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  1. Book ; Thesis: Experimentelle Untersuchungen zur epikardialen Fixierung von Flächenelektroden des AICD-Systems

    Mauch, Christian

    1990  

    Author's details vorgelegt von Christian Mauch
    Size III, 64 Bl. : Ill., graph. Darst.
    Document type Book ; Thesis
    Thesis / German Habilitation thesis Hannover, Med. Hochschule, Diss., 1991
    HBZ-ID HT004162599
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    92 E 1715 order for loan Magazin

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  2. Article: Knie-TEP: Operation und postoperative Physiotherapie Sicht eines chirurgischen Orthopäden

    Mauch, Christian

    Zeitschrift für Physiotherapeuten

    2015  Volume 67, Issue 6, Page(s) 62

    Language German
    Document type Article
    ZDB-ID 2274399-6
    ISSN 1614-0397
    Database Current Contents Medicine

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    In stock of ZB MED Cologne/Königswinter

    Uk I Zs.161: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  3. Book ; Online: hxtorch.snn

    Spilger, Philipp / Arnold, Elias / Blessing, Luca / Mauch, Christian / Pehle, Christian / Müller, Eric / Schemmel, Johannes

    Machine-learning-inspired Spiking Neural Network Modeling on BrainScaleS-2

    2022  

    Abstract: Neuromorphic systems require user-friendly software to support the design and optimization of experiments. In this work, we address this need by presenting our development of a machine learning-based modeling framework for the BrainScaleS-2 neuromorphic ... ...

    Abstract Neuromorphic systems require user-friendly software to support the design and optimization of experiments. In this work, we address this need by presenting our development of a machine learning-based modeling framework for the BrainScaleS-2 neuromorphic system. This work represents an improvement over previous efforts, which either focused on the matrix-multiplication mode of BrainScaleS-2 or lacked full automation. Our framework, called hxtorch.snn, enables the hardware-in-the-loop training of spiking neural networks within PyTorch, including support for auto differentiation in a fully-automated hardware experiment workflow. In addition, hxtorch.snn facilitates seamless transitions between emulating on hardware and simulating in software. We demonstrate the capabilities of hxtorch.snn on a classification task using the Yin-Yang dataset employing a gradient-based approach with surrogate gradients and densely sampled membrane observations from the BrainScaleS-2 hardware system.
    Keywords Computer Science - Neural and Evolutionary Computing
    Subject code 006
    Publishing date 2022-12-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  4. Book ; Online ; Thesis: Charakterisierung der zellulären Immunantwort gegen die Hepatitis-Delta-Antigene nach DNA-Immunisierung in der Maus

    Mauch, Christian

    2003  

    Author's details vorgelegt von Christian Mauch
    Language German
    Size Online-Ressource
    Document type Book ; Online ; Thesis
    Thesis / German Habilitation thesis Univ., Diss--Freiburg (Breisgau), 2003
    Database Former special subject collection: coastal and deep sea fishing

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  5. Book ; Online: Extending BrainScaleS OS for BrainScaleS-2

    Müller, Eric / Mauch, Christian / Spilger, Philipp / Breitwieser, Oliver Julien / Klähn, Johann / Stöckel, David / Wunderlich, Timo / Schemmel, Johannes

    2020  

    Abstract: BrainScaleS-2 is a mixed-signal accelerated neuromorphic system targeted for research in the fields of computational neuroscience and beyond-von-Neumann computing. To augment its flexibility, the analog neural network core is accompanied by an embedded ... ...

