User profiles for Michael Pfeiffer
Michael PfeifferBosch Center for Artificial Intelligence Verified email at de.bosch.com Cited by 8068 |
[HTML][HTML] Deep learning with spiking neurons: Opportunities and challenges
M Pfeiffer, T Pfeil - Frontiers in neuroscience, 2018 - frontiersin.org
Spiking neural networks (SNNs) are inspired by information processing in biology, where
sparse and asynchronous binary signals are communicated and processed in a massively …
sparse and asynchronous binary signals are communicated and processed in a massively …
[HTML][HTML] Conversion of continuous-valued deep networks to efficient event-driven networks for image classification
Spiking neural networks (SNNs) can potentially offer an efficient way of doing inference
because the neurons in the networks are sparsely activated and computations are event-driven. …
because the neurons in the networks are sparsely activated and computations are event-driven. …
Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
Deep neural networks such as Convolutional Networks (ConvNets) and Deep Belief Networks
(DBNs) represent the state-of-the-art for many machine learning and computer vision …
(DBNs) represent the state-of-the-art for many machine learning and computer vision …
[HTML][HTML] Training deep spiking neural networks using backpropagation
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…
efficiency of deep neural networks through data-driven event-based computation. However…
STDP enables spiking neurons to detect hidden causes of their inputs
The principles by which spiking neurons contribute to the astounding computational power
of generic cortical microcircuits, and how spike-timing-dependent plasticity (STDP) of …
of generic cortical microcircuits, and how spike-timing-dependent plasticity (STDP) of …
Gland segmentation in colon histology images: The glas challenge contest
Colorectal adenocarcinoma originating in intestinal glandular structures is the most common
form of colon cancer. In clinical practice, the morphology of intestinal glands, including …
form of colon cancer. In clinical practice, the morphology of intestinal glands, including …
[HTML][HTML] Real-time classification and sensor fusion with a spiking deep belief network
Deep Belief Networks (DBNs) have recently shown impressive performance on a broad
range of classification problems. Their generative properties allow better understanding of the …
range of classification problems. Their generative properties allow better understanding of the …
Phased lstm: Accelerating recurrent network training for long or event-based sequences
Recurrent Neural Networks (RNNs) have become the state-of-the-art choice for extracting
patterns from temporal sequences. Current RNN models are ill suited to process irregularly …
patterns from temporal sequences. Current RNN models are ill suited to process irregularly …
Clinical acceptance and accuracy assessment of spinal implants guided with SpineAssist surgical robot: retrospective study
DP Devito, L Kaplan, R Dietl, M Pfeiffer, D Horne… - Spine, 2010 - journals.lww.com
Study Design. Retrospective, multicenter study of robotically-guided spinal implant insertions.
Clinical acceptance of the implants was assessed by intraoperative radiograph, and when …
Clinical acceptance of the implants was assessed by intraoperative radiograph, and when …
[HTML][HTML] Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity
The principles by which networks of neurons compute, and how spike-timing dependent
plasticity (STDP) of synaptic weights generates and maintains their computational function, are …
plasticity (STDP) of synaptic weights generates and maintains their computational function, are …