PUBLICATIONS

[1]
Acharya J., Basu A., Legenstein R., Limbacher T., Poirazi P., Wu X. (2022). Dendritic computing: branching deeper into machine learning. Neuroscience, 489, 275-289.
[2]
Argyris, A. (2022). Photonic neuromorphic technologies in optical communications. Nanophotonics 11, 5, 897-916.
[3]
Donati, G., Mirasso, C.R., Mancinelli, M., Pavesi, L. and Argyris, A. (2022). Microring resonators with external optical feedback for time delay reservoir computing. Optics Express 30, 1, 522-537.
[4]
Ferrand R., Baronig M., Limbacher T., Legenstein R. (2023). Context-dependent computations in spiking neural networks with apical modulation. Submitted for publication.
[5]
Goldmann, M., Mirasso, C.R., Fischer, I., Soriano, M.C. (2022). Learn one size to infer all: Exploiting translational symmetries in delay-dynamical and spatiotemporal systems using scalable neural networks. Physical Review E 106, 044211.
[6]
Kulvicius, T., Tamosiunaite, M., Wörgötter, F. (2022). Combining optimal path search with task-dependent learning in a neural network. (http://arxiv.org/abs/2201.11104) under review and now with minor revisions at IEEE TNNLS.
[7]
Möller K, Kappel D, Tamosiunaite M, Tetzlaff C, Porr B, Wörgötter F (2022). Differential Hebbian learning with time-continuous signals for active noise reduction. PLoS ONE 17(5): e0266679.
[8]
Ortín, S., Soriano, M.C., Tetzlaff, C., Wörgötter, F., Fischer, I., Mirasso C.R., Argyris, A. (2023). Implementation of input correlation learning with an optoelectronic dendritic unit. Frontiers in Physics 11, 1112295.
[9]
Ortín, S., Soriano, M.C., Fischer, I., Mirasso C.R., Argyris, A. (2022). Optical dendrites for spatio-temporal computing with few-mode fibers. Optical Materials Express 12, 5, 1907-1919.
[10]
Tamosiunaite, M., Kulvicius, T. and Wörgötter, F. (2022). Bootstrapping Concept Formation in Small Neural Networks. IEEE Transactions on Cognitive and Developmental Systems.
[11]
Tamosiunaite, M., Tetzlaff, C. and Wörgötter, F. (2023). Unsupervised Learning of Perceptual Feature Combinations. Scientific Reports (submitted).


PATENTS

[1]
Wörgötter, F. and Tamosiunaite (inventors, 2022). Verfahren und Vorrichtung zur Ermittlung eines zeitlichen Versatzes zwischen Signalen an verschiedenen Signaleingängen. Patent submitted, 12.12. 2022, AZ.: DE102022132996.8.

Short description:
The ADSYNC technology provides a fast and adaptive method for the synchronization of different signals (skew compensation) for any electrical or optical system up to the GHz regime.

For more infomation, read this PDF.


WORKSHOPS

ADOPD has organized a workshop on algorithms for neuromorphic computing (6th-8th of September, 2022; https://neal2022.tetzlab.com/), discussing the interdependence between hardware and algorithms - also from the point of view of applications such as robotics. Furthermore, new results and insights from the scientific fields of machine learning and computational neuroscience, including dendritic computation, have been presented and discussed to stimulate the exchange of algorithmic principles that could advance neuromorphic computing. Taken together, this workshop brought together researchers from different scientific disciplines, each providing novel and interesting insights on this spectrum of perspectives on neuromorphic algorithms. We think that this workshop was a successful starting point to facilitate the coming together of a community focused specifically on these questions of neuromorphic algorithms at the interdisciplinary interface.

Workshop publications

[1]
Tetzlaff, C. (2023). Adaptive Neuromorphic computing on chips and photonics. At the: 20th German-American Frontiers of Engineering Symposium – On the way towards sustainability and resiliency, Humboldt foundation and the American National Academy of Engineering.
[2]
Ortín, S., Soriano, M.C., Fischer, I., Argyris, A, and Mirasso, C.R. (2023). Study and implementation of optical dendritic units. At the: XI Ibero-American Meeting of Optics/XIV Latin American Meeting of Optics, Lasers and Applications - RIAO OPTILAS 2023, in San Jose, Costa Rica.
[3]
Ortín, S., Soriano, M.C., Fischer, I., Mirasso, C.R. and Argyris, A. (2023). Few-mode fibers as optical dendrites for 40 Gbps computing. At the: SPIE Photonics West 2023 Conference, in San Francisco, U.S.A.
[4]
Soriano, M.C. (2022). Dendritic-like computation using optical fibres. At the: Rank Prize Symposium on Neuromorphic Photonics, Grasmere, UK.
[5]
Argyris, A. (2022). Photonic neuromorphic techniques for fast optical computing. At the: Materials for neuromorphic circuits 2022, Zaragoza, Spain, (EC ITN MANIC, Grant agreement: 861153), invited presentation.
[6]
Ortín, S., Soriano, M.C., Fischer, I., Mirasso, C.R. and Argyris, A. (2022). Dendritic-like computation using multimode optical fibers. At the: 5th International Conference on Application of Optics and Photonics (AOP 2022), Guimaraes, Portugal.
[7]
Ortín, S., Soriano, M.C., Fischer, I., Mirasso, C.R. and Argyris, A. (2022). Dendritic-like computation using multimode optical fibers. At the: 5th International Conference on Application of Optics and Photonics (AOP 2022), Guimaraes, Portugal.
[8]
Legenstein, R. (2022). On Memory in Spiking Neural Networks. At the: Fuerberg Workshop 2022, Fuerberg, Austria. Invited Talk.
[9]
Legenstein R. (2022). Spiking Neural Networks for Neuromorphic Computing. At the: 15th International Conference on Brain Informatics, Padova, Italy, keynote lecture.


PUBLIC TALKS

 
20.5.2022: Ferrand R. TU-Graz. A model for the interaction of synaptic and non-synaptic plasticity in memory-dependent computation in the brain. Poster presentation at the “Networking event of the Styrian Brain Research Initiative (INGE St.) 2022”, Graz, Austria.
 
2.7.2022: Legenstein R. TU-Graz. Discussion round at Telluride workshop “Future of Neuromorphic Engineering”. online.
 
24.11.2022: Legenstein R. TU-Graz. AI and Neuroscience. Invited talk at ASAI workshop. Linsberg, Austria.