SPACE: SPike-Aware Consistency Enhancement for Test-Time Adaptation in Spiking Neural Networks
Authors: Xinyu Luo
Kecheng Chen
Pao-Sheng Vincent Sun
Chris XING TIAN
Arindam Basu
Haoliang Li
Venue: NeurIPS 2025 poster ()
Keywords: Test-time Adaptation
Spiking Neural Networks
Primary area: applications
TL;DR: The first TTA algorithm designed specifically for SNNs.

Abstract: Spiking Neural Networks (SNNs), as a biologically plausible alternative to Artificial Neural Networks (ANNs), have demonstrated advantages in terms of energy efficiency, temporal processing, and biological plausibility. However, SNNs are highly sensitive to distribution shifts, which can significantly degrade their performance in real-world scenarios. Traditional test-time adaptation (TTA) methods designed for ANNs often fail to address the unique computational dynamics of SNNs, such as sparsity and temporal spiking behavior. To address these challenges, we propose SPike-Aware Consistency Enhancement (SPACE), the first source-free and single-instance TTA method specifically designed for SNNs. SPACE leverages the inherent spike dynamics of SNNs to maximize the consistency of spike-behavior-based local feature maps across augmented versions of a single test sample, enabling robust adaptation without requiring source data. We evaluate SPACE on multiple datasets. Furthermore, SPACE exhibits robust generalization across diverse network architectures, consistently enhancing the performance of SNNs on CNNs, Transformer, and ConvLSTM architectures. Experimental results show that SPACE outperforms state-of-the-art ANN methods while maintaining lower computational cost, highlighting its effectiveness and robustness for SNNs in real-world settings. The code will be available at https://github.com/ethanxyluo/SPACE.

SPACE: SPike-Aware Consistency Enhancement for Test-Time Adaptation in Spiking Neural Networks
Xinyu Luo
Kecheng Chen Pao-Sheng Vincent Sun Arindam Basu Haoliang Li"
Chris Xing Tian
Department of Electrical Engineering, City University of Hong Kong
fxinyuluo8-c, kechechen3-c, vincesun-c, Langtian4-chmm feffmy.ciyy.... edu.y.uedu.... edu. hk {arinbasu, haoliali)@cityu edu.
Abstract
Spiking Neural Networks (SNNs), as biologically plausible alternative to Artificial Neural Networks (ANNs), have demonstrated advantages in terms of energy effi- ciency, temporal processing, and biological plausibility. However, SNNs are highly sensitive to distribution shifts, which can significantly degrade their performance in real-world scenarios. Traditional test-time adaptation (TTA) methods designed for ANNs often fail to address the unique computational dynamics of SNNs, such sparsity and temporal spiking behavior. To address these challenges, we propose SPike-Aware Consistency Enhancement (SPACE), the first source-fire and single- instance TTA method specifically designed for SNNs. SPACE leverages the inher- ent spike dynamics of SNNs to maximize the consistency of spike-behavior behavior- based local feature maps across augmented versions of single test sample, enabling robust adaptation without requiring source data. We evaluate SPACE on multiple datasets. Furthermore, SPACE exhibits robust generalization across diverse net- work architectures, consistently enhancing the performance of SNNs on CNNs, Transformer, and ConvLSTMM architectures. Experimental results show that SPACE outperforms state-of-the-art ANN methods while maintaining lower computational cost, highlighting its effectiveness and robustness for SNNs real-world settings. The code will be available at http:///ggtbbbbooom/cccaa////s/p
Introduction
Recent advancements in ceuroscience-inspree computing have placed Spiking Neural Networks (SNNs) at the forefront biologically plausible alternative to traditional Artificial Neural Networks (ANNs). While ANNs have achieved remarkable success across domains, their reliance on dense, black-box architectures often limits interpretability and energy efficiency. In contrast, SNNs emulate the sparse, event-driven dynamics of biological neurons, offering advantages in computational efficiency, temporal processing, and explainability SNNs appealing temporal coding and sparse spiking, also introduce fragility in dynamic real-world environments. Because SNNs encode information over time, they can be highly sensitive shifts in the input distribution 2], which may induce substantial changes in layer-wise spiking activity and degrade downstream performance. At the same time, the historical SNN-ANN accuracy gap on standard vision benchmarks has narrowed considerably [58 52 foregrounding generalization and robustness as primary objectives for practical SNN deployment 10 51). application domains central to SNNs, event-driven sensing and low-power edge inference, the ability to remain robust to distribution shift thus deployment prerequisite rather than a post-hoc enhancement.
K
However, the very features that make
*Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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In real-world deployments, it is common for the training (ak.a. source) and test (a..a. target) distributions to differ owing to changes in lighting. background. sensor noise, or environmental conditions. Such domain shifts are known to impair model performance [18 30 Prior studies
have specifically noted robustness challenges of SNNs under distribution shift 38
27
23 but
remain hard to deploy online at scale. Mei et al. [38 perform gradual, unsupervised adaptation via teacher-student self self-training across intermediate domains, which requires iterative weight updates, pseudo-label calibration, and multiple passes during deployment with assumptions that clash with single-sample, low-latency edge constraints. Karilanova et al. [27] address only temporal-resolution shifts via parameter remapping that presumes a known source target sampling ratio and synchronized binning, offering no per-instance online adaptation and limited coverage of broader appearance/sensor shifts. To address the limitations, we adopt SNN- ANN-specific test-time adaptation (TTA) to meet practical deployment constraints.
Existing TTA approaches often rely on source data or batched target samples, e.g., using source- related proxy tasks 488. source statistics [00, or optimization over a batch of test inputs (49 54 34
Such assumptions are frequently impractical due to privacy, regulatory, and on-device resource constraints. We therefore focus on the source-free, single-instance regime, where the model adapts online using only the current test sample and without access to source data or target batches. Note that several influential ANN-oriented TTA methods fall outside this regime. For example, SHOT [37] requires mini-batches of target data and multi-pass pass self-training in static feature space while freezing a standalone classifier head; in SNNs. classification is tightly coupled to time-evolving spiking dynamics, leaving no stationary head to freeze and rendering distance-based pseudo-labeling insensitive to temporal structure. Similarly, TAST (25) relies on class-prior regularization and batch- level target statisties and is typically optimized over target mini-batches, assuming misaligned with sparse, discrete spike trains and incompatible with the single-instance constraint.
Within the truly source-free, single-instance family, MEMO (56) and SITA 28 are representative but face fundamental limitations for SNNs. MEMO enforces oupput-consistency by minimizing the entropy of the averaged prediction over augmented views; however, output probabilities capture only coarse semantics and provide weak control over the fine-graine temporal spiking dynamics that govern internal representations. SITA adapts batch-normalization (BN) statistics using test-time augmentations, yet many SNN architectures omit BN layers [55] 32]: even when present, sparse activations limit the effect of BN-moment shifts on spike generation. These gaps motivate an SNN- specific TTA framework that explicitly engages temporal spiking dynamics, avoids reliance on BN, and respects the source-free, single-instance constraints to improve robustness under domain shift.
In this work, we propose a novel method named SPike-Award ware Consistency Enhancement (SPACE), which is the first source-free and single-instance TTA approach specifically designed for SNNs. SPACE performs adaptation using only a single test point without access to source data, making particularly suitable for real-world scenarios where source data unavailable seivacy-sensitive. By leveraging the inherent spike dynamics of SNNs. SPACE maximizes the consistency in spike- mehavior-based local feature maps across augmented samples of the same input, ensuring robust and efficient adaptation in deep SNNs. Unlike existing TTA methods, which often overlook the unique characteristics of SNNs and the demanding of single instance scenarios, SPACE directly exploits spike-based representations for adaptation. Our contributions can be summarized as follows
1. To the best of our knowledge, we are the first TTA method tailored for SNNs, which operates using only a single test sample. This addresses the unique challenges of adapting SNNs in scenarios where source data are unavailable, while maintaining efficiency and accuracy.
2. Our method introduces a consistency-driver optimization framework to enhance the similar- ity of meike-hehavior-baaee local feature maps across augmented samples of the single point. By leveraging spike dynamics, this approach ensures robust adaptation in deep SNNs.
3. To assess the effectiveness and robustness of our proposed method, we perform extensive experiments across multiple benchmarks, including CIFAR-10, CIFAR-100 [31] Tiny- ImageNet, ImageNet [7] and the neuromorphic dataset DVS Gesture [.. Furthermore, we validate the adaptability of our method across different model architectures, specifically testing on SNN-VGG [47 29), SNN-ResNet [17 32), Spike-driven Transformer V3 (52),
experines
and SNN-ConvLSTTM [66 The results demonstrate that our approach not only achieves consistent performance improvements across datasets but also generalizes well to different network structures, showcasing its broad applicability and robustness.
2

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2 Related Work
2.1 SNN-based Deep Learning
Over the years, significant progress has been made in SNN-based deep learning. Many approaches primarily relied on converting pre-trained ANN models into SNNs, enabling SNNs to inherit the representational power of ANNs while benefiting from spike-based computations ( 3 22 26]. However, such conversion methods often suffer from performance degradation due to differences in activation dynamics. To overcome this, direct training of SNNs using surrogate gradient methods has gained traction, enabling end-to-end optimization of spiking models [55] 39 12 8 [3). Rathi et al. 41 proposed a hybrid method that combines conversion and surrogate gradient- based method, achieving tftte--f-the-art performance. These advancements have not only solved the challenge owing to the non-differentiable nature of spike functions and the the inherent complexity of temporal dynamics, but also led to the development of sophisticated SNN architectures, such as spiking convolutional networks [50] 32], spiking recurrent networks [53 57). and spiking transformer networks [58 [2]. which have denoted promising results in diverse tasks like image classification, speech recognition, and event-driven sensor data analysis.
2.2 Test-time Adaptation
Test-time adaptation (TTA) is a rapidly growing area in machine learning that tackles the challenge of adapting pre-trained models to unseen shifted distributions during inference [37 48 49 24).
Test-time training (TTT) 48 performs online updates to model parameters using supervised proxy task the test data. However, the dependence on source data makes these methods impractical in scenarios where the source domain is unavailable during deployment due to privacy or storage constraints. To overcome source dependency, nource-free TTA methods have been proposed, which perform adaptation using only test-time inputs. TENT (49 utilizes a straightforward yet effective entropy minimization approach optimize batch normalization parameters during test time, without relying on any proxy task during training. Lee et al. 333 employs pseudo-labeling to adjust model predictions.
Although these methods avoid source data, many assume batch-level adaptation, relying on test- time statistics over multiple samples, which is impractical requirement when only single test point is available. MEMO [56] addresses the single-instance setting by minimizing entropy and
enforcing prediction consistency across augmentations, thereby promoting nugmentation-invarian semantics but offering limited control over temporal spiking dynamics under shift. SITA (28) adapts naaciioomalization statistics using augmented views of the same instance; however, many SNNs lack BN (55] 32), and sparse activations diminish the effectiveness of moment updates, especially under severe corruptions. To overcome these limitations, we align spike patterns and temporal features across augmentations without relying on BN, yielding an SNN-specific TTA mechanism compatible with diverse architectures and truly single-sample operation, while offering a more stable and informative alignment signal for robustness.
2.3 Generalization of SNNs
Recent state-of-the-art SNNs 58 (2) achieve performance on clean data comparable to strong ANNs, shifting the research focus from in-distribution accuracy to generalization and robustness under real-world shifts. Several works have begun to examine these issues in SNNs I0 15 51I Guo et al. [1] study Source-Free Domain Adaptation (SFDA)
batch/epoch setting, where the model is updated after processing target-domain batches and typically assumes access to pre-collected target dataset. In contrast, our work tackles TTA in a stricter, online regime: we adapt to each incoming sample individually and on the fly, without labels, source data, target-set pre-collection. This per-sample update schedule reduces latency and memory footprint, making the approach directly applicable to streaming, autonomous deployments where SNNs are expected to excel.
3

