dendritic-icl-snn

name	dendritic-icl-snn
description	Dendritic In-Context Learning (DendriCL) — single-layer compartmental SNN that achieves ICL via dendritic subthreshold dynamics implementing online LMS. Use when: designing SNN architectures for in-context learning, exploring biologically-plausible ICL mechanisms, studying dendritic computation, implementing seed-stable ICL beyond moderate task dimensions, analyzing compartmental neuron models.
tags	["spiking-neural-networks","in-context-learning","dendritic-computation","neuromorphic","online-lms","compartmental-snn","biological-plausibility","icl-benchmark","seed-stability","single-layer-snn","garg-2022-benchmark"]
published	"2026-07-02T00:00:00.000Z"
arxiv_id	2607.02283
authors	["Juwei Shen","Yujie Wu","Changwen Chen"]

Dendritic In-Context Learning (DendriCL)

arXiv: 2607.02283 (2026-07-02) Authors: Juwei Shen, Yujie Wu, Changwen Chen

Overview

In-context learning (ICL) has been demonstrated in Transformers, Mamba, state-space models, and MLPs through implicit gradient descent embedded in the forward pass. However, existing Spiking Neural Networks fail the Garg-2022 ICL benchmark at non-trivial task dimensions.

Key Insight: Prior SNN designs route adaptation through inference-time synaptic plasticity, treating the dendritic compartment as a passive conduit for error/teacher signals. The authors challenge this assumption — the subthreshold dynamics of a single dendritic compartment already implement a complete online learning algorithm.

DendriCL is a single-layer compartmental spiking architecture whose apical recurrence is structurally identical to leaky online Widrow-Hoff LMS. This dynamics-only update collapses the architectural depth required for general-purpose ICL to a single layer.

Core Technical Contributions

1. Dendritic Compartment as Computational Substrate

The apical membrane potential of the dendritic compartment evolves as:

$$v[t] = (1 - \lambda) \cdot v[t-1] + \lambda \cdot (w^T x[t]) - \eta \cdot e[t]$$

where:

• $\lambda$ is the leaky factor controlling temporal integration
• $w$ are synaptic weights
• $x[t]$ are input spikes
• $\eta$ is the learning rate
• $e[t]$ is the prediction error signal

This directly implements leaky online Widrow-Hoff (LMS) learning without backpropagation or inference-time plasticity rules.

2. Architectural Design

• Single-layer compartmental neurons with separate basal and apical dendritic compartments
• Apical recurrence creates the feedback loop for online LMS
• Basal compartment receives input spikes and produces output
• Apical compartment implements the learning dynamics
• No depth, no attention, no inference-time plasticity required

3. Seed-Stable ICL at Super-Dimensional Dimensions

Key empirical finding: DendriCL is uniquely seed-stable at super-dimensional Garg-2022 ICL, where dense Transformers exhibit grokking-style instability and fail past moderate task dimension.

A linear probe recovers the reference online-LMS trajectory directly from the apical membrane at $R^2 = 0.93$, confirming the algorithm is structurally embedded in the dynamics rather than implicitly discovered during training.

4. Key Findings

• ICL requires neither attention, depth, nor inference-time plasticity
• A single compartment with online-LMS dynamics is sufficient
• Seed stability: DendriCL maintains performance across random seeds at task dimensions where Transformers fail
• The online-LMS trajectory is directly recoverable from apical membrane potential ($R^2 = 0.93$)

Implementation Guidelines

Compartmental Neuron Model

Each neuron has two compartments:

Basal compartment (input integration):
  - Receives input spikes x[t]
  - Integrates with synaptic weights w
  - Produces output y[t]

Apical compartment (learning dynamics):
  - Receives prediction error e[t]
  - Updates internal state via leaky LMS
  - Provides feedback to basal compartment

Online LMS Implementation

# Pseudocode for leaky online LMS dynamics
v_apical[t] = (1 - leak) * v_apical[t-1] + leak * (w.T @ x[t])
prediction = sigmoid(v_apical[t])
error = target[t] - prediction
v_apical[t] += learning_rate * error  # LMS update

Garg-2022 ICL Benchmark

DendriCL should be evaluated on:

• Garg-2022 ICL benchmark with varying task dimensions
• Seed stability analysis across multiple random initializations
• Linear probe analysis to verify LMS trajectory recovery

Comparison Baselines

Compare against:

• Dense Transformers (grokking-style failure at high dimensions)
• Prior SNN-ICL approaches (fail at non-trivial task dimensions)
• Reference online-LMS trajectory

Design Patterns

Pattern 1: Single-Layer ICL via Compartmental Dynamics

Replace multi-layer SNN architectures with single-layer compartmental neurons for ICL tasks. The apical dendritic compartment naturally implements the learning algorithm.

Pattern 2: Seed-Stable Architecture Design

When seed instability is observed at high task dimensions, consider whether the learning dynamics are structurally embedded (like DendriCL) or implicitly discovered (like Transformers).

Pattern 3: Linear Probe Verification

Use linear probes on internal states to verify that target algorithms (e.g., LMS) are structurally embedded in the dynamics.

Activation Keywords

dendritic, dendrite, in-context learning, ICL, compartmental, online-LMS, Widrow-Hoff, seed-stable, Garg-2022, single-layer, spiking, SNN, apical, basal, subthreshold, implicit gradient, biologically plausible, grokking, stability

Pitfalls

1. Prior SNN-ICL failures: Existing SNNs fail Garg-2022 at non-trivial dimensions. Don't assume SNNs can do ICL without compartmental dynamics.

2. Synaptic plasticity ≠ ICL: Prior approaches using inference-time synaptic plasticity fail. The learning must be embedded in compartmental dynamics.

3. Seed stability matters: Transformers exhibit grokking-style instability at high dimensions. DendriCL's seed stability is a key advantage.

4. Apical vs basal compartments: Must maintain separate compartments. Treating dendrites as passive conduits loses the ICL capability.

5. Garg-2022 benchmark: Standard ICL benchmarks may not capture seed stability. Use Garg-2022 for rigorous evaluation.

Related Skills

• spiking-neural-network-analysis
• spikingjelly-framework
• adaptive-spiking-neuron-asn
• meta-learning-in-context-brain-decoding
• dendrocentric-snn-event-classification
• working-memory-heterogeneous-delays