University of Zurich

About: University of Zurich is a education organization based out in Zurich, Switzerland. It is known for research contribution in the topics: Population & Transplantation. The organization has 50842 authors who have published 124042 publications receiving 5304521 citations. The organization is also known as: UZH & Uni Zurich.

Training Deep Spiking Neural Networks Using Backpropagation.

Abstract: Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

Patterned Delivery of Immunoglobulins to Surfaces Using Microfluidic Networks

Abstract: Microfluidic networks (microFNs) were used to pattern biomolecules with high resolution on a variety of substrates (gold, glass, or polystyrene). Elastomeric microFNs localized chemical reactions between the biomolecules and the surface, requiring only microliters of reagent to cover square millimeter-sized areas. The networks were designed to ensure stability and filling of the microFN and allowed a homogeneous distribution and robust attachment of material to the substrate along the conduits in the microFN. Immunoglobulins patterned on substrates by means of microFNs remained strictly confined to areas enclosed by the network with submicron resolution and were viable for subsequent use in assays. The approach is simple and general enough to suggest a practical way to incorporate biological material on technological substrates.

Fission-fusion dynamics: new research frameworks

Abstract: Renewed interest in fission‐fusion dynamics is due to the recognition that such dynamics may create unique challenges for social interaction and distinctive selective pressures acting on underlying communicative and cognitive abilities. New frameworks for integrating current knowledge on fission‐fusion dynamics emerge from a fundamental rethinking of the term “fission‐fusion” away from its current general use as a label for a particular modal type of social system (i.e., “fission‐fusion societies”). Specifically, because the degree of spatial and temporal cohesion of group members varies both within and across taxa, any social system can be described in terms of the extent to which it expresses fission‐fusion dynamics. This perspective has implications for socioecology, communication, cognitive demands, and human social evolution.