2.3k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- ETH ZurichCH61 papers
- University of ZurichCH59 papers
- SIB Swiss Institute of BioinformaticsCH13 papers
- Forschungszentrum JülichDE4 papers
- Italian Institute of TechnologyIT4 papers
- Universidad de SevillaES4 papers
- Intel (United States)US3 papers
- Swiss Federal Institute of Aquatic Science and TechnologyCH3 papers
- University of BernCH3 papers
- Accenture (United States)US2 papers
- Bielefeld UniversityDE2 papers
- California Institute of TechnologyUS2 papers
8 papers · 1 filter
A recipe for creating ideal hybrid memristive-CMOS neuromorphic computing systems
Elisabetta Chicca, Giacomo Indiveri
The development of memristive device technologies has reached a level of maturity to enable the design of complex and large-scale hybrid memristive-CMOS neural processing systems.…
EdgeDRNN: Enabling Low-latency Recurrent Neural Network Edge Inference
Chang Gao, Antonio Rios-Navarro, Xi Chen +2
This paper presents a Gated Recurrent Unit (GRU) based recurrent neural network (RNN) accelerator called EdgeDRNN designed for portable edge computing. EdgeDRNN adopts the spiking…
Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks
Charlotte Frenkel, Martin Lefebvre, David Bol
While the backpropagation of error algorithm enables deep neural network training, it implies (i) bidirectional synaptic weight transport and (ii) update locking until the forward…
Event-based Vision: A Survey
Guillermo Gallego, Tobi Delbruck, Garrick Orchard +8
Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness ch…
Event-Based Motion Segmentation by Motion Compensation
Timo Stoffregen, Guillermo Gallego, Tom Drummond +2
In contrast to traditional cameras, whose pixels have a common exposure time, event-based cameras are novel bio-inspired sensors whose pixels work independently and asynchronously…
Bayesian regression explains how human participants handle parameter uncertainty
Jannes Jegminat, Maya Jastrzebowska, Matt Pachai +2
The human brain copes with sensory uncertainty in accordance with Bayes' rule. However, it is unknown how the brain makes predictions in the presence of parameter uncertainty. Here…
The importance of space and time in neuromorphic cognitive agents
Giacomo Indiveri, Yulia Sandamirskaya
Artificial neural networks and computational neuroscience models have made tremendous progress, allowing computers to achieve impressive results in artificial intelligence (AI) app…
Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI
Georgia Koppe, Hazem Toutounji, Peter Kirsch +2
A major tenet in theoretical neuroscience is that cognitive and behavioral processes are ultimately implemented in terms of the neural system dynamics. Accordingly, a major aim for…