neuron-level analysis

**Neuron-level analysis** is the **interpretability approach that studies activation behavior and causal influence of individual neurons in transformer layers** - it aims to identify fine-grained units associated with specific concepts or computations. **What Is Neuron-level analysis?** - **Definition**: Measures when and how each neuron activates across prompts and tasks. - **Functional Probing**: Links neuron activity to linguistic, factual, or control-related features. - **Intervention**: Uses ablation or activation replacement to test neuron-level causal impact. - **Limit**: Single-neuron views can miss distributed feature coding across populations. **Why Neuron-level analysis Matters** - **Granular Insight**: Provides fine-resolution visibility into internal representation structure. - **Failure Diagnosis**: Can reveal sparse units associated with harmful or unstable behavior. - **Editing Potential**: Supports targeted neuron-level interventions in some workflows. - **Research Value**: Helps evaluate distributed versus localized representation hypotheses. - **Method Boundaries**: Highlights need to combine neuron and feature-level analysis approaches. **How It Is Used in Practice** - **Activation Dataset**: Collect broad prompt coverage before assigning neuron functional labels. - **Causal Test**: Pair descriptive activation maps with intervention-based impact checks. - **Population View**: Analyze neuron clusters to capture distributed computation effects. Neuron-level analysis is **a fine-grained interpretability method for transformer internal units** - neuron-level analysis is most informative when integrated with circuit and feature-level causal evidence.