Abstract.A model is developed by which path integration as observed in many animal species could be implemented neurobiologically. The proposed architecture is able to describe the navigation behaviour of Cataglyphis ants, and that of other social insects, at the level of interacting neurons. The basic idea of this architecture is the concept of activity patterns travelling along neural chains. Although experimental evidence has yet to be provided, this concept seems biologically plausible and not limited to the navigation problem. Neural chains are able to represent variables by activity patterns with high accuracy and temporal stability. Moreover, they are able to integrate incremental signals with high precision. Cyclical chains of neurons show superior performance as soon as cyclical variables are to be represented and integrated. Finally, representation of cyclical variables by travelling activity peaks allows simple approximations of goniometric functions as they are used in path integration systems.

We report an experimental study on the effect of exercise on tendon structure in mice. After one week of physical training an increase in mean diameter, in number, and in cross-sectional area, as well as a change in mean fibril diameter distribution, was demonstrated. In the long-term, there was an increase in fibril number, a fall in mean diameter, but no statistically significant changes in the relative cross-sectional area per unit compared with the control tendons.

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The fast simulation of large networks of spiking neurons is a major task for the examination of biology-inspired vision systems. Networks of this type label features by synchronization of spikes and there is strong demand to simulate these e ects in real world environments. As the calculations for one model neuron are complex, the digital simulation of large networks is not e cient using existing simulation systems. Consequently, it is necessary to develop special simulation techniques. This article introduces a wide range of concepts for the di erent parts of digital simulator systems for large vision networks and presents accelerators based on these foundations. Pulse-Coded Neural Vision NetworksThe communication in PCNNs is based on spike e x c hange. In contrast to conventional model neurons, e.g. McCulloch & Pitts neurons, the generation of a spike requires high computational e ort in connection with the time behaviour in the biological example. The computational e ort for individual neuron calculations compared to whole network processing is much higher in PCNNs than in conventional ANNs.Common simulation techniques for neural networks make use of vector representations for the neurons and matrix representations for the connection network 11]. These techniques are not suitable for PCNNs because the actual activity of one neuron is not representable by o n l y o n e v alue. Hence, common simulation techniques based on the acceleration of matrix-vector-calculations are not su cient for PCNNs. A new simulation paradigm is required with