Johannes Bill

TL;DR: A neural network model is proposed and it is shown by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time.

TL;DR: The potential of this work is showcased through the demonstration of successful learning in the presence of corrupted input data and probabilistic neurons, thus paving the way towards robust big-data processors.

TL;DR: The compound memristive synapse may provide a synaptic design principle for future neuromorphic architectures because its emergent synapse configuration represents the most salient features of the input distribution in a Mixture-of-Gaussians generative model.

TL;DR: It is shown how an ensemble of leaky integrate-and-fire neurons with conductance-based synapses embedded in a spiking environment can attain the correct firing statistics for sampling from a well-defined target distribution and establishes a rigorous link between deterministic neuron models and functional stochastic dynamics on the network level.