Dr. Michael Pfeiffer

Spiking Deep Neural Networks

We have developed some of the first spiking implementations of deep neural networks for visual object recognition and sensory fusion. These networks allow to utilize the power and accuracy of deep learning models in real time, with short latencies and with much higher efficiency in terms of power requirements and necessary update steps. They are also ideal models for processing neuromorphic sensor inputs, and for implementations on special purpose hardware platforms, such as SpiNNaker.
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Spike-based computation and learning in distributed neuromorphic systems

Together with Giacomo Indiveri I am leading this project, sponsored by the Swiss National Science Foundation (SNSF). The project investigates ways of configuring neuromorphic computing devices to enable functions such as learning, state-dependent computation, and probabilistic inference.
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A computational analysis of primate-specific features of corticogenesis

Marion Betizeau has been awarded a Transition Postdoc Fellowship by SystemsX.ch to investigate new data analysis methods based on Hidden Markov Trees to analyze cell lineages in primate cortical development.
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Last changed: 03 December 2015