Abstract.Starting from the idea that neural group activity as such is unlikely to be immediately relevant for neural synchronization, we investigate mechanisms that act at the level of individual nerve impulses (spikes). Hence, we consider populations of formal spike-emitting 'leaky integrate and fire' neurons instead of networks built from non-spiking oscillators. After outlining the principle of synchronization for basic forms of recurrent impulse coupling by using a pair of simplified formal neurons, we show that local lateral inhibition results in robust impulse synchronization in networks with nonvanishing transmission delays.

No abstract

No abstract

No abstract

In augmented reality applications, consistent illumination between virtual and real objects is important for creatingan immersive user experience. Consistent illumination can be achieved by appropriate parameterisation of thevirtual illumination model, that is consistent with real-world lighting conditions. In this study, we developed amethod to reconstruct the general light direction from red-green-blue (RGB) images of real-world scenes using amodified VGG-16 neural network. We reconstructed the general light direction as azimuth and elevation angles. Toavoid inaccurate results caused by coordinate uncertainty occurring at steep elevation angles, we further introducedstereographically projected coordinates. Unlike recent deep-learning-based approaches for reconstructing the lightsource direction, our approach does not require depth information and thus does not rely on special red-green-blue-depth (RGB-D) images as input.