no code implementations • 30 Mar 2024 • Elvin Hajizada, Balachandran Swaminathan, Yulia Sandamirskaya
Humans and animals learn throughout their lives from limited amounts of sensed data, both with and without supervision.
1 code implementation • 10 Apr 2023 • Jason Yik, Korneel Van den Berghe, Douwe den Blanken, Younes Bouhadjar, Maxime Fabre, Paul Hueber, Denis Kleyko, Noah Pacik-Nelson, Pao-Sheng Vincent Sun, Guangzhi Tang, Shenqi Wang, Biyan Zhou, Soikat Hasan Ahmed, George Vathakkattil Joseph, Benedetto Leto, Aurora Micheli, Anurag Kumar Mishra, Gregor Lenz, Tao Sun, Zergham Ahmed, Mahmoud Akl, Brian Anderson, Andreas G. Andreou, Chiara Bartolozzi, Arindam Basu, Petrut Bogdan, Sander Bohte, Sonia Buckley, Gert Cauwenberghs, Elisabetta Chicca, Federico Corradi, Guido de Croon, Andreea Danielescu, Anurag Daram, Mike Davies, Yigit Demirag, Jason Eshraghian, Tobias Fischer, Jeremy Forest, Vittorio Fra, Steve Furber, P. Michael Furlong, William Gilpin, Aditya Gilra, Hector A. Gonzalez, Giacomo Indiveri, Siddharth Joshi, Vedant Karia, Lyes Khacef, James C. Knight, Laura Kriener, Rajkumar Kubendran, Dhireesha Kudithipudi, Yao-Hong Liu, Shih-Chii Liu, Haoyuan Ma, Rajit Manohar, Josep Maria Margarit-Taulé, Christian Mayr, Konstantinos Michmizos, Dylan Muir, Emre Neftci, Thomas Nowotny, Fabrizio Ottati, Ayca Ozcelikkale, Priyadarshini Panda, Jongkil Park, Melika Payvand, Christian Pehle, Mihai A. Petrovici, Alessandro Pierro, Christoph Posch, Alpha Renner, Yulia Sandamirskaya, Clemens JS Schaefer, André van Schaik, Johannes Schemmel, Samuel Schmidgall, Catherine Schuman, Jae-sun Seo, Sadique Sheik, Sumit Bam Shrestha, Manolis Sifalakis, Amos Sironi, Matthew Stewart, Kenneth Stewart, Terrence C. Stewart, Philipp Stratmann, Jonathan Timcheck, Nergis Tömen, Gianvito Urgese, Marian Verhelst, Craig M. Vineyard, Bernhard Vogginger, Amirreza Yousefzadeh, Fatima Tuz Zohora, Charlotte Frenkel, Vijay Janapa Reddi
The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.
no code implementations • 5 Sep 2022 • Alpha Renner, Lazar Supic, Andreea Danielescu, Giacomo Indiveri, E. Paxon Frady, Friedrich T. Sommer, Yulia Sandamirskaya
The VO network we propose generates and stores a working memory of the presented visual environment.
no code implementations • 26 Aug 2022 • Alpha Renner, Lazar Supic, Andreea Danielescu, Giacomo Indiveri, Bruno A. Olshausen, Yulia Sandamirskaya, Friedrich T. Sommer, E. Paxon Frady
Understanding a visual scene by inferring identities and poses of its individual objects is still and open problem.
1 code implementation • 6 Mar 2022 • Eloy Parra-Barrero, Yulia Sandamirskaya
Representation is a key notion in neuroscience and artificial intelligence (AI).
no code implementations • 8 Aug 2021 • Antonio Vitale, Alpha Renner, Celine Nauer, Davide Scaramuzza, Yulia Sandamirskaya
Here, we explore how an event-based vision algorithm can be implemented as a spiking neuronal network on a neuromorphic chip and used in a drone controller.
no code implementations • 8 Aug 2020 • Sandro Baumgartner, Alpha Renner, Raphaela Kreiser, Dongchen Liang, Giacomo Indiveri, Yulia Sandamirskaya
We present a spiking neural network (SNN) for visual pattern recognition with on-chip learning on neuromorphichardware.
no code implementations • 17 Oct 2019 • Llewyn Salt, David Howard, Giacomo Indiveri, Yulia Sandamirskaya
The Lobula Giant Movement Detector (LGMD) is an identified neuron of the locust that detects looming objects and triggers the insect's escape responses.
1 code implementation • 9 Jul 2019 • Alpha Renner, Matthew Evanusa, Yulia Sandamirskaya
We present a fully event-driven vision and processing system for selective attention and tracking, realized on a neuromorphic processor Loihi interfaced to an event-based Dynamic Vision Sensor DAVIS.
no code implementations • 6 May 2019 • Bodo Rückauer, Nicolas Känzig, Shih-Chii Liu, Tobi Delbruck, Yulia Sandamirskaya
Mobile and embedded applications require neural networks-based pattern recognition systems to perform well under a tight computational budget.
no code implementations • 26 Feb 2019 • Giacomo Indiveri, Yulia Sandamirskaya
This efficiency and adaptivity gap is partially explained by the computing substrate of biological neural processing systems that is fundamentally different from the way today's computers are built.
no code implementations • 25 Oct 2018 • Sebastian Glatz, Julien N. P. Martel, Raphaela Kreiser, Ning Qiao, Yulia Sandamirskaya
In this paper, we present a spiking neural network architecture that uses sensory feedback to control rotational velocity of a robotic vehicle.
no code implementations • 17 Apr 2017 • Llewyn Salt, David Howard, Giacomo Indiveri, Yulia Sandamirskaya
We also investigate the use of Self-Adaptive Differential Evolution (SADE) which has been shown to ameliorate the difficulties of finding appropriate input parameters for DE.
no code implementations • 12 Oct 2012 • Sohrob Kazerounian, Matthew Luciw, Mathis Richter, Yulia Sandamirskaya
We introduce a dynamic neural algorithm called Dynamic Neural (DN) SARSA(\lambda) for learning a behavioral sequence from delayed reward.