In this work we propose a unidirectional post-synaptic potential dependent learning rule that is only triggered by pre-synaptic spikes, and easy to implement on ...
Jan 5, 2017 · This learning rule is extremely hardware efficient due to its dependence only on the presynaptic spike timing and not on the postsynaptic spike ...
Jan 5, 2017 · Further more, we demonstrate that this learning rule can be used to learn and classify spatio-temporal spike patterns in an unsupervised manner ...
Membrane-dependent neuromorphic learning rule for unsupervised spike pattern detection. S. Sheik, S. Paul, C. Augustine, and G. Cauwenberghs. BioCAS, page ...
Further more, we demonstrate that this learning rule can be used to learn and classify spatio-temporal spike patterns in an unsupervised manner using individual ...
Spike-timing-dependent plasticity (STDP) is an unsupervised brain-like learning rule implemented in many SNNs and neuromorphic chips. However, a significant ...
Membrane-Dependent Neuromorphic Learning Rule for Unsupervised Spike Pattern Detection · no code implementations • 5 Jan 2017 • Sadique Sheik, Somnath Paul, ...
Sep 24, 2021 · Spike time-dependent plasticity (STDP) is the most well-known learning rule for unsupervised learning in the brain, which is implemented in many ...
Mar 16, 2024 · We proposed an end-to-end hybrid unsupervised framework for training deep CNNs that can be potentially implemented in a neuromorphic setting.
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Deep unsupervised learning using spike-timing-dependent plasticity
iopscience.iop.org › article › pdf
May 7, 2024 · Spike-timing-dependent plasticity (STDP) is an unsupervised learning mechanism for spiking neural networks that has received significant ...