Neuron-Guided Fine-Tuning for Adaptive Neural Models

• Neuron-Guided Fine-Tuning is a strategy for selectively adapting critical neurons in neural networks to improve performance and safety.
• It employs graph theory, attribution analysis, and sensitivity metrics to identify and update influential neurons across varied applications.
• This approach reduces computational overhead and mitigates catastrophic forgetting while enhancing generalization and model alignment.

Neuron-Guided Fine-Tuning is an umbrella term for fine-tuning approaches in neural networks that strategically select, modify, or modulate individual neurons or neuron groups based on metrics of importance, sensitivity, redundancy, or attribution. In contrast to conventional full-parameter fine-tuning or coarse module adaptation, neuron-guided methodologies employ principled criteria—often drawn from graph theory, attribution analysis, or mechanistic interpretability—to target those neurons most influential for the downstream task, model robustness, or alignment objectives. This targeted intervention aims to optimize generalization, efficiency, locality, and safety of the adapted model, and is now pervasive in computer vision, natural language processing, and code intelligence.

1. Rationale and Theoretical Underpinnings

Neuron-guided fine-tuning is motivated by several observations and analogies:

• Network Influence and Redundancy: In analogy to social networks, neural networks contain a subset of 'central' neurons whose direct and indirect connectivity shapes global model behavior (Chowdhury et al., 14 Dec 2025). Mechanistic analyses define node-level intrinsic dimension as the minimum set of non-redundant nodes required to solve a task, formalized by

$$\mathrm{ID}_{\mathrm{nodes}}(G;T)=|\{n\in V:c(n)\ge T_r\}|,$$

where $c(n)$ quantifies node $n$'s contribution via intervention metrics (Li et al., 10 Feb 2025).

• Sparsity, Plasticity, and Stability: Selectively tuning only important or sensitive neurons controls the balance between plasticity (rapid adaptation) and stability (preservation of general representations), often outperforming full fine-tuning in low-data or high-noise regimes (Xu et al., 2024, Yin et al., 23 Dec 2025).
• Efficiency and Locality: Updating a minimal subset of neurons yields state-of-the-art results with a fraction of the trainable parameters, reducing compute, memory, and catastrophic forgetting (Zhang et al., 21 Oct 2025, Pan et al., 13 Aug 2025).
• Safety and Robustness: Targeting safety-critical neurons for realignment or adversarial robustness enables safety utility trade-offs and attack resistance, without degrading general performance (Yi et al., 2024, Pan et al., 13 Aug 2025).

2. Neuron Importance Criteria and Selection Algorithms

Various metrics quantify neuron importance for fine-tuning:

• Graph Centrality: Neurons represented as nodes in a similarity graph (with adjacency $A_{ij}$ thresholded from cosine similarities) are scored via the principal eigenvector $c$:

$Ac = \lambda_{\text{max}} c,$

with neurons of highest centrality scores retained for adaptation (Chowdhury et al., 14 Dec 2025).

• Cosine Similarity and Velocity Metrics: Dynamic selection based on epoch-to-epoch activation velocity,

$$v_i^t = \Delta \phi_i^t - \mu_{\text{eq}} v_i^{t-1},$$

ranks neurons by their ongoing "change" and updates the most dynamically relevant ones under budget constraints (Quélennec et al., 2023).

• Attribution Scores: Integrated Gradients (IG), gradient-feature products, and activation × gradient scores attribute each neuron’s output to the model’s confident predictions or safety outputs (Ali et al., 12 Jul 2025, Jin et al., 13 Jun 2025, Pan et al., 13 Aug 2025). In LLMs, contribution scores

$$c_{i,l,t} = h^l_{i,-1}\cdot (W_u W^l_{\text{out}})_{t,i}$$

pinpoint factual memories for editing (Pan et al., 3 Mar 2025).

• Saliency and Redundancy: Metrics like mutual independence cross-correlation, magnitude-based score $|W_{i,j}|$, or regression-based sensitivity $|T^l_n|+\lambda|u^l|$ guide selection or pruning in speech and vision models (Mitra et al., 2020, Zhang et al., 21 Oct 2025, Jin et al., 13 Jun 2025).
• Task-Specific Causal Effects: Circuit-tuning algorithms define contribution as the expected difference in output under node ablation or edge intervention and iteratively build minimal subgraphs for task adaptation (Li et al., 10 Feb 2025).

