In 3 experiments, rats were required to find a submerged platform located in 1 corner of an arena that had 2 long and 2 short sides; they were then trained to find the platform in a new arena that also had 2 long and 2 short sides but a different overall shape. The platform in the new arena was easier to find if it was in a corner that was geometrically equivalent, rather than the mirror image, of the corner where it had previously been located. The final experiment revealed that hippocampal lesions impaired rats' ability to find the platform in these arenas. The results suggest that rats did not use the overall shape of the arena to locate the platform but relied on more local cues and that the hippocampus plays a role in navigation based on these cues.

Abstract. Packet header traces are widely used in network analysis. Header traces are the aggregate of traffic from many concurrent applications. We present a methodology, based on machine learning, that can break the trace down into clusters of traffic where each cluster has different traffic characteristics. Typical clusters include bulk transfer, single and multiple transactions and interactive traffic, amongst others. The paper includes a description of the methodology, a visualisation of the attribute statistics that aids in recognising cluster types and a discussion of the stability and effectiveness of the methodology.

Rats were trained in Experiment 1 to find a submerged platform in 1 corner of either a rectangular or a kite-shaped pool. When the walls creating this corner were a different color than the opposite walls, then learning about the shape of the pool was potentiated in the kite but not in the rectangle. Experiments 2-4 revealed that learning about the rectangle can be overshadowed and blocked when information about the wall color indicates the location of the platform. The results mimic findings that have been obtained with Pavlovian conditioning, and they challenge the claim that learning about the shape of the environment takes places in a dedicated geometric module.