monitoring one's self-motion. Previously, we showed that access to spatial memory dramatically increases response precision in an angular self-motion updating task [1]. Here, we examined whether spatial memory also enhances a particular type of self-motion updating - angular path integration. "Angular path integration" refers to the abi...

Approximating Thin-Plate Splines for Elasti Registration: Integration of Landmark Errors and Orientation Attributes

Approximating Thin-Plate Splines for Elastic Registration: Integration of Landmark Errors and Orientation Attributes

We introduce an approach to elastic registration of tomographic images based on thin-plate splines. Central to this scheme is a well-de ned minimizing functional for which the solution can be stated analytically. In this work, we consider the integration of anisotropic landmark errors as well as additional attributes at landmarks. As attributes we use orientations at landmarks and we incorporat...

Evolving neural models of path integration.

We use a genetic algorithm to evolve neural models of path integration, with particular emphasis on reproducing the homing behaviour of Cataglyphis fortis ants. This is done within the context of a complete model system, including an explicit representation of the animal's movements within its environment. We show that it is possible to produce a neural network without imposing a priori any par...

Landmark selection strategies for path execution

A mobile robot often executes a planned path by measuring its position relative to visible landmarks at known positions and then using this information to estimate its own absolute position. The minimum number of landmarks k required for self-location depends on the types of measurements the robot can perform, such as visual angles using a video camera on a pan-tilt unit, distances using a lase...