3 papers
cs.NE2026
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…
q-bio.NC2025
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…
cs.NE2025
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…