3. Pruning, Freezing, and Fine-Tuning Protocols

Neuron-guided protocols involve:

• Structured Pruning: After ranking neurons, those below a centrality or attribution threshold are removed (weights/biases zeroed), producing a compact network for subsequent fine-tuning (Chowdhury et al., 14 Dec 2025, Ali et al., 12 Jul 2025, Jin et al., 13 Jun 2025).
• Selective Adaptation: Only parameters in neurons identified as important are updated, with others frozen via gradient masking or penalty terms:

$$\min_{\Delta \theta_N} L(\theta_{\neg N}, \theta_N+\Delta\theta_N),\quad \Delta \theta_i=0\ \forall i\notin N,$$

optimizing over the sensitive subset $N$ (Xu et al., 2024, Yin et al., 23 Dec 2025).

• Bypass Connection Methods: NeuroAda introduces bypass connections for top-$k$ ranked weights per neuron, freezing the base matrix and updating only auxiliary parameters per selected indices. The final model merges these for inference (Zhang et al., 21 Oct 2025).
• Feature and Synaptic Scaling: SAN propagates trainable feature scaling vectors ($\gamma^l$) to downstream weight matrices, emulating LTP/LTD and heterosynaptic plasticity, with optional low-rank re-calibration for flexibility (Dai et al., 2024).
• Dynamic and Meta-Learning Modulation: NeuronTune meta-learns activation scalars for safety and utility neurons, tuning amplification/suppression jointly under adversarial/benign scenarios (Pan et al., 13 Aug 2025).
• Knowledge Editing: FiNE localizes factual knowledge to a small neuron set (via contribution metrics) and solves a constrained optimization for fact revision, with penalties to preserve fluency and locality (Pan et al., 3 Mar 2025).

4. Empirical Performance and Benchmarks

Neuron-guided fine-tuning consistently improves accuracy, robustness, and efficiency across domains.

Model / Domain	Approach	Trainable Ratio (%)	Target Metric	Baseline	Neuron-Guided	SOTA PEFT	Reference
VGG16 / Flowers102	Eigenvector Pruning	30–90	Top-1 Accuracy (%)	30.88	43.14–48.26	28.67	(Chowdhury et al., 14 Dec 2025)
Llama2-7B-chat	Neuron-level FT (NeFT)	3–12	Translation BLEU	22.22	28.70	27.15	(Xu et al., 2024)
Llama-3.1-8B	Code Neuron-Guided FT	0.10–0.48	pass@3 (Python, %)	36,38	46,36	42,34	(Yin et al., 23 Dec 2025)
ViT-B/16	SAN	0.34	FGVC mean (%)	88.54	91.62	84.66	(Dai et al., 2024)
Llama-7B	NeuroAda	≤0.02	Commonsense Avg (%)	74.7	82.7	78.7	(Zhang et al., 21 Oct 2025)
Llama-3 8B	Circuit-Tuning	7–9	SQuAD2.0, EM/F1 (%)	74/87	75/88	72/86	(Li et al., 10 Feb 2025)
Llama-2 etc	NLSR Safety Patching	0 (patch-only)	Harmfulness (%)	56.6	20.4	52.1	(Yi et al., 2024)
Llama2-7B-chat	NeuronTune	variable	SU-F1 (AdvBench)	0.623	0.770	0.748	(Pan et al., 13 Aug 2025)

In vision (VGG/ResNet/ViT), eigenvector centrality pruning yields up to +17 pp accuracy gains over baseline fine-tuning (Chowdhury et al., 14 Dec 2025). In LLMs, neuron-level fine-tuning and pruning outperform LoRA and adapters both on low- and high-resource tasks, with 3–10× fewer parameters updated (Yin et al., 23 Dec 2025, Xu et al., 2024, Zhang et al., 21 Oct 2025). Safety-patching and meta-modulation markedly reduce harmful outputs (–38–60% relative), with negligible task accuracy cost (Yi et al., 2024, Pan et al., 13 Aug 2025).

