13 citations · 46 across the 16 of their papers we have counts for
7 papers · 1 filter
The Role of Temporal Hierarchy in Spiking Neural Networks
Filippo Moro, Pau Vilimelis Aceituno, Laura Kriener +1
Spiking Neural Networks (SNNs) have the potential for rich spatio-temporal signal processing thanks to exploiting both spatial and temporal parameters. The temporal dynamics such a…
DelGrad: Exact event-based gradients for training delays and weights on spiking neuromorphic hardware
Julian Göltz, Jimmy Weber, Laura Kriener +5
Spiking neural networks (SNNs) inherently rely on the timing of signals for representing and processing information. Incorporating trainable transmission delays, alongside synaptic…
Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks
Arianna Rubino, Matteo Cartiglia, Melika Payvand +1
Mixed-signal neuromorphic systems represent a promising solution for solving extreme-edge computing tasks without relying on external computing resources. Their spiking neural netw…
Synaptic metaplasticity with multi-level memristive devices
Simone D'Agostino, Filippo Moro, Tifenn Hirtzlin +5
Deep learning has made remarkable progress in various tasks, surpassing human performance in some cases. However, one drawback of neural networks is catastrophic forgetting, where…
Hardware calibrated learning to compensate heterogeneity in analog RRAM-based Spiking Neural Networks
Filippo Moro, E. Esmanhotto, T. Hirtzlin +10
Spiking Neural Networks (SNNs) can unleash the full power of analog Resistive Random Access Memories (RRAMs) based circuits for low power signal processing. Their inherent computat…
Online Training of Spiking Recurrent Neural Networks with Phase-Change Memory Synapses
Yigit Demirag, Charlotte Frenkel, Melika Payvand +1
Spiking recurrent neural networks (RNNs) are a promising tool for solving a wide variety of complex cognitive and motor tasks, due to their rich temporal dynamics and sparse proces…