1 citations · 1 across the 2 of their papers we have counts for
4 papers
Spatial Spiking Neural Networks Enable Efficient and Robust Temporal Computation
Lennart P. L. Landsmeer, Amirreza Movahedin, Mario Negrello +2
The efficiency of modern machine intelligence depends on high accuracy with minimal computational cost. In spiking neural networks (SNNs), synaptic delays are crucial for encoding…
ADSEQ: A delay-aware autograd-compatible framework for spike-event delivery in SNNs
Lennart P. L. Landsmeer, Amirreza Movahedin, Said Hamdioui +1
Spiking neural networks (SNNs), central to computational neuroscience and neuromorphic machine learning (ML), require efficient simulation and gradient-based training. While AI acc…
Gradient Diffusion: Sensitivity-Matrix Co-Simulation Enables Activity Adaptation and Learnable Plasticity in Neural Simulators
Lennart P. L. Landsmeer, Mario Negrello, Said Hamdioui +1
Computational neuroscience relies on large-scale dynamical-systems models of neurons, with a vast amount of offline, pre-simulation, tuned parameters, with models often tied to the…
Four-Dimensional Computational Ultrasound Imaging of Brain Haemodynamics
Michael D. Brown, Bastian S. Generowicz, Stephanie Dijkhuizen +8
Four-dimensional ultrasound imaging of complex biological systems such as the brain is technically challenging because of the spatiotemporal sampling requirements. We present compu…