53 citations · 149 across the 20 of their papers we have counts for
6 papers · 1 filter
Language Modeling on a SpiNNaker 2 Neuromorphic Chip
Khaleelulla Khan Nazeer, Mark Schöne, Rishav Mukherji +4
As large language models continue to scale in size rapidly, so too does the computational power required to run them. Event-based networks on neuromorphic devices offer a potential…
Beyond Weights: Deep learning in Spiking Neural Networks with pure synaptic-delay training
Edoardo W. Grappolini, Anand Subramoney
Biological evidence suggests that adaptation of synaptic delays on short to medium timescales plays an important role in learning in the brain. Inspired by biology, we explore the…
Exploring hyper-parameter spaces of neuroscience models on high performance computers with Learning to Learn
Alper Yegenoglu, Anand Subramoney, Thorsten Hater +7
Neuroscience models commonly have a high number of degrees of freedom and only specific regions within the parameter space are able to produce dynamics of interest. This makes the…
Embodied Synaptic Plasticity with Online Reinforcement learning
Jacques Kaiser, Michael Hoff, Andreas Konle +8
The endeavor to understand the brain involves multiple collaborating research fields. Classically, synaptic plasticity rules derived by theoretical neuroscientists are evaluated in…
Reservoirs learn to learn
Anand Subramoney, Franz Scherr, Wolfgang Maass
We consider reservoirs in the form of liquid state machines, i.e., recurrently connected networks of spiking neurons with randomly chosen weights. So far only the weights of a line…
Long short-term memory and learning-to-learn in networks of spiking neurons
Guillaume Bellec, Darjan Salaj, Anand Subramoney +2
Recurrent networks of spiking neurons (RSNNs) underlie the astounding computing and learning capabilities of the brain. But computing and learning capabilities of RSNN models have…