Anand Subramoney
Anand Subramoney
Institut für Neuroinformatik, Ruhr-Universität Bochum
Verified email at - Homepage
Cited by
Cited by
Long short-term memory and learning-to-learn in networks of spiking neurons
G Bellec*, D Salaj*, A Subramoney*, R Legenstein, W Maass
Advances in Neural Information Processing Systems 31, 787--797, 2018
A solution to the learning dilemma for recurrent networks of spiking neurons
G Bellec*, F Scherr*, A Subramoney, E Hajek, D Salaj, R Legenstein, ...
Nature Communications 11 (1), 3625, 2020
Scaling up liquid state machines to predict over address events from dynamic vision sensors
J Kaiser, R Stal, A Subramoney, A Roennau, R Dillmann
Bioinspiration & biomimetics 12 (5), 055001, 2017
Pattern representation and recognition with accelerated analog neuromorphic systems
MA Petrovici, S Schmitt, J Klähn, D Stöckel, A Schroeder, G Bellec, J Bill, ...
2017 IEEE International Symposium on Circuits and Systems (ISCAS), 1-4, 2017
Spike frequency adaptation supports network computations on temporally dispersed information
D Salaj*, A Subramoney*, C Kraisnikovic*, G Bellec, R Legenstein, ...
Elife 10, e65459, 2021
Reservoirs learn to learn
A Subramoney, F Scherr, W Maass
Reservoir Computing: Theory, Physical Implementations, and Applications., 2020
Embodied Synaptic Plasticity With Online Reinforcement Learning
J Kaiser*, M Hoff*, A Konle, JC Vasquez Tieck, D Kappel, D Reichard, ...
Frontiers in Neurorobotics 13, 81, 2019
Task decomposition with neuroevolution in extended predator-prey domain
A Jain, A Subramoney, R Miikulainen
The Thirteenth International Conference on the Synthesis and Simulation of …, 2012
Eligibility traces provide a data-inspired alternative to backpropagation through time
G Bellec*, F Scherr*, E Hajek, D Salaj, A Subramoney, R Legenstein, ...
NeurIPS 2019 workshop "Real Neurons & Hidden Units: Future directions at the …, 2019
Evaluating modular neuroevolution in robotic keepaway soccer
A Subramoney
The University of Texas at Austin, 2012
Revisiting the role of synaptic plasticity and network dynamics for fast learning in spiking neural networks
A Subramoney, G Bellec, F Scherr, R Legenstein, W Maass
bioRxiv, 2021
Slow processes of neurons enable a biologically plausible approximation to policy gradient.
A Subramoney*, F Scherr*, G Bellec*, E Hajek, D Salaj, R Legenstein, ...
NeurIPS 2019 workshop on Biological and Artificial Reinforcement Learning, 2019
Learning to Learn on High Performance Computing
A Yegenoglu, W Maas, M Herty, W Klijn, G Visconti, A Subramoney, ...
Society for Neuroscience Meeting 2019, 2019
EGRU: Event-based GRU for activity-sparse inference and learning
A Subramoney, KK Nazeer, M Schöne, C Mayr, D Kappel
arXiv preprint arXiv:2206.06178, 2022
Exploring parameter and hyper-parameter spaces of neuroscience models on high performance computers with Learning to Learn
A Yegenoglu, A Subramoney, T Hater, C Jimenez-Romero, W Klijn, ...
Frontiers in Computational Neuroscience, 46, 2022
A normative framework for learning top-down predictions through synaptic plasticity in apical dendrites
A Rao*, R Legenstein*, A Subramoney, W Maass
bioRxiv, 2021
Biologically plausible learning and meta-learning in recurrent networks of spiking neurons
A Subramoney
Graz University of Technology, 2020
Spike-frequency adaptation contributes long short-term memory to networks of spiking neurons
A Subramoney*, C Kraisnikovic*, D Salaj*, G Bellec, R Legenstein, ...
Bernstein Conference, 2020
IGITUGraz/L2L: L2L Gradient-free Optimization Framework v0.4.3
A Subramoney, A Rao, F Scherr, D Salaj, T Bohnstingl, J Jordan, N Kopp, ..., 2019
IGITUGraz/spore-nest-module: SPORE version 2.14.0
D Kappel, M Hoff, A Subramoney, 2017
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