    Abstract BrainScaleS-2 is a mixed-signal accelerated neuromorphic system targeted for research in the fields of computational neuroscience and beyond-von-Neumann computing. To augment its flexibility, the analog neural network core is accompanied by an embedded SIMD microprocessor. The BrainScaleS Operating System (BrainScaleS OS) is a software stack designed for the user-friendly operation of the BrainScaleS architectures. We present and walk through the software-architectural enhancements that were introduced for the BrainScaleS-2 architecture. Finally, using a second-version BrainScaleS-2 prototype we demonstrate its application in an example experiment based on spike-based expectation maximization.
    Keywords Computer Science - Neural and Evolutionary Computing
    Publishing date 2020-03-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  6. Book ; Online ; Thesis: Charakterisierung der zellulären Immunantwort gegen die Hepatitis-Delta-Antigene nach DNA-Immunisierung in der Maus

    Mauch, Christian [Verfasser]

    2003  

    Author's details vorgelegt von Christian Mauch
    Keywords Medizin, Gesundheit ; Medicine, Health
    Subject code sg610
    Document type Book ; Online ; Thesis
    Database Digital theses on the web

    Full text online

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  7. Article: A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware.

    Müller, Eric / Arnold, Elias / Breitwieser, Oliver / Czierlinski, Milena / Emmel, Arne / Kaiser, Jakob / Mauch, Christian / Schmitt, Sebastian / Spilger, Philipp / Stock, Raphael / Stradmann, Yannik / Weis, Johannes / Baumbach, Andreas / Billaudelle, Sebastian / Cramer, Benjamin / Ebert, Falk / Göltz, Julian / Ilmberger, Joscha / Karasenko, Vitali /
    Kleider, Mitja / Leibfried, Aron / Pehle, Christian / Schemmel, Johannes

    Frontiers in neuroscience

    2022  Volume 16, Page(s) 884128

    Abstract: Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This work presents the software aspects of this endeavor for the BrainScaleS-2 ... ...

    Abstract Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This work presents the software aspects of this endeavor for the BrainScaleS-2 system, a hybrid accelerated neuromorphic hardware architecture based on physical modeling. We introduce key aspects of the BrainScaleS-2 Operating System: experiment workflow, API layering, software design, and platform operation. We present use cases to discuss and derive requirements for the software and showcase the implementation. The focus lies on novel system and software features such as multi-compartmental neurons, fast re-configuration for hardware-in-the-loop training, applications for the embedded processors, the non-spiking operation mode, interactive platform access, and sustainable hardware/software co-development. Finally, we discuss further developments in terms of hardware scale-up, system usability, and efficiency.
    Language English
    Publishing date 2022-05-18
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2411902-7
    ISSN 1662-453X ; 1662-4548
    ISSN (online) 1662-453X
    ISSN 1662-4548
    DOI 10.3389/fnins.2022.884128
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Book ; Online: Inference with Artificial Neural Networks on Analog Neuromorphic Hardware

    Weis, Johannes / Spilger, Philipp / Billaudelle, Sebastian / Stradmann, Yannik / Emmel, Arne / Müller, Eric / Breitwieser, Oliver / Grübl, Andreas / Ilmberger, Joscha / Karasenko, Vitali / Kleider, Mitja / Mauch, Christian / Schreiber, Korbinian / Schemmel, Johannes

    2020  

    Abstract: The neuromorphic BrainScaleS-2 ASIC comprises mixed-signal neurons and synapse circuits as well as two versatile digital microprocessors. Primarily designed to emulate spiking neural networks, the system can also operate in a vector-matrix multiplication ...

    Abstract The neuromorphic BrainScaleS-2 ASIC comprises mixed-signal neurons and synapse circuits as well as two versatile digital microprocessors. Primarily designed to emulate spiking neural networks, the system can also operate in a vector-matrix multiplication and accumulation mode for artificial neural networks. Analog multiplication is carried out in the synapse circuits, while the results are accumulated on the neurons' membrane capacitors. Designed as an analog, in-memory computing device, it promises high energy efficiency. Fixed-pattern noise and trial-to-trial variations, however, require the implemented networks to cope with a certain level of perturbations. Further limitations are imposed by the digital resolution of the input values (5 bit), matrix weights (6 bit) and resulting neuron activations (8 bit). In this paper, we discuss BrainScaleS-2 as an analog inference accelerator and present calibration as well as optimization strategies, highlighting the advantages of training with hardware in the loop. Among other benchmarks, we classify the MNIST handwritten digits dataset using a two-dimensional convolution and two dense layers. We reach 98.0% test accuracy, closely matching the performance of the same network evaluated in software.
    Keywords Computer Science - Neural and Evolutionary Computing
    Publishing date 2020-06-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  9. Book ; Online: hxtorch