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Proposed Method
3.1 Preliminary
SSNNs emulate biological neurons by transmitting and encoding information through discrete spikes. Among various neuron models, the Leaky Integrate-and- Fire (LIF) neuron [6] is widely adopted for its balance between biological realism and computational efficiency. Its membrane potential dynamics follow
Tm U(1) -U( + RI(I),
where (t)) represents the membrane potential of the neuron at time t, Tm is the membrane time constant, R denotes the imput resistance and I(t) is the input current received from pre-synaptic neurons or inputs. When (t) exceeds a predefined threshold Utn. the neuron emits a spike and its membrane potential is reset resting value, typically 0 or (t) Un- Following [12] Equation 1 discretized as
u =
yub 1-1 +
Tm Σ wiyoff.
Tm
Here, j is the index of pre-synaptic neurons, o; is the binary spike activation, and Wi stands for connections between pre- and post-neurons. |Figure shsoows the LIF model, depicting membrane potential evolution and spike generation in response to input spikes [.].
The spiking mechanism in SNNs introduces non- differentiability, making difficult to apply stan- dard gradient-based optimization methods for training or test-time udaptation. To address this, surrogate gradient methods approximate the non- differentiable spike function with a smooth sur- rogate during the backward pass. A simple ap- proach is the shifted Heaviside step function, where the gradient of oj, the spiking activation function of neuron j. with respect to U is de- fined as
Iaper Spikes
Ouipat Spikes
20
60
00
302
Time sou
Figure 1: Illustration spike and membrane poten- tial dynamics in LIF neurons. When the membrane potential reaches the threshold, it is reset by sub- tracting the threshold value, triggering the neuron to fire spike.
0oj OU
1,
if Un, i Un-
(3)
00
While this not an exact analytical solution, is nevertheless valid because a reset promptly occurs after spike is generated when U 2 Uzh
These SNN foundational techniques underpin method enhance TTA by ensuring consistent spiking behavior across augmented samples.
3.2 SPike-Aware Consistency Enhancement
Algorithm Design Overview The proposed algorithm through spike-aware consistency enhance- ment (SPACE) for TTA is designed to improve test-time robustness of SNNs, as shown in (Figure 2] We first generate augmented batch from a single test sample using various augmentation tech- niques, introducing diversity while retaining the core characteristics of the original sample. This augmented batch is passed through the model obtain local feature maps, represented by spike counts over time. Next, the model is adapted by maximizing the similarity across the feature maps of the augmented samples, promoting consistency in feature representations. Finally, the adapted model predicts the label of the original test sample, ensuring robust performance under test-time conditions. This approach enhances the model's ability generalize unseen test samples, particularly in shifted domains. The overall method is presented in Algorithm I, and the details are introduced below.
Spike-Aware Feature Maps SPACE aims to adapt pre-trained SNN-based models Mg (with parameters 0 E +) during inference to improve performance under distribution shifts, without requiring ground truth labels or access to source data. No specific training process required, nor do we impose particular constraints on the model. The only assumptions that 0 can be adjusted

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Figure 2: Overview of the SPike- Aware ware Consistency Enhancement (SPACE) framework for TTA in
SNNs. The test sample is selected from CIFAR-10-C dataset [18] with Gaussian Noise corruption level 5. The model here follows the VGG9 architecture. The process involves four main steps:
1) Generate an augmented batch from the single test sample. 2) Pass the augmented batch through the model to obtain local feature maps, represented by the spike counts over the processing time. 3) Adapt the model by maximizing the similarity across the local feature maps of the augmented samples, 4) Use the adapted model to predict the label the original test sample.
and that the model produces intermediate feature maps Fxx), which is differentiable with respect to 0 and can be utilized for further analysis and adaptation. Here, x E X' is single test point that presented to Mj. is worth noting that this is reasonable assumption, supported by a recent spiking neuromorphic hardware, Concretely, we decompose the SNN into a spike-based feature extractor and a classifier, writing M*(x)
advance [43] that suggest gradient back- propagation can be implemented
Eeg with 0 = (0E, Oc). Here, the extractor Eag denotes the main body blocks), and the classifier Cae consists of the
of the network (e.g., CNN/Transformer/Coow/ last one two readout layers.
Aligned with previous work (28 (56, we achieve test-time robustness by leveraging data augmenta- tions and a self-supervised adaptation objective. In our work, set of augmentation functions with different intensities selected from A {a...... aN is applied to single test input x, forming an augmented batch of samples dynamics extracted from augmentations, ensuring that the model produces reliable and robust predic- tions in the presence of distribution shifts. SPACE differs from MEMO (56) by operating not on the conditional output pa (v) x) but on the spike dynamics of intermediate layers that determine temporal coding and semantic representations. Specifically, we observe the feature map O(-) E xxxxxxX H W at the deepest layer of Eag that still retains spatial support. Here T denotes the number of time steps, represents the number of channels, H and W are the spatial dimensions of the local feature maps. If the last pre-classifier feature map has shape with H x W 1, we use this layer; if global pooling or stride reduces it to 1 x 1, we use the penultimate layer of Eøg. This choice balances semantic abstraction and aligmment granularity, where using 1 map yields too few alignment targets and risks overfitting, whereas too shallow a layer may overemphasize low-level variations.
(x1, X2. x.) M S N. Using this batch, we align the spike
To leverage the intermediate spike activity over the entire time window, let 0(x,) E {0. denote binary spikes (1=fire, O=silent, refers to cossuseectoon 3. l) at the chosen extractor layer for an augmented view X;. We aggregate spikes over time to form spike-aware feature maps collection F [F (x(),, F(x2), ..., FxxM)), where each F(x;) E RCxD represents the total spike counts of neurons across different channels, with D H W indicating the spatial dimensionality.
5

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Algorithm SPACE for Test-Time Adaptation in SNNs
1: Input: Test sample augmentation functions A = {aI,.... aNJ. SNN model with extractor
parameters Dg, and learning rate
2: Output: Prediction y via updated model
3: Step 1: Generate augmented batch
4: Initialize B = Ø
5: for ag in a subset {aI...., aMM of A do
6: X5 ax(x), add X₃ to B
7: end for
8: Step 2: Extract feature maps
9: Pass B through the SNN feature extractor, obtain F. (F(xI),.... F(xM))
10: Step 3: Compute similarity
11: for each pair of (F,),). F(x,)) in F do
12: Compute similarity [ii, using Equation 4
13: end for
14: Step 4: Update model parameters
15: Compute loss L(O; x) using Equation 5 update 0E via SGD with learning rate
16: Step 5: Predict
17: y f- arg maxy Poz (y) x)
18: Return: Prediction result y²
We choose total spike counts for three main reasons: 1) SNNs often run for tens thousands of steps [45] 16 32 41, making stepwise matching redundant and costly. 2) Aggregation smooths the loss landscape: spikes jitter time but counts match, the loss remains unchanged, so gradients issue a clear directive: make this neuron more or less excitable for this input, producing stable, macro-level alignment of core feature activations across the augmented views. 3) distribution shift primarily manifests as changes in spiking rates and spatial activation support in intermediate layers; F(-) provides a direct, informative summary of these changes that output probabilities cannot capture.
Feature Maps Alignment Our goal is to make the model focus on invariant, task-relevant features rather than noise or minor transformations introduced by augmentations or distribution shift. If the feature maps of augmented views are highly similar, the model becomes less sensitive at test time and generalizes better to unseen, unlabeled samples. Concretely, given the spike-aware feature map F(x;) RCD of an augmented view we compute channel-wist local vectors F Fx(x;) E and normalize them across spatial locations with softmax to obtain a probability distribution (x.x) E A- This normalization emphasizes salient regions and suppresses noise, yielding stable target distribution. We then measure similarity between two augmented views x, and x; by the average channel-wise inner product
C
D
$ x) -C- c=1d=1
-
P
Pe.d(x P Px,)(x;),
(4)
which lies in [0, 1] and attains 1 if and only if the per-channel spatial distributions match exactly. Based on this, we adapt the extractor parameters by minimizing the loss function
((OE:
A
Σ (1- (1-S(i,j xx),
(5)
ISKISM
using a single SGD step with learning rate 7). Minimizing L enforces consistency across augmenta- tions, regularizing the model learn stable, robust representations and preventing overfitting to any specific view. Finally, predictions are obtained with the adapted model.
Theoretical Motivation Our objective instantiates consistency regularization at inference time: under semantics semantics-preserving. bounded-severin augmentations (e.g., AugMix [19),, we enforce in- variance internal representations across transformations. (and thus P) suppresses augmentation--peeiff variability while preserving reeeevat content, approximating an information bottleneck that favors invariant features; geometrically, when perturba- tions push sample off the learned manifold, adaptation pulls it toward the local manifold spanned
aligning

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Table Performance of different model architecture under domain shift. Values are Top-1 accuracy (%); Acc. Loss = Clean Shifted. "Shifted" denotes evaluation on shifted variants of each dataset; exact configuration described in Appendix C are trained with the same protocol.
SNN and ANN models share backbone and scale and
ANN
SNN
Architecture
Dataset
Clean
Shifted
Acc. Loss
Clean
Shifted
Acc. Loss
VGG9 VGGII VGG11
CIFAR-I0 CIFAR-I00 100
90.53
77.25
13.28
90.61
67.86
22.74
70.86
48.32
22.54
69.50
41.35
28.14
Tiny-ImageNet
61.24
19.76
41.48
58.36
14.56
43.80
Transformer
ImageNet
85.16
75.49
9.67
79.60
67.64
11.96
by its augmented views, effectively denoising the representation. In SNNs, shifts often manifest as event-rate changes and timing jitter that nudge membrane potentials U around the threshold Unn- making spike generation highly sensitive; by aligning time-resolved features across augmentations, we reduce the variance of U trajectories and spike counts, enlarge the effective margin IU Unh and stabilize and spike timing dynamics. Consequently, robustness arises from coupling semantic consistency with temporal stabilization within the SNN-TTA framework.
We also explored richer feature relations via kernel embeddings and Maximum Mean Discrepancy (MMD) (Appendix B). In practice, the above kmear-kerned objective already captures the essential distributional overlap with lower variance and cost, and stronger kernels did not bring noticeable gains while adding overhead. More discussion is presented in InAppendix A
Experimental Results
Datasets and Models The experiments were conducted on six benchmark datasets for out-of- distribution generalization: CIFAR-IO-C, CIFAR-100-C, Tiny-ImageNet-C [18], ImageNet- V2 42 ImageNet-R (20), and ImageNet-A (2T. The first three datasets include 15 corruption types across four categories and five severity levels, while the three ImageNet-based datasets capture real-world domain shifts, such as distributional discrepancies and semantic variations. Additionally, we evaluated on the neuromorphic dataset DVS Gesture-C [23], which introduces six corruption types. Four backbone models were used: 1) SNN-VGG with Batch-Norrmllizttion Through Time (BNTT))29), using 25 inference steps; 2) SNN-ResNet with 30 inference steps 32); 3) Spike-driven Transformer V3 (19M parameters) with inference steps [52]: and 4) ENN-ConvLSTM, with 32 inference steps. All models were pre-trained on their respective source domains to ensure fair evaluation on datasets with domain shifts.
Compared Methods We compared approach against models pre-trained on clean datasets and evaluated without any test-time adaptation (henceforth referred to as No Adapt), which serves as a lower-bound reference for robustness under distribution shift. Given the limited availability of TTA methods specifically designed for SNNs, we selected two representative traditional TTA methods, MEMO (56) and SITA (28), baselines. These methods represent the current state-of-the-arr for source-free and single-instance TTA, respectively. MEMO enforces output consistency across augmented samples, making it inherently compatible with SNNs by directly adjusting their final outputs. SITA, originally designed for models with BN layers, was adapted for SNN-VGG with BNTT by updating the BN layer parameters during TTA. Additionally, we included RoTTA (54] and the recent De [34] as baselines, despite their reliance on batch data. To ensure fair comparison, we applied the same augmentation strategy as SPACE to construct a pseudo-bater for each sample.
Implementation details To maximize the effectiveness of our proposed method, all experiments conducted on CIFAR- 10-C. CIFAR- 100-C, ImageNeCe and DVS Gesture-C were performed with the highest severity level (level=5). In line with our setting and practical scenarios, the test batch size for all experiments was fixed to 1, and the model was updated only once per test sample. Following MEMO's experimental setup, we applied Aug [19 augmentation to generate diverse augmented samples, using batch size of 32 for CIFAR-1O-C, 100-R-100-C, Tiny-ImageNet-CC and DVS Gesture-C, and 64 for the ImageNet series. Please refer to Appendix C for more details.
7

