A burst-dependent algorithm for neuromorphic on-chip learning of spiking neural networks
The field of neuromorphic engineering addresses the high energy demands of neural networks through brain-inspired hardware for efficient neural network computing. For on-chip learning with spiking neural networks, neuromorphic hardware requires a local learning algorithm able to solve complex tasks....
Saved in:
| Main Authors: | Michael Stuck, Xingyun Wang, Richard Naud |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
|
| Series: | Neuromorphic Computing and Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2634-4386/adb511 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Real-Time Large-Scale Neural Connectivity Inference on Spiking Neuromorphic System
by: Daeyoung Kim, et al.
Published: (2025-01-01) -
Variation-resilient spike-timing-dependent plasticity in memristors using bursting neuron circuit
by: Yize Liu, et al.
Published: (2025-01-01) -
Neuromorphic Wireless Split Computing With Multi-Level Spikes
by: Dengyu Wu, et al.
Published: (2025-01-01) -
Synchronized stepwise control of firing and learning thresholds in a spiking randomly connected neural network toward hardware implementation
by: Kumiko Nomura, et al.
Published: (2024-11-01) -
Resource-dependent heterosynaptic spike-timing-dependent plasticity in recurrent networks with and without synaptic degeneration
by: James Humble
Published: (2025-07-01)