Výsledky vyhledávání - "Raphaela Kreiser"

Akademický článek

An On-chip Spiking Neural Network for Estimation of the Head Pose of the iCub Robot

Autor: Raphaela Kreiser, Alpha Renner, Vanessa R. C. Leite, Baris Serhan, Chiara Bartolozzi, Arren Glover, Yulia Sandamirskaya

Publikováno v: Frontiers in Neuroscience, Vol 14 (2020)

In this work, we present a neuromorphic architecture for head pose estimation and scene representation for the humanoid iCub robot. The spiking neuronal network is fully realized in Intel's neuromorphic research chip, Loihi, and precisely integrates

Externí odkaz: https://doaj.org/article/c1b5875607224a7581377681c2dceac4

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Akademický článek

Organizing Sequential Memory in a Neuromorphic Device Using Dynamic Neural Fields

Autor: Raphaela Kreiser, Dora Aathmani, Ning Qiao, Giacomo Indiveri, Yulia Sandamirskaya

Publikováno v: Frontiers in Neuroscience, Vol 12 (2018)

Neuromorphic Very Large Scale Integration (VLSI) devices emulate the activation dynamics of biological neuronal networks using either mixed-signal analog/digital or purely digital electronic circuits. Using analog circuits in silicon to physically em

Externí odkaz: https://doaj.org/article/034478c895034a449b4d7853c62e7a84

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Digital Multiplier-Less Spiking Neural Network Architecture of Reinforcement Learning in a Context-Dependent Task

Autor: Yulia Sandamirskaya, Hajar Asgari, Raphaela Kreiser, Babak Mazloom-Nezhad Maybodi

Neuromorphic engineers develop event-based spiking neural networks (SNNs) in hardware. These SNNs closer resemble the dynamics of biological neurons than conventional artificial neural networks and achieve higher efficiency thanks to the event-based,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ee5b17413009ab0f0372334f3996ac73
https://www.zora.uzh.ch/id/eprint/200376/

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Visual Pattern Recognition with on On-Chip Learning: Towards a Fully Neuromorphic Approach

Autor: Yulia Sandamirskaya, Raphaela Kreiser, Dongchen Liang, Alpha Renner, Giacomo Indiveri, Sandro Baumgartner

Publikováno v: ISCAS
2020 IEEE International Symposium on Circuits and Systems (ISCAS)
Scopus-Elsevier

We present a spiking neural network (SNN) for visual pattern recognition with on-chip learning on neuromorphichardware. We show how this network can learn simple visual patterns composed of horizontal and vertical bars sensed by a Dynamic Vision Sens

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ee7515bc9a9059a35f93066a1ce3be68
https://doi.org/10.1109/iscas45731.2020.9180628

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A Digital Multiplier-less Neuromorphic Model for Learning a Context-Dependent Task

Autor: Raphaela Kreiser, Hajar Asgari, Babak Mazloom-Nezhad Maybodi, Yulia Sandamirskaya

Publikováno v: AICAS

Highly efficient performance-resources trade-off of the biological brain is a motivation for research on neuromorphic computing. Neuromorphic engineers develop event-based spiking neural networks (SNNs) in hardware. Learning in SNNs is a challenging

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::30ace2444a7aa1acde84e43f61ea2a5a
https://www.zora.uzh.ch/id/eprint/188460/

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Error estimation and correction in a spiking neural network for map formation in neuromorphic hardware

Autor: Raphaela Kreiser, Yulia Sandamirskaya, Nuria Armengol, Alpha Renner, Gabriel Waibel

Publikováno v: ICRA

Neuromorphic hardware offers computing platforms for the efficient implementation of spiking neural networks (SNNs) that can be used for robot control. Here, we present such an SNN on a neuromorphic chip that solves a number of tasks related to simul

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::fe747c520b52ae2b59168eeccb6ba47d
https://doi.org/10.1109/icra40945.2020.9197498

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Neural State Machines for Robust Learning and Control of Neuromorphic Agents

Autor: Yulia Sandamirskaya, Raphaela Kreiser, Carsten Nielsen, Giacomo Indiveri, Dongchen Liang, Ning Qiao

Publikováno v: IEEE Journal on Emerging and Selected Topics in Circuits and Systems

Mixed-signal analog/digital neuromorphic circuits are characterized by ultra-low power consumption, real-time processing abilities, and low-latency response times. These features make them promising for robotic applications that require fast and powe

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::412db4f20de578a0f8af8f191729f7f6
https://www.zora.uzh.ch/id/eprint/184181/

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Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor

Autor: Ning Qiao, Raphaela Kreiser, Julien N. P. Martel, Yulia Sandamirskaya, Sebastian Glatz

Publikováno v: ICRA

Neuromorphic computing is a new paradigm for design of both the computing hardware and algorithms inspired by biological neural networks. The event-based nature and the inherent parallelism make neuromorphic computing a promising paradigm for buildin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d9179bc6bbee14a94133ee75dddddea
https://doi.org/10.5167/uzh-184193

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