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Table 2: Performance comparison on CIFAR-101 -0-C. CIFAR-100-C and Tiny- Imy-ImageNet-C with CNN-based SNN models regarding Accuracy (%). The bold number indicates the best result.
Metbod
Guuss.
Noise Shot
Impl.
Defoc
Glass
Blar
Motion
Zoom
Snow
Fog
Weather
Frost
Bri.
Contr:
Elas.
Digial
Pit.
JPEG
Average Azc.
CIFAR-ID-C, SNN-VGG1
No Adage
72.38
14,70
58.37
63.05
63.96
64.44
71.33
76.32
43.57
75.72
82.44
22.54
75.01
70.25
84.28
66.57
RoTTA
73,60
75.49
61.61
66.08
67.53
67.15
72.40
74,34
51.54
76.35
80.95
23.01
74.23
68.60
83.29
67.79
De
73.97
62.45
14.90
50.68
75.19
70.44
82.85
73.06
58.41
62.94
70.72
76.67
82.51
22.00
MFMO
77.73
79.50
65.61
67.50
72.61
77.38
45.47
83.05
23.83
76.45
73.25
SPACE (ours)
77.98
79.34
69.41
71.59
67.76
72.14
74.67
78.43
52.80
79.59
83.22
23.85
73.49
76.24
$2.88
71.03
CIFAR-ID-C,
1
No Adage
72.85
74.49
49.59
67.77
63.53
65.14
72.06
78.11
42.84
73.12
51.71
12.72
74.88
77.08
82.47
65.96
RoTTA
73.12
75.66
19.93
67.89
63.79
61.88
72.75
78.14
16.34
73.59
80.36
12.90
74.68
78.25
83.12
66.15
MEMO
74.21
68.11
64.65
73.96
78.52
43.47
SPACE (ours)
75.90
77.81
56.85
66.02
78.74
45.47
76.79
50.32
CIFAR-I00-C,
SNN-YGG11
No Adage
42.51
45-40
25.50
42.15
42.84
41.81
46:37
44.88
16.51
44.12
49.36
5.42
51.33
52.35
58.41
40.63
RoTTA
43.02
44.94
255 48
42.24
43. 76
42.13
46. 93
44.50
16.63
43.83
49.54
5.45
50.99
52.37
58.50
40.65
DeYO
43.00
45.45
255 6-
42.02
42.88
42.47
46.61
44.97
16.79
43.82
49.
5.45
51.34
52.85
59.45
40174
SITA
43.01
44.83
25. 18
12.25
43.29
42.11
46
64.49
16.65
43.84
69.53
5.56
50.99
52.48
58.71
40.68
MEMO
43.46
42,47
43.90
42.55
16.76
44.34
52.82
59.29
SPACE (ours)
44.71
46.73
27.99
17.62
53.81
58.96
Crn--In SNN-VGGI
No Adage
12.43
15:20
8.74
1.58
6.50
14.48
13.59
15:58
5.27
18.05
15.01
1.32
20.53
31.62
31.15
14.47
RoTTA
12.67
15.34
8.89
7.55
6.56
14.57
13.94
15.75
5.21
18.06
14.82
1.38
21.53
32.21
31.25
14.65
DeYO
12.69
15.36
8.56
7.39
6.26
14.38
13.75
15.75
5.29
18.12
14.85
1.41
20.93
31.99
33.47
14.55
SITA
12.71
15.17
8.87
7,56
6.57
14.57
13.94
15.72
5.32
18:05
14.82
1.38
21.45
32.20
31.51
14.66
MEMO
13.64
SPACE (ouns)
16.71
11.72
31.89
4.1
Evaluation of Robustness of SNNs under Domain Shift.
To further substantiate the observation that SNNs are particularly vulnerable to domain shifts, we conducted controlled comparison between SNNs and ANNs that share the same backbone family and model scale, trained under identical protocols, and evaluated on shifted variants of standard benchmarks (details in Appendix C). As summarized in Table SNNs consistently incur larger accuracy drops than their ANN counterparts across CIFAR-IO, CIFAR-100 Liny-ImaeeNe,, and ImageNet. These empirical results motivate the development of robust, SNN-specific TTA strategies tailored to spiking computation.
4.2
Main Results
In this section, we compare SPACE's performance with four existing TTA methods. To demonstrate the broad applicability of the proposed method, we evaluate all methods on CIFAR-IO-C, CIFAR- 100-C, and Tiny-ImageNet-CC datasets using SNN-VGG, on CIFAR-IO-C with SNN-ResNet I, on ImageNet series with Spike-driven Transformer V3, and on DVS Gesture-C with SNN-ComVLSTM, to demonstrate the broad applicability of our proposed method.
Performance Comparison on CNN-based SNN Models The results CIFAR-10-C, CIFAR-100- C, and Tiny-ImageNet- at level 5 corruption, shown in Table 2| highlight the effectiveness of SPACE. Key observations include: 1) Optimal Performance: SPACE consistently outperforms all baselines in aver- age accuracy across all datasets under both VGG and ResNet architectures. Broad Applicabil- ity: SPACE demonstrates effectiveness across differ- ent CNN structures, showcasing its generalizability. 3) Robustness on Complex Datasets: A particu- larly notable result is SPACE's performance on Tiny- ImageNet-C, where it surpasses all methods across every corruption type. This highlights its robustness in handling complex datasets like Tiny-ImageNet-C. 4) Vs. RoTTA: RoTTA's memory bank cannot
Table 3: Performance comparison on ImageNet-V2/R/A with Spike-driven Trans- former V3 regarding Accuracy (%). The bold number indicates the best result.
Accuracy (%)
Method
V2
R
Avg.
No Adapt
67.64
41.94
15.12
41.57
RoTTA
67.34
43.12
14.87
41.78
DeYO
67.92
41.42
15.42
41.59
SITA
67.21
42.05
13,92
41.06
MEMO
67.57
42.15
14.97
41.56
SPACE (ours)
68.82
45.07
16.22
43.37
be effectively utilized this single-sample setting, leading to limited performance gains. 5) Vs.
SITA: The -NN-ResNet does not include BN layers, rendering SITA inapplicable. Furthermore.

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Table Performance comparison on DVS Gesture-C with ConvLSTM-basse SNN model regarding Accuracy (%). The bold number indicates the best result.
Accuracy (%)
Method
DropPixel
DropEvent
RefractoryPeriod
TimeJitter
SpatialJitter
UniformNoise
Avg.
No Adapt
$3.03
54.55
36.74
31.06
92.05
91.67
59.85
RoTTA
$5.38
56.54
37.50
30.68
91.62
92.05
MEMO
56.06
55.30
36.74
30.68
90.91
91.67
60.23
SPACE (ours)
57.20
57.95
52.27
43.18
92.42
92.42
65.91
Impulse Noise Demain
Tiny-ImggeNetCC
Local Fenture Based SPACE
Gioball Feature Maps Bosed SPACE
Frequency
CIFAR-100-C
10.9%
No Adapi
71,0%
CIFAR-IO-C
Similariit Value
Figure 3: a) Local feature maps based SPACE outperforms global feature maps based SPACE. b) Similarity distribution of spike count feature maps before and after SPACE adaptation.
SITA performs worse than No Adapt, indicating its ineffectiveness for SNNs. to the spike- based transmission in SNNs: directly adjusting BN parameters without gradient guidance can disrupt spike information, leading to significant performance degradation. 6) Vs. Ye: De relies on smooth probability outputs for confidence comparison, which incompatible with SNN's discrete, temporal spike-based outputs, introducing significant errors. Also, DeYO only works normalization layer. 7) Vs. MEMO: MEMO exhibits slightly better performance a few specific cases, such as JPEG corruption on CIFAR- 10-C (85.05% for MEMO vs. 82.88% for SPACE) and CIFAR- 100-C (59.29% for MEMO vs. 58.96% for SPACE). The minor edge cases hint at MEMO's advantage under certain corruption types, yet they don't 't't diminish SPACE's overall superiority.
This may be attributed
Performance Comparison on Spike-driven Transformer V3 To explore the real-world impact of SPACE, conducted tests diverse datasets, including ImageNet- V2, ImageNet-R, and ImageNet- A. Furthermore, to evaluate its adaptability in deeper SNN architectures, we tested SPACE on the state-of-the-are Spike-driven Transformer V3. As shown in Table SPACE consistently outperforms all baselines, achieving at least 1.59% improvement, which demonstrates its robustness in real-world scenarios and its effectiveness in advanced SNN models.
Performance Comparison on Conv --b-- SNN Model SNNs are well-suited to event- based neuromorphic data, thanks to their temporal dynamics and energy efficiency. The DVS- WVS-Gesture dataset [1] is an ideal benchmark to evaluate the performance of SPACE on neuromorphic datasets, highlighting its compatibility with dynamic vision tasks and data with temporal structures. To further explore potential network architectures, we incorporated ConvLSTMM BN-dependent SITA and DeYO not suitable for this architecture. As shown in Table 4] SPACE achieves significantly better results compared to existing methods, demonstrating its effectiveness and adaptability.
4.3
Ablation Studies
Local vs. Global Feature Map Similarity Definitions. Let "global feature maps" refers to treating the entire feature map of size C' x H x W, as single entity and measuring similarity on this full representation. Let the "local feature map" considers each spatial position in the x W dimension independently, which is adopted to calculate the similarity and averaging across the C' channels in the main paper.
We compared the performance of local versus global feature map similarity for our proposed method. The results in FFguue 3/ clearly illustrate the decisive advantage of using similarity of local feature maps over global feature maps. While the global approach does not account for variability in neuron activations across channels, the local method isolates the contributions of active neurons within each channel, offering a more stable and meaningful measure of feature map consistency. As shown FFiure 3R, the local method yields substantial accuracy improvement, whereas the global approach negatively impacts performance across all three datasets. This demonstrates that

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Table 5: Comparison of computational cost CIFAR-IO-C with SNN-VGG9 model.
Method
RoTTA
DeYO
SITA
MEMO
SPACE
GFLOPs
160.67
318.07
161.48
161.48
158.11
Accuracy
67.79%
67.99%
66.41%
69.20%
71.03%
Table 6: Performance comparison of SPACE with different augmentation quantities CIFAR-10- - with SNN-VGG9 model regarding Accuracy (%) and Evaluation Time (seconds) per test point.
Number of Augmentation
No Adapt
2
4
8
16
32
64
128
Accuracy
66.57
54.60
66.91
69.77
70.60
71.03
71.07
71.11
Evaluation Time
0.144
0.318
0.320
0.327
0.339
0.345
0.443
0.788
emphasizing individual channel stability leads to better generalization and adaptation. These findings underscore the distinct roles of each channel in SNNs and further highlight the practical limitations of comparing entire feature maps.
Effect of SPACE on Feature Map Similarity further investigate the impact of SPACE adaptation on the feature map similarity, we analyzed the distribution of similarity values (computed using Equation 4) across the CIFAR-1O-C dataset. As shown in Figure 3p, we observe a noticcable increase the similarity between the original samples and their augmented counterparts after applying SPACE adaptation, particularly in the Impulse Noise domain. The similarity distributions between spike counts based feature maps in these domains indicate that the feature map consistency improves following the adaptation process. Specifically, the "After SPACE Adaptation" distributions shift towards higher similarity values, reflecting the enhanced alignment of feature maps across augmented samples. This demonstrates the effectiveness of SPACE adaptation in increasing the stability of feature representations and improving generalization under various augmentation conditions.
Analyses of Computational Cost To evaluate computational efficiency, compared the GFLOPs of different baselines with the same augmented batch size, and SPACE achieved the lowest GFLOPs, demonstrating its efficiency (refers to Table 5). Regarding the additional cost of optimizing spike- behavior consistency, this step is performed only once per input in single-sample setting, without requiring whole model backpropagation or iterative updates.
Effect of Augmentation Quantity In the main paper, we follow MEMO [6 to determine the number of augmentations M. To assess M's effect, we evaluated the average accuracy and the evaluation time per test point under the same computing resource conditions on CIFAR- using SNN-VGG9 with varying augmentation quantities M = {2, 8, 16, 32, 64, 128). As shown in Table 6. at least 8 augmentations are required to achieve noticeable performance improvement. For
M = (32, 64, 128), while the performance continues improve gradually, the evaluation time increases significantly. Considering the trade-off between accuracy and evaluation time, M = 32 is the optimal choice.
More ablation studies are presented in Appendix D
Conclusion
In this work, we propose SPike-Aware Consistency Enhancement (SPACE), the first source-free and single-instance TTA method tailored for SNNs. By leveraging the unique spike-driven dynamics of SNNs, SPACE optimizes spike- bilke-behavior-ar based feature map consistency across augmented samples, directly addressing the limitations of ANN-based TTA methods that overlook the temporal and sparse nature of SNN computations. Experiments on CIFAR-10 10, CIFAR- 100, Finy-ImageNet, ImageNet, and DVS Gesture series distribution shifted datasets show that SPACE consistently improves performance under severe corruptions and generalizes well across architectures and backbones, demonstrating robust, practical resilience real-world domain shifts.
10

