A5058735145- Advanced Memory and Neural Computing
- Ferroelectric and Negative Capacitance Devices
- Neural Networks and Reservoir Computing
- Neural dynamics and brain function
- Complex Systems and Time Series Analysis
- Time Series Analysis and Forecasting
- Dust and Plasma Wave Phenomena
- Modular Robots and Swarm Intelligence
- Laser-induced spectroscopy and plasma
- Particle Dynamics in Fluid Flows
- Ecosystem dynamics and resilience
- CCD and CMOS Imaging Sensors
- Neurobiology and Insect Physiology Research
- Engineering Applied Research
- Nonlinear Dynamics and Pattern Formation
Kirchhoff (Germany)
2019-2022
Heidelberg University
2018-2022
Institute for Physics
2019-2020
Since the beginning of information processing by electronic components, nervous system has served as a metaphor for organization computational primitives. Brain-inspired computing today encompasses class approaches ranging from using novel nano-devices computation to research into large-scale neuromorphic architectures, such TrueNorth, SpiNNaker, BrainScaleS, Tianjic, and Loihi. While implementation details differ, spiking neural networks-sometimes referred third generation networks-are...
Significance Neuromorphic systems aim to accomplish efficient computation in electronics by mirroring neurobiological principles. Taking advantage of neuromorphic technologies requires effective learning algorithms capable instantiating high-performing neural networks, while also dealing with inevitable manufacturing variations individual components, such as memristors or analog neurons. We present a framework resulting bioinspired spiking networks high performance, low inference latency,...
Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with aim replicating its hallmark functional capabilities in terms computational power, robust learning energy efficiency. We employ a single-chip prototype BrainScaleS 2 neuromorphic system implement proof-of-concept demonstration reward-modulated spike-timing-dependent plasticity spiking network that learns play simplified version Pong video game by smooth pursuit. This combines electronic...
We present first experimental results on the novel BrainScaleS-2 neuromorphic architecture based an analog neuro-synaptic core and augmented by embedded microprocessors for complex plasticity experiment control. The high acceleration factor of 1000 compared to biological dynamics enables execution computationally expensive tasks, allowing fast emulation long-duration experiments or rapid iteration over many consecutive trials. flexibility our is demonstrated in a suite five distinct...
In computational neuroscience, as well in machine learning, neuromorphic devices promise an accelerated and scalable alternative to neural network simulations. Their connectivity synaptic capacity depend on their specific design choices, but is always intrinsically limited. Here, we present a strategy achieve structural plasticity that optimizes resource allocation under these constraints by constantly rewiring the pre- postsynaptic partners while keeping neuronal fan-in constant connectome...
The evolution of biological brains has always been contingent on their embodiment within respective environments, in which survival required appropriate navigation and manipulation skills. Studying such interactions thus represents an important aspect computational neuroscience and, by extension, a topic interest for neuromorphic engineering. Here, we present three examples the BrainScaleS-2 architecture, dynamical timescales both agents environment are accelerated several orders magnitude...
Bees display the remarkable ability to return home in a straight line after meandering excursions their environment. Neurobiological imaging studies have revealed that this capability emerges from path integration mechanism implemented within insect's brain. In present work, we emulate neural network on neuromorphic mixed-signal processor BrainScaleS-2 guide bees, virtually embodied digital co-processor, back location randomly exploring To realize underlying integrators, introduce...
To rapidly process temporal information at a low metabolic cost, biological neurons integrate inputs as an analog sum but communicate with spikes, binary events in time. Analog neuromorphic hardware uses the same principles to emulate spiking neural networks exceptional energy-efficiency. However, instantiating high-performing on such remains significant challenge due device mismatch and lack of efficient training algorithms. Here, we introduce general in-the-loop learning framework based...
The Fourier phase information play a key role for the quantified description of nonlinear data. We present novel tool time series analysis that identifies nonlinearities by sensitively detecting correlations among phases. method, being called walk analysis, is based on well established measures from random which are now applied to unwrapped phases series. provide an analytical its functionality and demonstrate capabilities systematically controlled leptokurtic noise. Hereby, we investigate...
Since the beginning of information processing by electronic components, nervous system has served as a metaphor for organization computational primitives. Brain-inspired computing today encompasses class approaches ranging from using novel nano-devices computation to research into large-scale neuromorphic architectures, such TrueNorth, SpiNNaker, BrainScaleS, Tianjic, and Loihi. While implementation details differ, spiking neural networks - sometimes referred third generation are common...
In computational neuroscience, as well in machine learning, neuromorphic devices promise an accelerated and scalable alternative to neural network simulations. Their connectivity synaptic capacity depends on their specific design choices, but is always intrinsically limited. Here, we present a strategy achieve structural plasticity that optimizes resource allocation under these constraints by constantly rewiring the pre- gpostsynaptic partners while keeping neuronal fan-in constant...
We demonstrate five experiments that are performed on the novel BrainScaleS-2 neuromorphic architecture based an analog neuro-synaptic core and augmented by embedded microprocessors for complex plasticity experiment control. The high acceleration factor of 1000 compared to biological dynamics enables execution computationally expensive tasks, allowing fast emulation long-duration or rapid iteration over many consecutive trials. were chosen emphasize different aspects system, especially...