Dendritic In-Context Learning in a Single-Layer Spiking Neural Network
In-context learning (ICL) operates via implicit gradient descent embedded in the forward pass of modern AI architectures -- Transformers, Mamba, state-space models, and MLPs. Capturing this capability in biologically plausible Spiking Neural Networks (SNNs) has remained an open challenge: existing SNNs fail the Garg-2022 benchmark at non-trivial task dimensions. We trace this failure to a structural assumption: prior SNN designs route adaptation through inference-time synaptic plasticity, viewing the dendritic compartment as a passive conduit for error or teacher signals. We challenge this ass
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- PossiblePossibly related (embedding) · 54%Algorithm–hardware co-design of neuromorphic networks with dual memory pathways →
- LinkedLinked via arxiv author · 85%Juwei Shen →
“Dendritic In-Context Learning in a Single-Layer Spiking Neural Network”
- LinkedLinked via arxiv author · 85%Yujie Wu →
“Dendritic In-Context Learning in a Single-Layer Spiking Neural Network”
- LinkedLinked via arxiv author · 85%Changwen Chen →
“Dendritic In-Context Learning in a Single-Layer Spiking Neural Network”
- PossiblePossibly related (embedding) · 49%fangwei123456/spikingjelly →
- PossiblePossibly related (embedding) · 53%BindsNET/bindsnet →
- PossiblePossibly related (embedding) · 49%norse/norse →
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Dendritic In-Context Learning in a Single-Layer Spiking Neur” ≈ “aymericdamien/TopDeepLearning””