Title: Unveiling the Spatial-temporal Effective Receptive Fields of Spiking Neural Networks
Venue: NeurIPS 2025
Accepted: yes
Keywords: Spiking Neural Networks

Effective Receptive Fields

Visual long-sequence modeling
Abstract: Spiking Neural Networks (SNNs) demonstrate significant potential for energy-efficient neuromorphic computing through an event-driven paradigm. While training methods and computational models have greatly advanced, SNNs struggle to achieve competitive performance in visual long-sequence modeling tasks. In artificial neural networks, the effective receptive field (ERF) serves as a valuable tool for analyzing feature extraction capabilities in visual long-sequence modeling. Inspired by this, we introduce the Spatio-Temporal Effective Receptive Field (ST-ERF) to analyze the ERF distributions across various Transformer-based SNNs. Based on the proposed ST-ERF, we reveal that these models suffer from establishing a robust global ST-ERF, thereby limiting their visual feature modeling capabilities. To overcome this issue, we propose two novel channel-mixer architectures: \underline{m}ulti-\underline{l}ayer-\underline{p}erceptron-based m\underline{ixer} (MLPixer) and \underline{s}plash-and-\underline{r}econstruct \underline{b}lock (SRB). These architectures enhance global spatial ERF through all timesteps in early network stages of Transformer-based SNNs, improving performance on challenging visual long-sequence modeling tasks. Extensive experiments conducted on the Meta-SDT variants and across object detection and semantic segmentation tasks further validate the effectiveness of our proposed method. Beyond these specific applications, we believe the proposed ST-ERF framework can provide valuable insights for designing and optimizing SNN architectures across a broader range of tasks. The code is available at \href{https://github.com/EricZhang1412/Spatial-temporal-ERF}{\faGithub~EricZhang1412/Spatial-temporal-ERF}.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces the Spatio-Temporal Effective Receptive Field (ST-ERF), a gradient-based metric for analyzing how spiking neural networks (SNNs) process spatial and temporal information. Using ST-ERF, the authors evaluate several existing Spiking Vision Transformer (S-ViT) models and identify a limitation in their global receptive field coverage. To address this, they propose two new channel-mixer designs—MLPixer and Splash-and-Reconstruct (SR) Block—which aim to enhance global spatial ERF in early stages. The proposed methods are evaluated on COCO 2017 and ADE20K, showing improvements in detection and segmentation tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper proposes ST-ERF, a well-motivated and theoretically grounded framework to understand the spatial and temporal behavior of SNNs.\n2. The diagnostic application of ST-ERF to Transformer-based SNNs is insightful and helps identify architectural bottlenecks related to global receptive field coverage.\n3. The proposed MLPixer and SR Block modules are lightweight and show performance improvements on standard vision benchmarks.\nWeaknesses:\n1.  The comparison between ViT-B (a lightweight, pure Transformer) and S-ViTs (which include heavy neuromorphic modules like SPS blocks) is not well controlled. Differences in ERF may arise from structural depth and convolutional paths rather than the spiking mechanism itself.\n2. ERF distributions can vary with activation types. No comparisons are made with ReLU/GELU or quantized ViT variants.  Modern ViT variants (e.g., Swin-T, PVT) are excluded, weakening claims about S-ViT inferiority in global modeling.\n3. The paper does not isolate the effects of MLPixer and SR Block via controlled replacements or report their individual contributions. Though the method is motivated by temporal modeling, only static image datasets (COCO, ADE20K) are used. No temporal benchmarks are evaluated.\n4. The path from ERF diagnosis to MLPixer/SR block design lacks clarity. More intuition, derivation, or visualization is needed to justify the structural choices.\n\nQUESTIONS:\nSee Weaknesses\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces the Spatio-Temporal Effective Receptive Field (ST-ERF), an extension of the traditional Effective Receptive Field concept from ANNs to SNNs that considers both spatial and temporal dynamics, and shows that ST-ERFs are Gaussian distributed in space and decay over time according to a power law. Based on ST-ERF, this paper also reveals that existing transformer-based SNNs suffer from overly localized receptive fields that hinder long-sequence modeling. The authors attribute this issue to the design of channel mixers and propose two novel modules, MLPixer and Splash-and-Reconstruct Block, to enhance early global receptive fields. Experiments confirm the effectiveness of these modules. In summary, this paper defines ST-ERF for SNNs, identifies a key limitation in transformer-based architectures, and offers effective mixer designs to address it.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1.This work introduces the concept of the Effective Receptive Field to the SNN community, filling a critical gap and uncovering the key properties of ST-ERF.\n\n2.By analyzing the spatio-temporal effective receptive field (ST-ERF) of transformer-based SNNs, this paper identifies their limitations and proposes novel modules to address them. The proposed modules are well motivated.\n\nWeaknesses:\n\n1.In the Verification of Gaussian-like distribution of ERF, the paper should also include the ERF of the more commonly used LIF model to check if it likewise follows a Gaussian distribution.\n\n2.There is no comparison with other state-of-the-art methods.\n\nQUESTIONS:\n1. In Figure 6, the receptive field of SR-SDT is mainly concentrated in the lower-left region. What causes this localization?\n\n2. More experiments should conducted to compare to SOTA  methods?\n\n3. The authors have conducted comprehensively analysis about ERF in SNNs,  how can we adapt these conclusions to practical applications?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces the Spatio-Temporal Effective Receptive Field (ST-ERF), a novel framework for analyzing learning behaviors in Spiking Neural Networks (SNNs). Extending traditional receptive field analysis to account for SNNs’ temporal dynamics, the authors establish two fundamental properties of ST-ERF: **Spatial Gaussianity**: ST-ERF exhibits Gaussian-like distributions in the spatial domain. **Temporal Power-law Attenuation**: Influence decays following a power-law pattern over time, driven by neuronal dynamics. Applying ST-ERF to analyze Transformer-based SNNs, the paper reveals that current architectures suffer from limited global receptive fields, impairing performance on visual long-sequence tasks. Crucially, the authors identify the channel-mixer design as the primary bottleneck. By proposing two novel block design: **MLPixer** and **SR block**, these designs significantly improve spatial ERF coverage in early network stages, enabling state-of-the-art performance on COCO and ADE20K benchmarks. The work bridges theoretical understanding (ST-ERF properties) and practical advancements (new architectures) for SNNs in complex vision tasks.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n1. The paper provides convincing proofs on one proposition and two properties of ST-ERF, establishing a solid theoretical basis for understanding SNN learning dynamics and representing the first formal adaptation of ERF theory to SNN spatiotemporal dynamics.\n\n2. The proposed MLPixer/SR Blocks demonstrate consistent performance gains across all metrics, with improvements up to +3.6 mIoU, providing solid validation of the theoretical insights.\n\n3. Intuitive visualizations including ERF heatmaps (Figures 2, 4, 6) and decay curves (Figure 5) effectively illustrate spatial-temporal properties and make complex concepts tangible.\n\n4. Original analysis revealing channel-mixer design as the global ERF bottleneck, with the SR Block's MLP-convolution hybrid offering a novel solution for SNN receptive field enhancement.\n\n5. Thoughtful adaptation of architectural advances (Metaformer/MLPixer) to overcome SNN limitations, demonstrating how cross-paradigm synthesis can drive meaningful progress in the field.\n\n**Weaknesses:**\n1. Some figures contain ambiguities, such as Figure 3 where the second block design appears to lack residual connections, requiring verification and clearer explanations.\n\n2. Figure 5(b) legend lacks clear explanation of which experiments (a)-(d) each element represents, hindering interpretation.\n\n3. The paper primarily focuses on visual long-sequence tasks, which may limit the generalizability of findings to other domains and applications, suggesting need for broader task validation to demonstrate framework versatility.\n\nQUESTIONS:\n1. Is there any further connections between ST-ERF and other biological theories of neural dynamics? The paper mentions that ST-ERF is a theoretical framework, but it does not discuss how it relates to existing biological theories of neural dynamics. Are there any connections between ST-ERF and other theories, such as the theory of synaptic plasticity or the theory of neural oscillations?\n2. Proposition 1 correlates spatial ERF with ANN-like behavior under the condition of \"pure instantaneous response\". However, many SNNs use time-coding (e.g., delayed coding), which defeats this premise. How is the ST-ERF compatible with the scheme of encoding information in pulse timing?\n\nLIMITATIONS:\nYes. As discussed in the appendix, the authors transparently acknowledge the technical constraints (e.g., unexplored neurodynamics influences on ST-ERF, narrow experimental validation, and its connection with scalability). The authors also provide a clear roadmap for future work: exploring ST-ERF in more complex conditions.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