    Spilger, Philipp / Müller, Eric / Emmel, Arne / Leibfried, Aron / Mauch, Christian / Pehle, Christian / Weis, Johannes / Breitwieser, Oliver / Billaudelle, Sebastian / Schmitt, Sebastian / Wunderlich, Timo C. / Stradmann, Yannik / Schemmel, Johannes

    PyTorch for BrainScaleS-2 -- Perceptrons on Analog Neuromorphic Hardware

    2020  

    Abstract: We present software facilitating the usage of the BrainScaleS-2 analog neuromorphic hardware system as an inference accelerator for artificial neural networks. The accelerator hardware is transparently integrated into the PyTorch machine learning ... ...

    Abstract We present software facilitating the usage of the BrainScaleS-2 analog neuromorphic hardware system as an inference accelerator for artificial neural networks. The accelerator hardware is transparently integrated into the PyTorch machine learning framework using its extension interface. In particular, we provide accelerator support for vector-matrix multiplications and convolutions; corresponding software-based autograd functionality is provided for hardware-in-the-loop training. Automatic partitioning of neural networks onto one or multiple accelerator chips is supported. We analyze implementation runtime overhead during training as well as inference, provide measurements for existing setups and evaluate the results in terms of the accelerator hardware design limitations. As an application of the introduced framework, we present a model that classifies activities of daily living with smartphone sensor data.
    Keywords Computer Science - Neural and Evolutionary Computing
    Subject code 006
    Publishing date 2020-06-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  10. Article: Demonstrating Advantages of Neuromorphic Computation: A Pilot Study.

    Wunderlich, Timo / Kungl, Akos F / Müller, Eric / Hartel, Andreas / Stradmann, Yannik / Aamir, Syed Ahmed / Grübl, Andreas / Heimbrecht, Arthur / Schreiber, Korbinian / Stöckel, David / Pehle, Christian / Billaudelle, Sebastian / Kiene, Gerd / Mauch, Christian / Schemmel, Johannes / Meier, Karlheinz / Petrovici, Mihai A

    Frontiers in neuroscience

    2019  Volume 13, Page(s) 260

    Abstract: Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency. We employ a ... ...

    Abstract Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency. We employ a single-chip prototype of the BrainScaleS 2 neuromorphic system to implement a proof-of-concept demonstration of reward-modulated spike-timing-dependent plasticity in a spiking network that learns to play a simplified version of the Pong video game by smooth pursuit. This system combines an electronic mixed-signal substrate for emulating neuron and synapse dynamics with an embedded digital processor for on-chip learning, which in this work also serves to simulate the virtual environment and learning agent. The analog emulation of neuronal membrane dynamics enables a 1000-fold acceleration with respect to biological real-time, with the entire chip operating on a power budget of 57 mW. Compared to an equivalent simulation using state-of-the-art software, the on-chip emulation is at least one order of magnitude faster and three orders of magnitude more energy-efficient. We demonstrate how on-chip learning can mitigate the effects of fixed-pattern noise, which is unavoidable in analog substrates, while making use of temporal variability for action exploration. Learning compensates imperfections of the physical substrate, as manifested in neuronal parameter variability, by adapting synaptic weights to match respective excitability of individual neurons.
    Language English
    Publishing date 2019-03-26
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2411902-7
    ISSN 1662-453X ; 1662-4548
    ISSN (online) 1662-453X
    ISSN 1662-4548
    DOI 10.3389/fnins.2019.00260
    Database MEDical Literature Analysis and Retrieval System OnLINE

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