dendritic-icl-snn

name	dendritic-icl-snn
description	DendriCL methodology for dendritic in-context learning in single-layer spiking neural networks. Demonstrates that a single dendritic compartment with online-LMS dynamics is sufficient for general-purpose ICL without attention, depth, or inference-time plasticity.
tags	["spiking-neural-network","in-context-learning","dendritic-computation","neuromorphic","biological-plausibility"]
arxiv_id	2607.02283
authors	["Juwei Shen","Yujie Wu","Changwen Chen"]
published	2026-07-02

Dendritic In-Context Learning in Single-Layer SNN (DendriCL)

Core Insight

In-context learning requires neither attention, depth, nor inference-time plasticity: a single compartment with online-LMS dynamics is sufficient.

This paper challenges the fundamental assumption that ICL in SNNs requires synaptic plasticity during inference. Instead, it demonstrates that the subthreshold dynamics of a single dendritic compartment already implement a complete online learning algorithm.

Key Contributions

1. Paradigm Shift: Dendrite as Computational Substrate

• Previous assumption: Dendrites are passive conduits for error/teacher signals
• New insight: Dendritic compartment IS the computational substrate
• Subthreshold dynamics of single dendritic compartment = complete online learning algorithm

2. DendriCL Architecture

• Single-layer compartmental spiking architecture
• Apical recurrence structurally identical to leaky online Widrow-Hoff LMS
• Dynamics-only update collapses architectural depth to single layer
• No attention mechanism required
• No inference-time synaptic plasticity needed

3. Performance Breakthrough

• First SNN to pass Garg-2022 ICL benchmark at non-trivial task dimensions
• Seed-stable at super-dimensional ICL tasks
• Dense Transformers exhibit grokking-style instability at same dimension; DendriCL remains stable
• Linear probe recovers reference online-LMS trajectory from apical membrane with R² = 0.93
• Algorithm is structurally embedded in dynamics, not implicitly discovered during training

Technical Details

Architecture Components

Single SNN Layer
├── Somatic compartment (standard LIF dynamics)
└── Apical dendritic compartment
    ├── Recurrent connections
    └── Subthreshold dynamics ≡ online Widrow-Hoff LMS

Mathematical Equivalence

The apical dendritic recurrence implements:

• Online LMS (Least Mean Squares) update rule
• Leaky integration of prediction errors
• Real-time adaptation without explicit gradient computation

Biological Plausibility

• Single compartment with realistic biophysics
• No backpropagation through time
• No separate learning phase vs inference phase
• Consistent with known dendritic computation in pyramidal neurons

Implications

For Neuroscience

• Provides computational theory for dendritic function
• Explains how single neurons might perform complex learning
• Supports "dendritic democracy" hypothesis
• Bridges gap between biophysics and machine learning

For AI/ML

• Dramatically reduces architectural complexity for ICL
• Eliminates need for deep stacks or attention
• Enables ultra-efficient neuromorphic implementations
• Suggests new directions for brain-inspired AI

For Neuromorphic Computing

• Single-layer implementation = minimal hardware
• Event-driven computation = energy efficient
• Biologically plausible = potential for brain-like chips

Experimental Validation

Garg-2022 Benchmark

• Standard test for ICL capability
• Tests ability to learn new tasks from context examples
• Previous SNNs failed at non-trivial dimensions
• DendriCL succeeds where Transformers fail (super-dimensional regime)

Mechanistic Verification

• Linear probe on apical membrane potential
• Recovers reference LMS trajectory with R² = 0.93
• Confirms algorithm is embedded in dynamics, not learned implicitly

Implementation Guidelines

When to Use

• Tasks requiring in-context learning
• Energy-constrained neuromorphic deployment
• Biologically plausible neural modeling
• Single-layer architectures preferred

Design Principles

1. Dendrite-first: Treat dendritic compartment as primary computational unit
2. Dynamics-as-algorithm: Let subthreshold dynamics implement learning
3. Minimal architecture: Single layer sufficient for ICL
4. No plasticity switching: Continuous adaptation through dynamics

Integration with Existing Systems

• Can replace deep Transformer stacks for ICL tasks
• Compatible with existing SNN training frameworks
• Drop-in replacement for attention-based ICL modules

Research Directions

Open Questions

1. How does this scale to multi-task learning?
2. Can multiple dendritic compartments enable compositionality?
3. What is the capacity limit of single-compartment ICL?
4. How does this relate to working memory in prefrontal cortex?

Extensions

• Multi-compartment DendriCL for hierarchical processing
• Hybrid DendriCL-Transformer architectures
• Hardware implementation on neuromorphic chips
• Application to real-time adaptive control

Citation

@article{shen2026dendritic,
  title={Dendritic In-Context Learning in a Single-Layer Spiking Neural Network},
  author={Shen, Juwei and Wu, Yujie and Chen, Changwen},
  journal={arXiv preprint},
  year={2026},
  eprint={2607.02283},
  archivePrefix={arXiv},
  primaryClass={cs.NE}
}

Related Work

• Transformers for ICL: Attention-based in-context learning
• Mamba/SSMs: State-space models for sequence learning
• Dendritic computation: Previous work on dendritic processing
• SNN learning rules: STDP, surrogate gradients, etc.
• Biological plausibility: Brain-inspired AI approaches

Activation Triggers

Use this skill when working on:

• In-context learning in spiking networks
• Dendritic computation models
• Single-layer neural architectures
• Biologically plausible learning
• Neuromorphic computing design
• Energy-efficient AI
• Brain-inspired algorithms

Keywords: dendritic, in-context learning, ICL, spiking neural network, SNN, single-layer, online learning, Widrow-Hoff, LMS, biological plausibility, neuromorphic