18 citations · 40 across the 6 of their papers we have counts for
7 papers
Rewiring of adult neural networks - learning from structural plasticity across multiple scales
Michael Fauth, Han Lu, Jannik Luboeinski +36
Synaptic connections are continuously formed and eliminated even in adult neuronal networks. This process - called structural plasticity - allows networks to change their topology…
Tunable Synaptic Working Memory with Volatile Memristive Devices
Saverio Ricci, David Kappel, Christian Tetzlaff +2
Different real-world cognitive tasks evolve on different relevant timescales. Processing these tasks requires memory mechanisms able to match their specific time constants. In part…
Decision Making by a Neuromorphic Network of Volatile Resistive Switching Memories
Saverio Ricci, David Kappel, Christian Tetzlaff +2
The necessity of having an electronic device working in relevant biological time scales with a small footprint boosted the research of a new class of emerging memories. Ag-based vo…
Brian2Loihi: An emulator for the neuromorphic chip Loihi using the spiking neural network simulator Brian
Carlo Michaelis, Andrew B. Lehr, Winfried Oed +1
Developing intelligent neuromorphic solutions remains a challenging endeavour. It requires a solid conceptual understanding of the hardware's fundamental building blocks. Beyond th…
A synapse-centric account of the free energy principle
David Kappel, Christian Tetzlaff
The free energy principle (FEP) is a mathematical framework that describes how biological systems self-organize and survive in their environment. This principle provides insights o…
Robust trajectory generation for robotic control on the neuromorphic research chip Loihi
Carlo Michaelis, Andrew B. Lehr, Christian Tetzlaff
Neuromorphic hardware has several promising advantages compared to von Neumann architectures and is highly interesting for robot control. However, despite the high speed and energy…
Transfer entropy-based feedback improves performance in artificial neural networks
Sebastian Herzog, Christian Tetzlaff, Florentin Wörgötter
The structure of the majority of modern deep neural networks is characterized by uni- directional feed-forward connectivity across a very large number of layers. By contrast, the a…