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Acknowledgments and Disclosure of Funding
This work supported in part by Chow Sang Sang Research Fund 9229161, CityU SGP grant 9380132 and ITF MSRP grant ATS/018/22MS Any opinions, findings, conclusions, or recommen- dations expressed in this material do not reflect the views the Govermment of the Hong Kong Special Administrative Region, the Innovation and Technology Commission, or the Innovation and Technology Fund Research Projects Assessment Panel.
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A Discussion
Selection of Alignment Layer We compared aligning the classifier's feature map with aligning extractor layers used in the main paper. Empirically, classifier-level alignment occasionally degrades performance on several corruptions, whereas extractor- alignment is consistently more stable. We attribute this to: 1) classifier features being highly task task-specific and high-curvature, hence small per-sample TTA steps can move decision boundaries and induce catastrophic forgetting [5]: 2) lower signal-to-noise at the classifier under pseudo-label or consistency noise, which amplifies domain- specific biases; and 3) greater adaptation overhead when updating deeper, larger parameter blocks. In contrast, channel-wise alignment at mid-level layers promotes augmentation-inaarie structure while keeping the classifier intact, offering a better stability-plastiiity trade-off with lower compute.
Potential Information Loss in Enquationn 4
We aggregate spikes over time, convert discrete spike counts into continuous probability values via softmax function, then calculate the similarity. This transformation is the intentional discarding of the absolute magnitude of spike counts to focus on the relative importance of neurons within channel. By applying softmax, we normalize away noisy fluctuations in absolute firing rates and create a stable spatial saliency map. Aligning these maps forces the model learn which spatial features are consistently most important, directly encouraging the learning of invariant representations. In addition, while our alignment objective operates on this spatially-focused abstraction, simultaneously preserves and refines the inherent temporal characteristics of the SNN. The underlying model remains a fully temporal processor, and the spike counts are the direct result of its complex dynamics. By demanding consistency in the outcome (the count-based saliency map), our method implicitly forces the network to stabilize the cause (the spike generation process). This creates a powerful regularization pressure that reduces temporal jitter and enhances the robustness of spike timing, all without the computational cost and overfitting risks associated with direct, fokke-for-spike temporal alignment.
Why not cosine or KL similarity We compute similarity as per-channel dot product over softmax- normalized spike counts, which is bounded, symmetric, and well-defined even when raw counts are zero. Cosine similarity requires l2 normalization and numerically fragile under SNN sparsity: many channels yield zero or near-zero vectors, causing division-by-zero or unstable gradients; adding an fixes numerics but introduces a sensitive hyperparameter and slightly degrades performance in our trials. KL divergence, KL(P| is asymmetric and becomes ill-conditioned when entries approach zero (common with peaked softmax under corruptions). leading to exploding losses unless additional smoothing is used, which adding tuning burden again. A symmetrized variant (e.g., JSD) alleviates this but increased compute without improving over the simple dot product. Given our per- per-sample TTA regime, the dot product on per-channel probability maps offers the best robustness-efficien trade-off, avoiding normalization pitfalls while retaining discriminative alignment.
Feasibility of Deployment On neuromorphic platforms like SpiNNaker2 14. the data pre- processing unit and neural network inference module are typically separate. Batch data augmentation can be performed in the pre-processing stage, outside the neuromorphic core, before feeding data into the system for inference. This design allows augmentation to be handled efficiently without impacting the neuromorphic computing core, making our method feasible on such platforms.
Limitation Proposed SPACE's reliance on backpropagation may limit its applicability in resource- constrained scenarios. Future work will explore alternative optimization strategies to expand its practicality for more neuromorphic hardware and energy-efficient AI.
B Kernel-Based Consistency Regularization
To investigate whether higher-dimensions feature relationships can enhance the consistency mea- sure, we integrate kernel embeddings into the loss function. The channel-wise feature probability distribution P can be mapped into a higher-dimensional reproducing kernel Hilbert space (RKHS) H, using a Gaussian kernel defined as
||z
z'112
k(2,, z') exp(
(6)
20
22

---

Table Performance comparison of SPACE without/with kernel embedding (k.e.) on CIFAR-IO-C with NNGV model regarding Accuracy (%). The bold number indicates the best result.
Metbod
Gauss.
Noisc Shot
Impl
Defoc.
Glass
Blur
Motion
Zoom
Snow
Fog
Westher
Frost
Bri
Contr.
Blas.
Digital
Fix.
JPEG
Average Acc.
No Adage
72.38
74.70
58.57
63.05
63.56
64.44
71.33
76.32
43.57
75.72
82.44
22.54
75.01
70.25
84.28
66.57
SPACE
77.98
79.34
69.41
71.59
67.76
72.1
74.67
78.43
52.80
79.59
83
23,85
75.49
76.24
82.88
71.03
SPACE k.e
77.85
74.34
69.25
71,42
67.61
7231
74.61
78.48
52,46
79.66
83.25
23:40
75.42
76.13
82.98
70.94
where a is the kernel bandwidth. The kernel function ki z') implicitly defines mapping Φ: 2 Hi such that the inner product in H. can be given by the kernel function:
k(z, z (Ø(z). (z
This allows us to compute relationships between feature vectors a digh-diimnssooal space without explicitly constructing o(-). To align feature distributions from augmented samples, we the Mean Embedding of Distributions, which maps Pe(x,) into a single point (x.(x;) in RKHS. Specifically, the mean embedding is defined
(P.(x:) = (xx)))((x))((x
(8)
Given samples ~ Po(x,), the embedding can be approximated as
1
(x.()))
o (Ziu))
D
To measure the discrepancy between distributions Px(x; and Po(x;) in RKHS, employ the Maximum Mean Discrepancy (MMD):
MMD" Pc(x))) (x))) - (x)))
(10)
Using the kernel trick, this can be computed without explicitly constructing o(-):
2
Σ p
Σ
Zjja
(11)
2
d=1
Then average the MMD values across channels:
C MMD = Σ (M Px(x))
(12)
Finally, we integrate the average MMD values into the original loss function C:
L* xx) & L AMMD
Σ
MMD3(1.,]x)
(13)
ISKGEM
We evaluated augmenting SPACE with a kernel embedding loss (MMD) on CIFAR-IO-C using SNN-VGG9 [Table 7). The average accuracy is virtually unchanged and occasionally slightly worse than our base alignment. We hypothesize three reasons. 1) Representation sufficiency: the spike- count feature map, coupled with our linear, channel-wise similarity, already captures the essential distributional alignment across augmentations; mapping to RKHS adds little signal. 2) Regime mismatch: our augmentations are semantics- preeaatriis-eeeevingg and bounded in severity (e.g., AugMix [19). inducing small, approximately uni-mode shifts where MMD is often helpful under large or multi- modal discrepancies and provides limited additional benefit. 3) Estimation and cost: per-sample TTA affords only small augmented batch, making MMD estimates (and their gradients) nigher-variance and sandwidth-sensitive while adding nontrivial compute. These factors combined suggest that kernel embedding is not particularly beneficial in this experimental setting.
23

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C Additional Experimental Information
C.1 Details on Datasets
In this paper, we conducted six static datasets and one neuromorphic dataset to evaluate the out-of- distribution generalization ability. They are CIFAR-10 10-C CIFAR-100-C2 Imay-ImaeeNe--
18
ImageNet-V? V 42, ImageNet-H (20), ImageNet-A 21 and DVS Gesture- 23
CIFAR-IO-C, CIFAR-I00-C, and Iiny-tmageNet-CC These three datasets are commonly used benchmarks for evaluating the robustness of models under various types of input corruptions. They are derived from the original CIFAR- 10, CIFAR 100, and Tiny-ImageNct datasets by applying 15 coruuption types (e.g., noise, blur, weather, and digital distortions) at 5 severity levels to the original 7 datasets, respectively. CIFAR-I0- and CIFAR- 100-C consist of 10 and 100 classes, each with 50,000 training images and 10,000 testing images. Tiny-mmageNet-C, derived from Iiny-ImageNet, includes 200 classes with 100,000 training images and 10,000 validation images.
CIFAR-IO, CIFAR- 100 [3. and
ImageNet-VV2 V2, ImageNet-R and ImageNet-A These three datasets are designed to evaluate model robustness under different real-world challenges. ImageNet-V V2 consists of new of images collected under similar conditions as the original ImageNet [7]. serving as a test of distribution shift. ImageNet-R focuses on image classification robustness across various artistic renditions, such as paintings, sketches, and cartoons, covering 200 ImageNet classes. ImageNet-A, on the other hand, an adversarially filtered subset of natural images that models often misclassify, targeting their inherent weaknesses. Compared to corruption based datasets, these benchmarks provide better reflection of real-world scenarios and challenges.
DVS Gesture-C DVS Gesture- C is a corrupted variant of the standard DVS Gesture [ dataset. This variant introduces six corruption types: DropPixel, DropEvent, RefractoryPeriod. TimeJitter, SpatialJitter, and UniformNoise. These corruptions, implemented via the Tonic API [35), effectively simulate real-world imperfections in event-based data, including sensor noise and timing inaccuracies.
C.2 Details on Pretrained Models
We adopt SNN-VGG with Normalzioniiioonoonn Through Time (BNTT) 29 as our main backbone model. BNTT decouples parameters along the time axis within each layer, effectively capturing spikes' temporal dynamics. The temporally evolving parameters in BNTT enable neurons to regulate spike rates across time steps, facilitating low-latency, low-energy training from scratch. The pre-trained model achieves accuracies of 90.61 %, 69.50% and 58.36% on the CIFAR-10, CIFAR- 100, and Tiny-Iman Inageetee test sets, respectively.
SNN-VGG with BNTT ?
SNN-ResNet
To explore other CNN architecture, we use SNN-ResNetll 32 which trains SNNs directly via surrogate gradient. The pre-trained model used in our experiments can achieve 90.95% accuracy on the original CIFAR-10 test sets.
10
Spike-driven Transformer V3 To evaluate the effectiveness of our work on more complex networks and datasets, utilized 19M parameter model in Spike-driven Transformer V3 (52). which achieves 79.60% accuracy on the ImageNet-IK validation set. This model has a patch size of 14 14, with an embedding dimension of 360 output by the final feature extractor. Instead of
http://zanddoor///oot///BBB
http:://eooddoo////eeodd///////
Lale--a---aaaaaYYaYaaaaaa Batchh-oormlizzaionnn- Through-Time Time

---

Table Performance comparison of SPACE with different augmentation strength on CIFAR-IO-C (Gaussian Noise) with SNN-VGG9 model regarding Accuracy (%).
Strength
3
5
7
10
Larger Boundary
Accuracy 77.98
77.88
77.48
77.41
77.02
74.52
using the class token, the entire embedding feature map is employed the input the classifier. This design naturally aligns with our method, where the channel dimension C' corresponds to the embedding dimension, and the spatial dimension D matches the patch size.
SNN-ConVLSTM DVS Gesture is neuromorphic dataset with a temporal dimension, making well-suited for evaluating RNN-based architectures. To validate the effectiveness of our work such networks, we employed a spike-driven 2ConvLSTM-IIF model, with hidden layer dimensions of 32 and 64. This network achieves an accuracy of 93.36% on the original DVS Gesture test set.
C.3 Details on Implementation
To better reflect real-world scenarios, we adapt our method to independently perform adaptation on each individual test input. In the same experiment, the learning rate remains fixed across different shifted domains, as the domain of given test sample is typically unknown in real-world settings. For each set of experiments, we employ the SGD optimizer and evaluate 7) ranging from 0.001 to 0.8, selecting the optimal value based on performance. We trained and tested all models and datasets in NVIDIA GeForce RTX 3090 GPUs.
D Additional Ablation Studies
strength on SPACE. Following MEMO, our default is s=1; we sweep s E [1, 10] while keeping all other AugMix settings fixed (mixture width/depth, operators) and respecting AugMix's semantic bounds (e.g., rotation < 30, translation < +16 px). As shown in Table 8, accuracy remains stable within these bounds, with only minor declines as s increases. When deliberately exceed the bounds (e.g.. rotation 50% translation 188 px), accuracy drops noticeably, indicating that overly aggressive transformations inject harmful noise and destabilize per-sample alignment under the augmented batches. In this regime, the alignment objective tends to match iigmentationinduuee invariant, class-relevant structure in the feature maps, thereby undermining TTA and diminishing its gains. These results consistent with AugMix's 's design goal of bounded, semantics preetticcs-peeevinng perturbations; we therefore adopt s=1 by default and recommend staying within AugMix's prescribed ranges for robust adaptation.
Effect of Augmentation Strength We study the impact of AugMix [19
artifacts (noise) rather than
Effect of Encoding Methods for SNNs We assess sensitivity to spike encoding by replacing Poisson rate coding with temporal coding in SNN-VGG9 while keeping architecture, training, and TTA hyperparameters fixed. As reported in Table 9 the temporal-coded baseline is markedly less robust under domain shift than its rate-coded counterpart. This aligns with intuition: temporal coding relies on precise spike times and is therefore vulnerable to perturbation-indueer timing jitter, whereas rate coding averages over spikes, providing redundancy and noise tolerance.
Table 10 shows that SPACE improves both mod- cls, with consistently larger relative gains for the temporal-coded network. The mechanism is a lemporal-boundan effect: the total spike count is a sensitive proxy for timing stability because small perturbations can shift spikes across the fixed process- ing window; for spikes near the boundary, tiny jitters flip their contribution from 1 to O, causing discrete changes in the count. By enforcing consistency total counts across augmentations, SPACE penal- izes these coundar--crossing events and implicitly forces the model to stabilizes spike timing without
Table 9; Performance SNNs with different encoding under domain shift. Values are Top- accuracy (%); Acc. Loss Clean Shifted.
Encoding Method
Clean
Shifted
Acc. Loss
Temporal Coding
89.27
58.11
31.16
Rate Coding
90.61
66.57
24.04
25

