3 citations · 6 across the 5 of their papers we have counts for
5 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…
A Lightweight Architecture for Real-Time Neuronal-Spike Classification
Muhammad Ali Siddiqi, David Vrijenhoek, Lennart P. L. Landsmeer +6
Electrophysiological recordings of neural activity in a mouse's brain are very popular among neuroscientists for understanding brain function. One particular area of interest is ac…
Tricking AI chips into Simulating the Human Brain: A Detailed Performance Analysis
Lennart P. L. Landsmeer, Max C. W. Engelen, Rene Miedema +1
Challenging the Nvidia monopoly, dedicated AI-accelerator chips have begun emerging for tackling the computational challenge that the inference and, especially, the training of mod…