We would like to announce our new paper on stable network function through stochastic plasticity. Summary: Networks of neurons in the brain are exposed to a multitude of internal and external changes and perturbations, to which they have to respond quickly in order to maintain stable functionality. In addition, experimental data suggest that these networks are simultaneously able to maintain structural constraints such as the empirically found connection probability between specific types of neurons, and heavy-tailed distributions of synaptic weights. Other experimental data point to surprising ongoing fluctuations in dendritic spines and spine volumes, to some extent even in the adult brain and in the absence of synaptic activity. In our paper "Network Plasticity as Bayesian Inference" we have shown that stochasticity of synaptic connection may support stable network function. It enables networks to sample parameters from some low-dimensional manifold in a high-dimensional parameter space that represents attractive combinations of structural constraints and a good fit to empirical evidence (e.g., sensory inputs). The resulting new theory of network plasticity explains from a functional perspective the experimentally observed ongoing fluctuations and structural priors that previously appeared to be quite puzzling, and provides a viable alternative to existing models that propose convergence of parameters to point estimates of their optimal values, e.g. to maximum likelihood values. A preprint of the paper is available at: http://arxiv.org/abs/1504.05143v1 The supplement at: http://www.igi.tugraz.at/kappel/pdfs/ms-541-suppl.pdf -- David Kappel Institute for Theoretical Computer Science Graz University of Technology Inffeldgasse 16b, A-8010 Graz, Austria Tel.: ++43/316/873-5847 http://www.igi.tugraz.at/kappel/