---

Table 10: Performance comparison of spike encoding methods for ANN- model on CIFAR-IO-C regarding Accuracy (%). The bold number indicates the performance improvement.
Method
Gams.
Noise Shot
Impl
Deloc.
Glass
Motion
Zoom
Snow
Fog
Weather
Frost
Bri..
Contr.
Elas.
Digital
Pix.
JPG
Averae
Acc.
Tompueal Codiing
No. Adapt
39,74
39.32
68.42
59.59
54.45
60.19
13.90
69.788
46.21
06.77
78.14
27:26
70.29
46,00
79.59
58.11
SPACE
45.19
44.00
71.82
64.16
56,31
64.90
68.65
70.20
56
72.40
79.09
68.64
70.66
50,06
80,07
64.21 (46.10)
Rate Coding
No Adapt
72:38
43.57
22.54
75.01
70.25
66.57
SPACE
77.98
79.34
69.41
71.59
67.76
72.14
74.67
78.43
52.80
79.59
83.22
23.85
73.49
76.24
82.88
71.00 (+4.46)
Table 11: Performance comparison of SPACE with different objectives on CIFAR-10-C with SNN- VGG9 model regarding Accuracy (%). The bold number indicates the best result. Average membrane potential and spike through all time steps are referred as a.m.p and S.L.I. respectively.
Method
Gauss.
Noiae
Shot
Impl.
Defoc
Glass
Blur
Mosiin
Zoom
Snow
Fog
Weather
Frest
Bril.
Cantr.
Elas
Digital
Pix.
IPEG
Average
Acc.
No Adapt
72.38
74.70
58.57
63.05
63.96
64.44
21.35
76.32
43.57
75.72
82,44
22.54
25.01
70.25
84,28
66.57
SPACE
77.96
79.34
69.41
71.59
67.76
72.14
74.67
78.43
$2.80
79.59
83.22
23,85
75.49
76.24
82.88
71.03
SPACE + a.M.p.
74.87
76.05
61.64
64.90
65.79
05.88
72.22
77.66
45.66
76.64
82.81
21.57
75.53
70.91
84,74
67.79
SPACE+
72.74
73.84
57.86
62.91
63.66
64.33
70.94
76.35
43.16
74.77
82.30
21.20
74.24
69.35
83.97
brittle spike-to- sp-sk matching. The resulting gradients are stronger in temporal-coded models than in rate-coded ones, yielding larger robustness gains.
Exploration of Alternative Alignment Objectives We evaluate two alternatives to SPACE's default count-based feature map on CIFAR- 10-C with SNN-VGG9, keeping architecture, training, and TTA hyperparameters fixed. Results are summarized in Table II]
1) Average Membrane Potential (SPACE a.m.p.). Let U(x) E RTXCxD denote the LIF membrane traces from the selected layer (see subsection 3.2). For spikes, the post-reset potential is used. We form F(x) = 1 Σ.= U2(x)
E CxD and apply the same channel-wise consistency loss as SPACE. It improves over No Adapt but is consistently worse than count-based SPACE on most corruptions. Membrane traces are smooth and highly correlated across augmentations, making them less sensitive to discrete spiking changes; near-threshold, non-firing trajectories can be indistinguishable in F(x), whereas counts capture such events directly.
-
2) Spikes Through Time (SPACE s.L.t), Instead of summing over time, retain the temporal dimension of the spike tensor S(x) E RTxCCD and align per-channel activity at each time step across augmentations (loss averaged over f); optimization is unchanged. This variant does not improve robustness. Keeping the full temporal waveform inflates variance and forces the model to match sugmentation-sseeciee transients: benign timing jitter (e.g., spike at 1=10 vs. 1=12) is penalized as a mismatch, producing conflicting gradients that push spike times to align across views rather than stabilizing the underlying excitability. The model thus overfits to view-specific temporal shapes, hurting generalization, while also incurring higher compute and memory. We further applied Gaussian smoothing along time before alignment, = * S, to reduce jitter-induced overfitting. Results remained suboptimal. With small a, smoothing is too weak suppress thift-induced conflicts; with larger a,, it erases informative peaks and attenuates counts. The core issue is not merely information loss but the learning signal each representation provides: spike counts yield shift-invariant, macro- level target that is robust to timing jitter and directs clear adjustments to neuron excitability, whereas smoothed sequences still demand matching temporal shapes, preserving shift-sensitivity and inducing noisy, contradictory gradients.
Consequently, SPACE's count-based objective offers more stable and effective alignment signal for our TTA setting.
Statistical Significance We assess statistical significance using the Wilcoxon signed-rank test. Specifically, for each of the three main experiments, we run five independent trials and apply a paired Wilcoxon test between SPACE and each baseline across the five runs; reported p-values in Table 12 confirm that SPACE's gains are statistically significant. To further strengthen the evidence, we select Gaussian and Shot Noise from CIFAR-IO-C. and conduct Wilcoxon signed-rank tests the per-class level (Table 13). demonstrating that the improvements are consistent across categories rather than driven by a few classes.
26

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Table 12: Wilcoxon signed-rank test on CIFAR-IO-C, CIFAR-100-C, and CNN-based SNN models. W denotes the Wilcoxon signed-rank statistic and p is the two-sided p-value.
with
Oomporal Mithed Maric
Gaass
Nose Sloc
Impl.
Defoc.
Blur Mosion
Zoor
Saow
Foz
Weather
Froi
Bri.
Clest
Dightl
Pis.
ISG
Avecage
CIEAR C, SNN VGGB
45. No Aibei
W
0,0312
15
0.0012
15
0/312
15
0,0812
18
0.0012
15
0.0012
15
0.0012
15
0,0312
15
0.0312
15
00012
15
0.0312
15
0,3122
15
0.0012
15
000122
15
1,0000
0.012
15
RoTTA
0,0012
0.013
00012
00312
W0012
13. DeYO
W
0,0512
15
0.0612
15
011312
15
0.0812
15
0.0012
15
0.0012
15
0.0012
15
01512
15
0.0312
0.012
15
0,0312
15
0.0122
15
0.0012
15
01812
15
66312
15
0.511
15
SITA
0.0812
0.0012
0.0312
0.0012
0.0012
0.0122
0.012
0.0012
0,0122
00312
45. MEMO
1,0000
0.0012
00312
0.0812
15
0.0012
15
0.0012
15
0.0012
15
0,3122
15
0.0112
00012
15
0.4000
10
0.3383
0.0812
0,012
15
1,0000
06312
15
CIFAR JW0.
C. VGGB
Nu Adhei
0.0312
0.0012
18
0/312
15
0.8812
15
0.0012
18
0.0012
0.0012
15
0.1312
15
0.0312
0.012
15
0,0312
15
06811
15
0.0912
0,0312
15
60312
18
06312
15
No. ROTTA
P
0,0312
15
0.0012
15
0.312
0.0812
15
0.0812
0.0012
15
0.0012
15
0.0322
00012
0.0012
15
0.0312
06312
15
0.0812
0.012
15
6,312
06312
15
DeVD
0,0312
0.0912
0.0122
0/0812
0.0812
0.0022
0.0012
0.0312
0.0312
0.002
0mi1
06612
0.0812
0.012
15
66312
06312
N5. SITA
W
P
0.0M13
15
0.112
15
00112
0,012
15
0.0812
0.092
0.0012
15
0.012
0012
0.012
15
00012
15
00M2.
15
0.0122
00112
15
00M2.
15
0082
15
MEMO
0.0312
0.0012
15
0/312
0.0812
0.0812
0.012
0.0012
15
0.8312
0,0312
0.0022
0.0012
066312
0.0812
0.812
15
6.0000
06812
Tiny- C. C. SNN-VGG11
Adgit
15
15
15
15. ROTTA
W
0,0512
15
0.0012
15
00312
0,0812
15
0.0012
15
0.0012
0.0012
15
0,0312
15
0.0312
0.012
15
0.0312
15
0,3122
15
0.0012
15
0.012
15
6.312
15
06612
15
1.
15
0.0812
0.0022
45. SITA
W
0.0512
00512
15
00112
0.0812
15
0.012
15
a0812
15
ani12
15
am12
15
0012
15
a0i1
15
ami2
15
0682
15
00812
15
0112
15
0032
15
06312
15
MEMO
15
15
Table 13: Wilcoxon signed-rank test at the per-class level on CIFAR-10-C (Gaussian/Shot Noise) with CNN-based SNN models. W denotes the Wilcoxon signed-rank statistic and p is the two-sided p-value.
Class Index
Compared Method
Metric
2
5
7
8
Gaussian Noise
W
15
15
15
15
15
15
15
15
15 0.0312
vs. No Adapt
P
0.0312
0.0312
0,0312
0.0312
0.0312
0,0312
0.0312
0.0312
0.0312
W
15
15
15
15
15
15
15
15
15
15 0.0312
Vs. RoTTA
P 0,0312
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
W
15
15
15
15
15
15
15
15
15
15 0.0312
Vs. De
P 0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
W
15
15
15
15
15
15
15
15
vs. SITA
P
0.0312
0.0312
0,0312
0.0312
0.0312
0,0312
0.0312
0.0312
0.0312
0.0312
W
15
15
15
15
15
15
15
15
15
15 0.0312
vs. MEMO
P
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
Shot Noise
W
15
15
15
15
15
15
15
1.5
15
15 0.0312
Vs. No Adapt
P
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
W
15
15
15
15
15
15
15
15
15 0.0312
Vs. RoTTA
P
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
W
15
15
15
15
15
15
15
15
15
15 0.0312
VS. DeYO
P
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
W
15
15
15
15
15
15
15
14
15
15 0.0312
vs. SITA
P
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
0.0312
0.0625
0.0312
W
0
5
0
0
14
vs. MEMO
0.7812
1.0000
1.0000
0.7812
0,7812
1.0000
1.0000
1.0000
0.0625
1.0000
27


