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Yulia Sandamirskaya
Researcher at University of Zurich
Publications - 92
Citations - 1597
Yulia Sandamirskaya is an academic researcher from University of Zurich. The author has contributed to research in topics: Neuromorphic engineering & Spiking neural network. The author has an hindex of 17, co-authored 92 publications receiving 1037 citations. Previous affiliations of Yulia Sandamirskaya include Ruhr University Bochum & University of Wisconsin-Madison.
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Journal ArticleDOI
Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook
Michael Davies,Andreas Wild,Garrick Orchard,Yulia Sandamirskaya,Gabriel A. Fonseca Guerra,Prasad Joshi,Philipp Plank,Sumedh R. Risbud +7 more
TL;DR: Loihi as mentioned in this paper is a neuromorphic research processor designed to support a broad range of spiking neural networks with sufficient scale, performance, and features to deliver competitive results compared to state-of-the-art contemporary computing architectures.
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An embodied account of serial order: How instabilities drive sequence generation
TL;DR: This work proposes an architecture in which dynamic neural networks create stable states at each stage of a sequence by exploiting neural attractors triggered by a neural representation of a condition of satisfaction for each action.
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Dynamic neural fields as a step toward cognitive neuromorphic architectures
TL;DR: The relationship between DFT and soft WTA networks is leveraged to systematically revise and integrate established DFT mechanisms that have previously been spread among different architectures to generate behavior and autonomous learning.
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Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System
Moritz B. Milde,Hermann Blum,Alexander Dietmüller,Dora Sumislawska,Jörg Conradt,Giacomo Indiveri,Yulia Sandamirskaya +6 more
TL;DR: This work interfaced a mixed-signal analog-digital neuromorphic processor ROLLS to a neuromorphic dynamic vision sensor mounted on a robotic vehicle and developed an autonomous neuromorphic agent that is able to perform neurally inspired obstacle-avoidance and target acquisition.
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Using Dynamic Field Theory to extend the embodiment stance toward higher cognition
TL;DR: Instances of representation that stand for perceptual objects, motor plans, or action intentions are peaks of activation in the DNFs and it is shown how such peaks may arise from input and are stabilized by intra-field interaction.