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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- Linked via arxiv authorJuwei Shen →
Dendritic In-Context Learning in a Single-Layer Spiking Neural Network
- Linked via arxiv authorYujie Wu →
Dendritic In-Context Learning in a Single-Layer Spiking Neural Network
- Linked via arxiv authorChangwen Chen →
Dendritic In-Context Learning in a Single-Layer Spiking Neural Network