Reviews:
[{"review_id": "ZGQNqLEya9", "reviewer": "Reviewer_hnfy", "summary": "This work addresses domain shift by leveraging SNNs' unique spiking dynamics rather than output probabilities or batch normalization. The key insight is a spike-behavior consistency loss: For a single test sample and its augmented views, SPACE enforces similarity in local spike-based feature maps across the network layers. This approach bypasses limitations of existing methods that fail on SNNs due to their neglect of temporal spiking patterns and lack of BN layers.", "questions": "See weakness", "limitations": "The focus on domain adaptation overlooks SNNs' more fundamental challenge: closing the accuracy gap with ANNs. The practical value of TTA for current SNNs warrants scrutiny.", "rating": 2, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 1, "originality": 3, "strengths_and_weaknesses": "**Strengths: **\n\nS1: Directly exploits spike dynamics as adaptation signals—aligning with SNNs' fundamental computation mechanism. This addresses a critical gap in TTA literature, which predominantly targets ANNs.\n\nS2: Eliminates privacy/bandwidth constraints by avoiding source data.\n\nS3: Works with modern SNN architectures lacking BatchNorm.\n\n\n** Weaknesses: **\n\nW1: While SPACE leverages spike dynamics, the computational cost of optimizing spike-behavior consistency is not explicitly analyzed. For SNNs, which are often prized for low-latency inference, this overhead could affect practical deployment.\n\nW2: The impact of spike encoding methods (e.g., rate vs. temporal coding) on adaptation efficacy remains unexplored. Theoretical grounding for encoding-dependent robustness is needed.\n\nW3: The influence of critical parameters like time steps on adaptation performance is not discussed, despite their direct link to SNNs' temporal feature extraction.\n\nW4: Equation (4) relies on converting spike trains to continuous values for channel-wise alignment. Implementation details and potential information loss during conversion require clarification.\n\nW5: The focus on domain adaptation overlooks SNNs' more fundamental challenge: closing the accuracy gap with ANNs. The practical value of TTA for current SNNs warrants scrutiny.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thoughtful comments, which have helped us make our work clearer and more solid. We have carefully addressed all your concerns in detail.\n\n**[W1] Analyses of Computational Cost.** To evaluate computational efficiency, we compared the GFLOPs of different baselines with the same augmented batch size, and SPACE achieved the lowest GFLOPs, demonstrating its efficiency. Regarding the additional cost of optimizing spike-behavior consistency, this step is performed only **once** per input in single-sample setting, without requiring whole model backpropagation or iterative updates. While real hardware profiling would provide deeper insights, SPACE is feasible for deployment on neuromorphic platforms like BrainScale, SpiNNaker2, and Loihi2, where the preprocessing unit and inference module are typically **separate**. Augmentation batches can be pre-generated during the preprocessing stage, outside the neuromorphic core, and then fed into the system for efficient inference. This separation ensures that augmentation does not burden the neuromorphic core, maintaining energy and memory efficiency while leveraging our single-sample TTA strategy.\n|Method|RoTTA|DeYO|SITA|MEMO|SPACE|\n|-|-|-|-|-|-|\n|GFLOPs|160.67|318.07|161.48|161.48|**158.11**|\n|Accuracy|67.79%|67.99%|66.41%|69.20%|**71.03%**|\n\n**[W2] Effectiveness on Temporal Coding Model.** We thank the reviewer for this insightful suggestion. To explore this, we conducted additional experiments by replacing the Poisson rate coding in SNN-VGG9 with temporal coding—where neurons representing higher pixel values fire spikes earlier—while keeping all other structures and hyperparameters identical during training. We first observed that the baseline temporal-coded model is inherently less robust, showing a larger accuracy drop on domain-shifted data compared to its rate-coded counterpart. We posit this is because temporal coding encodes information in precise spike timings, making it highly sensitive to input perturbations that can disproportionately alter these timings. In contrast, rate coding relies on a statistical average of spikes, providing natural redundancy and resilience to such noise.\n|Coding|Clean Data|Shifted Data|Acc. Loss|\n|-|-|-|-|\n|Temporal Coding|89.27%|58.11%|**31.16%**|\n|Rate Coding|90.61%|66.57%|**24.04%**|\n\nWhile our method measures total spike counts rather than their precise timing, its enhanced efficacy in temporal-coded models stems from a powerful underlying principle: the total spike count serves as a highly sensitive proxy for temporal stability due to **the temporal boundary effect**. The core insight is that we regularize the macroscopic outcome (the count) to stabilize the microscopic cause (the timing). In a temporal coding regime, minor input perturbations cause significant spike timing jitter. For any spike occurring near the edge of the fixed processing window, even slight jitter can push its timing across this boundary. This induces a discrete, \"all-or-nothing\" change in that neuron's contribution to the total count (e.g., from 1 to 0). Our TTA method, by enforcing consistency in the total spike count across augmentations, directly targets the cumulative result of these boundary-crossing events. To satisfy this objective, the model is implicitly forced to adapt its weights to suppress the underlying temporal jitter, ensuring spike timings are generated robustly and do not erratically cross the window boundary. This discrete, high-magnitude error signal is far more pronounced than the small statistical fluctuations in a rate-coded model, allowing our method to apply a stronger and more effective corrective pressure, thus yielding a greater improvement in robustness. \\\nCrucially, this approach provides a significant advantage: by operating on the aggregate count rather than enforcing a strict, spike-for-spike temporal alignment, our method avoids the dual risks of overfitting to the specific augmentations and catastrophically forgetting the model's vital pre-trained knowledge.\n|Method||Noise|||Blur||||Weather||||Digital|||Acc.\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n||**Gauss.**|**Shot**|**Impl.**|**Defoc.**|**Glass**|**Motion**|**Zoom**|**Snow**|**Fog**|**Frost**|**Brit.**|**Contr.**|**Elas.**|**Pix.**|**JPEG**|**Avg.**|\n|Temporal&nbsp;Coding|||||||\n|No Adapt|39.74%|39.32%|68.42%|59.59%|54.43%|60.19%|65.90%|69.78%|46.21%|66.77%|78.14%|27.26%|70.29%|46.00%| 79.59%|58.11%|\n|SPACE|45.19%|44.00%|71.82%|64.16%|56.31%|64.90%|68.65%|70.29%|56.90%|72.40%|79.09%|68.64%|70.66%|50.06%|80.07%|64.21%(**+6.10%**)|\n|Rate&nbsp;Coding|||||||||\n|No Adapt|72.38%|74.70%|58.57%|63.05%|63.96%|64.44%|71.33%|76.32%|43.57%|75.72%|82.44%|22.54%|75.01%|70.25%|84.28%|66.57%|\n|SPACE|77.98%|79.34%|69.41%|71.59%|67.76%|72.14%|74.67%|78.43%|52.80%|79.59%|83.22%|23.85%|75.49%|76.24%|82.88%|71.03%(**+4.46%**)|\n\n**[W3] Impact of Time Steps.** The time steps in our method are predetermined during the model's pretraining phase and remain fixed during TTA. Regardless of the specific number of inference time steps, our method operates by aggregating spike counts across the entire time window to extract temporal features. This design ensures that our approach is **independent** of the exact time step setting. In our experiments, we evaluated four different backbones with varying time steps: SNN-VGG (25), SNN-ResNet (30), Spike-driven Transformer V3 (4), and SNN-ConvLSTM (32). The consistent performance of our method across these diverse architectures demonstrates its robustness and generalizability, even when applied to models with different temporal configurations. This independence from time step settings highlights the flexibility of our TTA framework and its ability to adapt effectively across various SNN structures.\n\n**[W4] Potential Info Loss in Equation (4).** Equation (4) relies on converting discrete spike counts into continuous probability values via a softmax function. This transformation is not a source of unintended information loss, but rather a **principled mechanism for robust feature selection** that is central to our method's success. The \"information loss\" is the intentional discarding of the absolute magnitude of spike counts to focus on the **relative importance** of neurons within a channel. By applying softmax, we normalize away noisy fluctuations in absolute firing rates and create a stable spatial saliency map. Aligning these maps forces the model to learn which spatial features are consistently most important, directly encouraging the learning of invariant representations. \\\nIn addition, while our alignment objective operates on this spatially-focused abstraction, it simultaneously **preserves and refines the inherent temporal characteristics of the SNN**. The underlying model remains a fully temporal processor, and the spike counts are the direct result of its complex dynamics. By demanding consistency in the outcome (the count-based saliency map), our method implicitly forces the network to stabilize the cause (the spike generation process). This creates a powerful regularization pressure that reduces temporal jitter and enhances the robustness of spike timing, all without the computational cost and overfitting risks associated with direct, spike-for-spike temporal alignment.\n\n**[W5] Motivation of the Work.** Thank you for this thoughtful question, as it touches upon the core motivation for our research. We agree completely that the practical value of any new method is a paramount concern, and we appreciate the opportunity to clarify our perspective on why TTA is a crucial and timely area of study for SNNs. \\\nOur work is built upon the exciting recent progress in the SNN field. While a performance gap has historically been a key consideration, several recent studies [1,2] have shown that SNNs can now achieve accuracy that is highly competitive with their ANN counterparts. This convergence in performance makes SNNs a highly compelling choice for applications where their unique architectural advantages are paramount. Specifically, for tasks involving event-based sensing (e.g., DVS cameras) or deployment on resource-constrained edge hardware, the inherent efficiency and temporal processing capabilities of SNNs make them a more natural and powerful solution than traditional ANNs. Crucially, in these target applications—robotics navigating new environments, devices operating in changing weather, or wearables adapting to user behavior—domain shift is not a hypothetical risk, but an operational certainty. An SNN that achieves high accuracy on a static benchmark but fails when faced with real-world variations is of limited practical utility. We therefore posit that **robustness to domain shift is a prerequisite for deployment, not a post-hoc optimization**. \\\nAddressing this adaptation challenge, however, is not straightforward. The established TTA techniques developed for ANNs are often not directly transferable to the discrete, sparse, and temporal dynamics of spiking neurons (referring to my response to Reviewer sQFP [W3&Q1] and Reviewer 4hgi [W2]). This highlights a specific need for adaptation methods designed for the SNN paradigm. Therefore, our research is motivated by the goal of bridging this gap. By developing a TTA method tailored for SNNs, we aim to enhance their robustness and practical value in the very domains where they are already becoming a preferred solution. We see this as a necessary and complementary step to the ongoing work on accuracy, ensuring that high-performing SNNs are also reliable and effective when deployed in the dynamic conditions of the real world.\n\nReference \\\n[1] Yao, Man, et al. \"Scaling spike-driven transformer with efficient spike firing approximation training.\" IEEE Transactions on Pattern Analysis and Machine Intelligence (2025). \\\n[2] Zhou, Zhaokun, et al. \"Spikformer: When Spiking Neural Network Meets Transformer.\" The Eleventh International Conference on Learning Representations, 2023."}, {"review_id": "EZzrMQRHt0", "reviewer": "Reviewer_sQFP", "summary": "The paper identifies the challenges that traditional Test-Time Adaptation (TTA) methods face when applied to SNNs due to ignoring the fundamental essence of SNNs. For example, MEMO [4], a traditional TTA method, just focuses on optimizing the entropy of prediction and fails to capture the fine-grained spiking dynamics that encode essential features. Also, in real-world scenarios, access to source data is restricted due to sensitive privacy. To mitigate these issues, the paper proposes SPACE framework, the first source-free and single test-time adaptation framework. SPACE leverages the unique dynamic of SNNs to maximize the consistency of feature maps across several augmented versions of the same input sample. To this end, the paper conducts extensive experiments through several datasets to demonstrate the effectiveness and robustness of the proposed framework.", "questions": "1. Could the authors conduct more experiments to compare with the current or SOTA Source-free Domain Adaptation methods such as SHOT [1], RoTTA [2], and Tast [3],...\n\n\n2. The authors argue that when the feature maps of augmented samples are highly similar, the model becomes less sensitive to variations at test time. Why does the model ensure that it will learn core characteristics of samples instead of noise or variation? How could the model do that?\n\n3. Could the authors clarify why MMD did not work?\n\n4. Could the authors provide additional theoretical guarantees for the model’s robustness?\n\n5. Although the authors provide the impact of the number of augmentations on the model, could the authors investigate the model’s sensitivity to augmentation strength?\n\n6. As the authors claims that their SNNs have the advantage of energy efficiency, could the authors conduct additional experiments for energy consumption, either theoretical estimates or real hardware profiling?", "limitations": "1. The number of compared methods is limited, resulting in not highlighting the robustness of the proposed method comprehensively.