21 citations · 30 across the 12 of their papers we have counts for
5 papers · 1 filter
Phase State Space Models: Parallel, Surrogate-Free Training of Spiking Networks
Wilkie Olin-Ammentorp
State-space models (SSMs) provide a powerful theoretical framework to enable parallel training of recurrent networks. We expand on previous work adapting SSMs to spiking models to…
More than MACs: Exploring the Role of Neuromorphic Engineering in the Age of LLMs
Wilkie Olin-Ammentorp
The introduction of large language models has significantly expanded global demand for computing; addressing this growing demand requires novel approaches that introduce new capabi…
Sparsifying Spiking Networks through Local Rhythms
Wilkie Olin-Ammentorp
It has been well-established that within conventional neural networks, many of the values produced at each layer are zero. In this work, I demonstrate that spiking neural networks…
Deep Phasor Networks: Connecting Conventional and Spiking Neural Networks
Wilkie Olin-Ammentorp, Maxim Bazhenov
In this work, we extend standard neural networks by building upon an assumption that neuronal activations correspond to the angle of a complex number lying on the unit circle, or '…
Stochasticity and Robustness in Spiking Neural Networks
Wilkie Olin-Ammentorp, Karsten Beckmann, Catherine D. Schuman +2
Artificial neural networks normally require precise weights to operate, despite their origins in biological systems, which can be highly variable and noisy. When implementing artif…