Anand Subramoney
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
Spike frequency adaptation supports network computations on temporally dispersed information
D Salaj*, A Subramoney*, C Kraisnikovic*, G Bellec, R Legenstein, ...
Elife 10, e65459, 2021
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
Reservoirs learn to learn
A Subramoney, F Scherr, W Maass
Reservoir Computing: Theory, Physical Implementations, and Applications., 2020
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
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
Efficient recurrent architectures through activity sparsity and sparse back-propagation through time
A Subramoney, KK Nazeer, M Schöne, C Mayr, D Kappel
The Eleventh International Conference on Learning Representations, 2023
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
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
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
Beyond Weights: Deep learning in Spiking Neural Networks with pure synaptic-delay training
E Grappolini, A Subramoney
Proceedings of the 2023 International Conference on Neuromorphic Systems, 1-4, 2023
Language Modeling on a SpiNNaker 2 Neuromorphic Chip
KK Nazeer, M Schöne, R Mukherji, B Vogginger, C Mayr, D Kappel, ...
arXiv preprint arXiv:2312.09084, 2023
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, 2021.01. 25.428153, 2021
Igitugraz/l2l: v1.0.0-beta, March 2019
A Subramoney, S Diaz-Pier, A Rao, F Scherr, D Salaj, T Bohnstingl, ... 2590760, 2019
Activity sparsity complements weight sparsity for efficient RNN inference
R Mukherji, M Schöne, KK Nazeer, C Mayr, A Subramoney
arXiv preprint arXiv:2311.07625, 2023
Block-local learning with probabilistic latent representations
D Kappel, KK Nazeer, CT Fokam, C Mayr, A Subramoney
arXiv preprint arXiv:2305.14974, 2023
Efficient Real Time Recurrent Learning through combined activity and parameter sparsity
A Subramoney
ICLR 2023 Workshop on Sparsity in Neural Networks, 2023
Evaluating modular neuroevolution in robotic keepaway soccer
A Subramoney
The University of Texas at Austin, 2012
Self-supervised learning of probabilistic prediction through synaptic plasticity in apical dendrites: A normative model
A Rao, R Legenstein, A Subramoney, W Maass
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