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The paper is well-written and easy to follow.\n\n2. The challenges when applying traditional TTA for SNNs are thoroughly discussed.\n\n3. This paper is the first attempt to adopt both Source-free and Test-Time Adaptation to SNNs.\n\n4. The motivation of the proposed method is reasonably discussed.\n\n5. The proposed framework is rigorously evaluated across diverse architectures and real-world scenarios. \n\n\n\nWeaknesses:\n\n1. Generating many augmentations and doing forward/backward processes may introduce high-cost energy and memory, even becoming more severe when employed in real-time or low-energy neuromorphic hardware.\n\n2. The authors claim that consistency-based feature alignment helps the model become more robust, but do not provide a theoretical analysis on robustness bounds.\n\n3. The number of compared methods is limited; SITA and MEMO were published since 2022. It would be better to compare with the current or SOTA Source-free Domain Adaptation methods.", "was_revised": true, "final_justification": "My concerns have been satisfactorily addressed.", "rebuttal": "Thank you for your thoughtful comments, which have helped us make our work clearer and more solid. We have carefully addressed all your concerns in detail.\n\n**[W1\\&Q6] Analyses of Computational Cost.** To evaluate computational efficiency, we compared the GFLOPs of different baselines with the same augmented batch size, and SPACE achieved the lowest GFLOPs, demonstrating its efficiency. Regarding the additional cost of optimizing spike-behavior consistency, this step is performed only **once** per input in single-sample setting, without requiring whole model backpropagation or iterative updates. While real hardware profiling would provide deeper insights, SPACE is feasible for deployment on neuromorphic platforms like BrainScale, SpiNNaker2, and Loihi2, where the preprocessing unit and inference module are typically **separate**. Augmentation batches can be pre-generated during the preprocessing stage, outside the neuromorphic core, and then fed into the system for efficient inference. This separation ensures that augmentation does not burden the neuromorphic core, maintaining energy and memory efficiency while leveraging our single-sample TTA strategy.\n|Method|RoTTA|DeYO|SITA|MEMO|SPACE|\n|-|-|-|-|-|-|\n|GFLOPs|160.67|318.07|161.48|161.48|**158.11**|\n|Accuracy|67.79%|67.99%|66.41%|69.20%|**71.03%**|\n\n**[W2\\&Q4] Theoretical analysis on Feature Alignment.** Our method is theoretically grounded in the well-established principle of **consistency regularization** [1,2], operationalized at inference time. Specifically, we generate an augmented batch from a single test sample and adapt the model by maximizing the similarity of its internal feature representations (i.e., spike counts). This enforces feature-level consistency, compelling the model to learn an invariant mapping for semantic-preserving transformations introduced by augmentations. Importantly, these augmentations remain **within a reasonable range (e.g., AugMix [9])**, ensuring robustness to meaningful, controlled perturbations without introducing excessive noise. \\\nThe robustness of our method can be understood from two complementary theoretical perspectives. From an information-theoretic viewpoint, the process approximates the objective of an information bottleneck by making the feature representations maximally informative about the input’s identity while minimizing sensitivity to augmentation-induced variations. This results in a more disentangled and robust feature space. From a geometric perspective, if test-time perturbations push a sample off the learned data manifold, our adaptation process effectively projects it back toward a stable, central point within the local manifold defined by the augmentations. This \"denoising\" effect mitigates the impact of perturbations, stabilizing the feature representation and ensuring reliable predictions. Thus, the enhanced robustness of our method arises naturally from enforcing invariance and representational stability within the SNN-TTA framework.\n\n**[W3\\&Q1] Additional Baselines.**  We would like to clarify the setting of our work. In our **single-sample TTA** scenario, each sample undergoes test-time adaptation independently. This setting reflects real-world scenarios where it is often impractical to test with a mini-batch of data. Consequently, many batch-based TTA methods that rely on adjusting normalization layers [1,2] are not suitable for this setting. Moreover, some SNN architectures lack normalization layers altogether. Therefore, we initially chose SITA and MEMO, two single-instance TTA methods, as baselines. \\\nFor the ANN-based TTA methods such as SHOT[5] and TAST[6], they are incompatible with SNNs for the following reasons:\n1. SHOT relies on pseudo-labeling in a static feature vector space, which disregards the critical temporal dynamics of SNNs. This results in incomplete feature representation and invalidates distance-based metrics.\n2. SHOT freezes the static classifier layer, but SNN classification is tightly coupled with spiking dynamics over time. There is no standalone, static classification layer that can be frozen without disrupting the functionality.\n3. TAST depends on prior probabilities, which are similarly unsuitable given SNNs' temporal and discrete spiking nature. \n\nTo address the your suggestion, we additionally included RoTTA[7] and the recent DeYO[8] as baselines. Since both methods require batch data, we applied the same augmentation strategy as SPACE to create a pseudo-batch for each sample to ensure a fair comparison. The results are shown below. RoTTA's memory bank cannot be effectively utilized in the independent single-sample setting, leading to limited performance gains. DeYO's reliance on smooth probability outputs for confidence comparison does not align with SNN's discrete, spike-based outputs, introducing significant errors. Our SPACE method, designed specifically for SNNs, outperforms these baselines while maintaining lower computational cost.\n|Method||Noise|||Blur||||Weather||||Digital|||Acc.\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n||**Gauss.**|**Shot**|**Impl.**|**Defoc.**|**Glass**|**Motion**|**Zoom**|**Snow**|**Fog**|**Frost**|**Brit.**|**Contr.**|**Elas.**|**Pix.**|**JPEG**|**Avg.**|\n|No&nbsp;Adapt|72.38%|74.70%|58.57%|63.05%|63.96%|64.44%|71.33%|76.32%|43.57%|75.72%|82.44%|22.54%|75.01%|70.25%|84.28%|66.57%|\n|RoTTA|73.60%|75.49%|61.61%|66.08%|67.53%|67.75%|72.40%|74.34%|51.54%|76.35%|80.98% |23.01%|74.23%|68.60%|83.29%|67.79%|\n|DeYO|73.97%|76.16%|62.45%|66.29%|67.37%|67.60%|73.52%|74.90%|50.68%|76.23%|81.30% |20.90%|75.19%|70.44%|82.85%|67.99%|\n|SITA|73.06%|74.15%|58.41%|62.94%|64.21%|64.36%|70.72%|76.67%|43.40%|75.24%|82.51% |22.02%|74.74%|69.62%|84.16%|66.41%|\n|MEMO|77.73%|79.50%|65.74%|65.61%|67.50%|66.24%|72.61%|77.38%|45.47%|78.64%|83.05% |23.83%|76.45%|73.25%|85.05%|69.20%|\n|SPACE|77.98%|79.34%|69.41%|71.59%|67.76%|72.14%|74.67%|78.43%|52.80%|79.59%|83.22% |23.85%|75.49%|76.24%|82.88%|71.03%|\n\n**[W2\\&Q2\\&Q5] Analysis of Augmentation Robustness Bounds.** While our work does not provide a formal theoretical analysis on robustness bounds, it builds upon the theoretical insights provided by AugMix [9], which is specifically designed to improve robustness and uncertainty calibration by enforcing consistent embeddings of augmented images. AugMix defines clear boundaries for transformations (e.g., maximum rotation angle of 30°, translation within ±16 pixels), ensuring that noise is not excessive and the model learns core, invariant characteristics instead of overfitting to spurious variations. AugMix provides 10 predefined strength levels, which control the intensity of augmentations applied. Our main setup followed MEMO's configuration (strength = 1), which is the lowest and most controlled level, ensuring robustness without introducing excessive noise. To investigate the model's sensitivity to augmentation strength, we conducted additional experiments with different AugMix strength. Within AugMix’s designed boundaries, accuracy remained stable, with only slight declines as strength increased. However, when we broke AugMix’s boundaries (e.g., increasing rotation angles to 50° or translation to ±18 pixels), accuracy dropped significantly, demonstrating that overly strong augmentations introduce harmful noise. These findings validate AugMix’s reliability and SPACE’s ability to focus on core features while maintaining robustness under controlled augmentation settings.\n|AugMix Strength|1|3|5|7|10|Larger Boundary|\n|-|-|-|-|-|-|-|\n|Acc. on Gaussian Noise|77.98%|77.88%|77.48%|77.41%|77.02%|74.52%|\n\n**[Q3] Why MMD did not work.** The MMD-based method still achieves good TTA performance overall. However, compared to the simpler approach of directly aligning spike counts without the MMD mapping, it does not provide further improvement and, in some cases, results in a slight drop in accuracy. We hypothesize that this is because the spike count feature map in its original space already captures the essential characteristics of the sample, and further mapping it into a higher-dimensional space introduces unnecessary complexity or noise that does not contribute to better alignment. MMD is generally effective when there is a large discrepancy between distributions, but in our case, the augmentations are controlled (e.g., via AugMix) and already maintain strong consistency. Thus, the direct alignment of spike counts is sufficient and avoids additional computational overhead. These findings suggest that our simpler approach is more effective and efficient for aligning spike behaviors in SNNs.\n\nReference \\\n[1] Tarvainen, Antti, and Harri Valpola. \"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.\" Advances in neural information processing systems 30 (2017). \\\n[2] Zhang, Marvin, Sergey Levine, and Chelsea Finn. \"Memo: Test time robustness via adaptation and augmentation.\" Advances in neural information processing systems 35 (2022): 38629-38642. \\\n[3] Boudiaf, Malik, et al. \"Parameter-free online test-time adaptation.\" CVPR, 2022. \\\n[4] Lim, Hyesu, et al. \"TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation.\" ICLR, 2023. \\\n[5] Liang, Jian, Dapeng Hu, and Jiashi Feng. \"Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation.\" ICML, 2020. \\\n[6] Jang, Minguk, Sae-Young Chung, and Hye Won Chung. \"Test-Time Adaptation via Self-Training with Nearest Neighbor Information.\" ICLR, 2023. \\\n[7] Yuan, Longhui, Binhui Xie, and Shuang Li. \"Robust test-time adaptation in dynamic scenarios.\" CVPR, 2023. \\\n[8] Lee, Jonghyun, et al. \"ENTROPY IS NOT ENOUGH FOR TEST-TIME ADAPTATION: FROM THE PERSPECTIVE OF DISENTANGLED FACTORS.\" ICLR, 2024. \\\n[9] Hendrycks, Dan, et al. \"AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty.\" ICLR, 2020."}, {"review_id": "NpaaouQJng", "reviewer": "Reviewer_t7Fe", "summary": "The authors provide a technique for re-training SNNs on image data at test-time to achieve higher accuracies on those samples.", "questions": "- I'm not an expert at test-time adaption, but I'm somewhat confused why this is needed. I understand SNNs would perform bad at test time if trained without data augmentation, and then performing the author's similarity aligning approach at test-time seems to help. But wouldn't it also help/solve the problem to just train with good data augmentation to begin with? I can't seem to find any controls in the paper to this extent that show this: train just on dataset and train on dataset with good augmentation and performing the author's test-time method.\n- It's also unclear to which degree it's possible to perform the proposed algorithm on neuromorphic computers e.g. I'm not aware of neuromorphic performing batch data augmentation?\n- Why was rate-coding chosen for the similarity measure - in particular as the sparsity of individual spikes is a nice property of SNNs when deploying them for inference on neuromorphic computers; shouldn't this be taken into account?", "limitations": "yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 1, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- I'm impressed by the number of datasets and control models the authors test. Clearly they have put in a lot of effort into the work, which deserves merit.\n\nWeaknesses:\n- I'm a bit weary of the method actually performing better. The results in Table 1 would need statistical validation, I'd suggest running a Wilcoxon Signed-Rank Test.", "was_revised": true, "final_justification": "The author's have addressed my questions and concerns.", "rebuttal": "Thank you for your thoughtful comments, which have helped us make our work clearer and more solid. We have carefully addressed all your concerns in detail.\n\n**[W1] Wilcoxon Signed-Rank Test.** Thank you for your insightful suggestion. Initially, the results in Table 1 were obtained by running experiments three times with different random seeds and selecting the best outcome. Following your recommendation, we conducted two additional sets of experiments on CIFAR-10-C and performed a Wilcoxon Signed-Rank Test. The results shown in the table below, consistent with our original findings, confirm that our method achieves statistically significant improvements over the baselines. This further validates the effectiveness of our approach. We will give the Wilcoxon Signed-Rank Test for all experiments in the revised version.\n|Compared Method|Metric||Noise|||Blur||||Weather||||Digital||||\n|:----------------:|:------:|:----------:|:--------:|:---------:|:----------:|:---------:|:----------:|:--------:|:----------:|:-------:|:---------:|:---------:|:----------:|:---------:|:--------:|:--------:|:--------:|\n||| **Gauss.** | **Shot** | **Impl.** | **Defoc.** | **Glass** | **Motion** | **Zoom** |  **Snow**  | **Fog** | **Frost** | **Brit.** | **Contr.** | **Elas.** | **Pix.** | **JPEG** | **Avg.** |\n| **vs.