comments: SUMMARY:
This paper proposes a novel framework called the Spatio-Temporal Effective Receptive Field (ST-ERF) to study the learning behavior of both spatial and temporal domains. It discovers two key patterns: spatial patterns are Gaussian-like, and their influence fades predictably over time. Using these findings, the authors analyze existing spiking transformers and then build two new efficient building blocks (MLPixer and SR blocks). Finally, they test these new blocks successfully on tasks requiring processing long sequences of visual data.

STRENGTHS AND WEAKNESSES:
Strength:
1. ​​Introduces a new way (ST-ERF) to understand both spatial and temporal learning in Spiking Neural Networks, filling an important research gap.
2. ​​Rigorously demonstrates ST-ERF's core findings (Gaussian space, power-law time decay) through comprehensive experiments on synthetic models and real-world tasks.
3. ​​Designs simple but impactful new blocks (MLPixer, SR Block) that significantly improve SNN performance on demanding long-sequence vision tasks.

Weakness:
1. Some of the operator notations in equations (especially Eq. (12), (13)) are overloaded or ambiguously defined, making it harder to parse the exact pipeline.
2. other see questions.

QUESTIONS:
1. How would ST-ERF behave under other neuron types like Izhikevich or P-LIF? Would the power-law decay pattern still hold?

2. Please provide a clearer explanation of what each subplot (a)-(d) in Figure 5(b) represents. What cause the jagged temporal ERF?

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03