5. Neuron-Guided Tuning in Safety, Robustness, and Locality

Multiple works show targeted neuron adaptation is uniquely effective for:

• Safety Realignment: NLSR identifies safety-critical neurons by measuring similarity drift post-fine-tuning, patching only those regions via transplantation from a pre-amplified reference model. This approach eliminates harmful behavior with zero gradient steps and preserves general accuracy (Yi et al., 2024).
• Balanced Safety-Utility: NeuronTune quantifies and meta-modulates safety versus utility contributions neuron-wise, deploying scalar amplification/suppression, achieving tunable trade-offs for refusal rates, entropy, and F1-style aggregate metrics (Pan et al., 13 Aug 2025).
• Dataset-Specific Mechanism Pruning: Selective IG-based pruning disables neurons driving spurious correlations, forcing the model to rely on robust, generalizable pathways and boosting multi-task accuracy (Ali et al., 12 Jul 2025).
• Robustness to Noise: Attribution-guided partitioning and regression-based pruning remove neurons sensitive to corrupted data, followed by fine-tuning on clean samples. Substantive accuracy gains and reduced compute follow in noisy environments (Jin et al., 13 Jun 2025).
• Knowledge Locality: FiNE updates only the neurons contributing most to specific factual memories, dramatically improving edit locality and minimizing unwanted side-effects compared to global locate-then-edit methods (Pan et al., 3 Mar 2025).

6. Practical Implementation and Limitations

Technical and practical considerations in neuron-guided fine-tuning include:

• Selection Initialization: Full or partial forward/backward passes compute importance metrics (centrality, attribution, velocity, saliency). Some methods require running over validation sets to avoid leakage (Quélennec et al., 2023).
• Parameter-Freezing: Freezing mechanisms leverage optimizer masks, $L_2$ penalties, or architectural modifications (e.g., bypass connections); mainstream frameworks (PyTorch, TensorFlow) now support granular freezing.
• Granularity and Budget: Most approaches allow controlling the fraction of neurons updated (from ~0.01% to ~30% depending on scenario), with sharp diminishing returns beyond moderate budgets (Zhang et al., 21 Oct 2025, Yin et al., 23 Dec 2025).
• Meta-Learning Overhead: Modulation and safety-balancing (NeuronTune) add runtime cost proportional to inner/outer optimization steps; attribution over very large models is computationally intensive (Pan et al., 13 Aug 2025).
• Polysemy / Function Overlap: Some neurons serve multi-task roles, limiting perfect modularity of adaptation (Yin et al., 23 Dec 2025).
• Model and Layer Coverage: Techniques extend well to feed-forward/attention modules; extension to convolutional or other parameter groups is ongoing (Jin et al., 13 Jun 2025).

7. Outlook and Future Directions

Promising future directions include:

• Automated Subcircuit Discovery: Circuit-tuning inspires tools for automatic partitioning into minimal, task-relevant subgraphs (Li et al., 10 Feb 2025).
• Continual and Multi-Edit Knowledge Editing: Neuronal memory banks, meta-edit networks, and group-wise updates enable scalable factual revision (Pan et al., 3 Mar 2025).
• Cross-Modal Generalization: SAN principles apply to transformer blocks in language, vision, and multimodal architectures (Dai et al., 2024).
• Online/Adaptive Noise Unlearning: Efficient streaming adaptation via incremental attribution updates and online pruning is under investigation (Jin et al., 13 Jun 2025).
• Dynamic Inference-Gating: Runtime neuron selection or gating based on input is a plausible next step for achieving micro-intervention and adaptable control (Yin et al., 23 Dec 2025, Pan et al., 13 Aug 2025).

Neuron-guided fine-tuning now spans a spectrum from static, graph-theoretic pruning to dynamic, attribution/meta-learned modulation and circuit-level mechanistic adaptation. This diverse family of approaches provides efficient, robust, and interpretable alternatives to global model adaptation and is likely to remain central for future development of adaptive neural architectures.