&nbsp;No&nbsp;Adapt** |  **W** |15|15|15|15|15|15|15|15|15|15|15|15|15|15|0|15|\n||  **p** |0.0312|0.0312|0.0312|0.0312|0.0312|0.0312|0.0312|0.0312|0.0312|0.0312|0.0312|0.0312|0.0312|0.0312|1.0000|0.0312|\n|   **vs. SITA**   |  **W** |15|15|15|15|15|15|15|15|15|15|15|15|15|15|0|15|\n||  **p** |0.0312|0.0312|0.0312|0.0312|0.0312|0.0312|0.0312|0.0312|0.0312|0.0312|0.0312|0.0312|0.0312|0.0312|1.0000|0.0312|\n|   **vs. MEMO**   |  **W** |15|0|15|15|15|15|15|15|15|15|10|9|0|15|0|15|\n||  **p** |0.0312|1.0000|0.0312|0.0312|0.0312|0.0312|0.0312|0.0312|0.0312|0.0312|0.4000|0.3333|0.0312|0.0312|1.0000|0.0312|\n\n**[Q1] Explanation of Domain Shift.** Thank you for raising this interesting point. To clarify, our method addresses the challenge of domain shift, where the distribution of the test data differs from that of the training data. While training with data augmentation can improve generalization within **the same domain**, it cannot fully prepare the model for unseen domains, as training-time augmentations are inherently limited to the distributions observed or assumed during training. TTA, on the other hand, is specifically designed to handle **cross-domain** scenarios. In real-world applications, the domain shift caused by unknown factors, such as changes in environment or sensor characteristics, is often unpredictable and cannot be accounted for during training. Our method leverages the test-time data itself by performing augmentations within the shifted domain and aligning the features in our space, enabling unsupervised TTA to adapt effectively to the new domain, even when both labels and domain-shifted information are unavailable beforehand.\n\n**[Q2] Feasibility of Deployment.** On neuromorphic platforms like BrainScale, SpiNNaker2, and Loihi2, the data preprocessing unit and neural network inference module are typically **separate**. Batch data augmentation can be performed in the preprocessing stage, outside the neuromorphic core, before feeding data into the system for inference. This design allows augmentation to be handled efficiently without impacting the neuromorphic computing core, making our method feasible on such platforms.\n\n**[Q3] Similarity Measurement Objective.** Our method calculates the total spike count at only the final timestep and only focuses on one specific layer of neurons. This design choice ensures that it does not interfere with the sparse spike propagation characteristic of SNNs during inference. Additionally, as discussed in Appendix B, we explored aligning spikes at every timestep. However, this approach caused the model to overfit to augmentation-specific details, losing essential underlying information and ultimately degrading TTA performance. Our choice strikes a balance between preserving sparsity and achieving robust TTA."}, {"review_id": "N0jAx8MTxY", "reviewer": "Reviewer_4hgi", "summary": "This paper proposes a source-free single-instance test-time adaptation framework for SNNs to address the problem of their high sensitivity to distribution shifts, characteristics of real-world scenarios. Traditional test-time adaptation methods in ANNs, such as MEMO and SITA, cannot match the internal dynamics or external architecture of SNNs. To this end, the authors introduce a spike-aware consistency enhancement method, in which a spike feature map extractor is trained to mitigate distribution shifts of the augmented batch from a single instance and is then incorporated into the prediction model. Experiments demonstrate the consistent performance improvements on different network structures and datasets.", "questions": "1. Why didn't the TTL method consider cosine similarity when designing the similarity function?\n\n2. Line 273: What is the number of inference steps used in Spike-driven V3 and SNN-ConvLSTM?\n\n3. Line 15 in Appendix: What is the kernel function \\phi ?\n\n4. How is it possible to improve the similarity of domain-shifted instances during the training of the extractor while maintaining classification performance? This question is purely out of my curiosity.\n\n5. minors:\n(1) line 56: names->named\n(2) line 211: F(x_1), F(x_1)->F(x_1), F(x_2)", "limitations": "I agree that alternative optimization strategies to expand the practicality can be explored for neuromorphic hardware and energy-efficient AI.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1.The observed performance improvements on various datasets and network frameworks confirm the applicability of the proposed approach.\n\n2. The ablation study of the feature map, along with the extended investigation of kernel techniques and objectives in the appendix, is detailed and valuable.\n\n3. The paper is well-organized and is clearly articulated.\n\nWeaknesses:\n\n1. Since the paper states in line 34 that“SNNs are particularly vulnerable to domain shifts,” the authors are implying that SNNs are more prone to domain‑shift issues than ANNs. Yet, they neither cite any prior work analyzing robustness or domain-shift problems of SNNs, nor present any empirical or theoretical observations to support it, nor compare SNNs to ANNs in terms of domain shift. This significantly weakens the persuasiveness of their motivation.\n\n2. Given that this is the first work integrating SNNs and TTA, it would be valuable to compare its performance against existing ANN baselines. The authors should consider adding such experiments.\n\n3. The description of the SPACE framework is not clear enough. In Figure 2, the SPACE framework consists of two parts: the SNN Feature Extractor and the Classifier. Could the authors please clarify which part of the neural network the Extractor refers to? According to my understanding, if a neural network has L layers, the classifier is usually the last one or two layers, so the Extractor seems to be the main body of the network. How would the performance of SPACE change if different depths of the Extractor were used (for example, dividing the network into more layers for the classifier)?\n\n4. In Line 334, the authors mention the \"global feature map\" for the first time but do not explain what it means. Moreover, what is the difference between the global feature map and the local feature map?", "was_revised": true, "final_justification": "I think Test-Time Adaptation is helpful for the SNN field. After reviewing the authors’ response, I raised my score.", "rebuttal": "Thank you for your thoughtful comments, which have helped us make our work clearer and more solid. We have carefully addressed all your concerns in detail.\n\n**[W1] SNN's Vulnerability to Domain Shift.** As mentioned in [1,2,3], prior works have highlighted the robustness challenges and vulnerability of SNNs to domain shift. To further support this claim, we conducted additional experiments where we evaluated the performance of both SNNs and ANNs with the same architecture and scale on shifted datasets. The results, shown in the table below, indicate that SNNs experience a significantly larger accuracy drop compared to ANNs under domain shifts. These findings validate our motivation and emphasize the need for developing robust TTA methods for SNNs.\n|Architecture|Dataset Series|Clean Data|Shifted Data|Acc. Loss|Clean Data|Shifted Data|Acc. Loss|\n|-|-|-|-|-|-|-|-|\n||||ANN|||SNN||\n|VGG9|CIFAR10|90.53%|77.25%|**13.28%**|90.61%|67.86%|**22.74%**|\n|VGG11|CIFAR100|70.86%|48.32%|**22.54%**|69.50%|41.35%|**28.14%**|\n|VGG11|TinyImageNet|61.24%|19.76%|**41.48%**|58.36%|14.56%|**43.80%**|\n|Transformer|ImageNet|85.16%|75.49%|**9.67%**|79.60%|67.64%|**11.96%**|\n\n**[W2] Additional Baselines.** We would like to clarify the setting of our work. In our **single-sample TTA** scenario, each sample undergoes test-time adaptation independently. This setting reflects real-world scenarios where it is often impractical to test with a mini-batch of data. Consequently, many batch-based TTA methods that rely on adjusting normalization layers [4,5] are not suitable for this setting. Moreover, some SNN architectures lack normalization layers altogether. Therefore, we initially chose SITA and MEMO, two single-instance TTA methods, as baselines. \\\nFor the ANN-based TTA methods such as SHOT[6] and TAST[7] mentioned by Reviewer sQFP, they are incompatible with SNNs for the following reasons:\n1. SHOT relies on pseudo-labeling in a static feature vector space, which disregards the critical temporal dynamics of SNNs. This results in incomplete feature representation and invalidates distance-based metrics.\n2. SHOT freezes the static classifier layer, but SNN classification is tightly coupled with spiking dynamics over time. There is no standalone, static classification layer that can be frozen without disrupting the functionality.\n3. TAST depends on prior probabilities, which are similarly unsuitable given SNNs' temporal and discrete spiking nature. \n\nTo address the your suggestion, we additionally included RoTTA[8] and the recent DeYO[9] as baselines. Since both methods require batch data, we applied the same augmentation strategy as SPACE to create a pseudo-batch for each sample to ensure a fair comparison. The results are shown below. RoTTA's memory bank cannot be effectively utilized in this single-sample setting, leading to limited performance gains. DeYO's reliance on smooth probability outputs for confidence comparison does not align with SNN's discrete, spike-based outputs, introducing significant errors. In addition, all the baselines exhibit higher computational costs (GFLOPs). Our SPACE method, designed specifically for SNNs, outperforms these baselines while maintaining lower computational cost.\n|Method||Noise|||Blur||||Weather||||Digital|||Acc.|GFLOPs\n|-|-|-|-|---------|--------|--------|--------|---------|--------|--------|------------|----------|-------------------|----------|------------------|--------|--------|\n|| **Gauss.** | **Shot** | **Impl.** | **Defoc.** | **Glass** | **Motion** | **Zoom** |  **Snow**  | **Fog** | **Frost** | **Brit.** | **Contr.** | **Elas.** | **Pix.** | **JPEG** | **Avg.** ||\n|No&nbsp;Adapt|72.38%|74.70%|58.57%|63.05%|63.96%|64.44%|71.33%|76.32%|43.57%|75.72%|82.44%|22.54%|75.01%|70.25%|84.28%|66.57%|-|\n| RoTTA|73.60%|75.49%|61.61%|66.08%|67.53%|67.75%|72.40%|74.34%|51.54%|76.35%|80.98% |23.01%|74.23%|68.60%|83.29%|67.79%|160.67|\n| DeYO |73.97%|76.16%|62.45%|66.29%|67.37%|67.60%|73.52%|74.90%|50.68%|76.23%|81.30% |20.90%|75.19%|70.44%|82.85%|67.99%|318.07|\n| SITA |73.06%|74.15%|58.41%|62.94%|64.21%|64.36%|70.72%|76.67%|43.40%|75.24%|82.51% |22.02%|74.74%|69.62%|84.16%|66.41%|161.48|\n| MEMO |77.73%|79.50%|65.74%|65.61%|67.50%|66.24%|72.61%|77.38%|45.47%|78.64%|83.05% |23.83%|76.45%|73.25%|85.05%|69.20%|162.48|\n| SPACE|77.98%|79.34%|69.41%|71.59%|67.76%|72.14%|74.67%|78.43%|52.80%|79.59%|83.22% |23.85%|75.49%|76.24%|82.88%|71.03%|158.11|\n\n**[W3] More Description of Our Framework.** In our framework, the feature extractor refers to the main body of the neural network, which could be a CNN, Transformer, or ConvLSTM, excluding the last one or two fully connected layers that form the classifier. We observe the feature map of the last layer of the feature extractor. If this layer’s spatial dimension is 1×1, alignment targets become too scarce, potentially leading to overfitting. In such cases, we use the penultimate layer of the feature extractor to ensure that the alignment targets retain rich, high-level semantic information that is more compact and representative, reducing redundancy while focusing on features critical for classification. We also experimented with aligning the classifier’s feature map before, but this approach sometimes degraded performance in certain domains. We attribute this to the classifier being more sensitive to changes, which we believe makes the TTA process less stable and potentially more prone to catastrophic forgetting [10]. Moreover, aligning deeper feature maps introduces additional computational costs.\n\n**[W4] Explanation of Global Feature Map.** The \"global feature map\" refers to treating the entire feature map of size $C \\times H \\times W$ as a single entity and measuring similarity on this full representation. In contrast, the \"local feature map\" considers each spatial position in the $H \\times W$ dimension independently, calculating similarity at each  $H \\times W$ dimension and averaging across the $C$ channels. This distinction reflects whether similarity is computed globally or spatially at a finer granularity. We’ll clarify this in the revised version.\n\n**[Q1] Similarity Function.** Our method uses softmax followed by inner product to evaluate similarity, which, like cosine similarity, is based on inner product. However, cosine similarity involves dividing by the L2 norm, which can lead to division-by-zero errors due to the sparsity of SNNs, where some neurons in certain channels may produce zero spikes across all timesteps. To avoid this issue and ensure robust similarity computation, we opted for our current design.\n\n**[Q2] Inference Steps.** We follow the setting in Spike-driven Transformer V3 work [11], where the time step is set to **4**. The time steps used in ConvLSTM is **32**.\n\n**[Q3] Kernel Function $\\phi$.**  $\\phi$ represents an **implicit** mapping function that projects input data from the original space $\\mathcal{Z}$ to a higher-dimensional RKHS $\\mathcal{H}$. This mapping is implicitly defined by the kernel function $k(\\mathbf{z},\\mathbf{z}')$, such that the inner product in $\\mathcal{H}$ is given by $\\langle \\phi(\\mathbf{z}),\\phi(\\mathbf{z}')\\rangle_\\mathcal{H}=k(\\mathbf{z},\\mathbf{z}')$. Hence, $\\phi$ does not need to be explicitly constructed, as all computations in $\\mathcal{H}$ can be performed directly through the kernel function, which is Gaussian kernel here. \n\n**[Q4] Transfer Our Strategy on Training Phase.** This is indeed a great idea. By incorporating our strategy into the loss function during training, we can further amplify the impact of augmentation in the training phase. Due to time constraints, we implemented this approach on the ConvLSTM backbone, and the test accuracy improved from 90.23% to 91.97%. In future work, we plan to explore more efficient methods along these lines to help further narrow the gap between SNNs and ANNs.\n\n**[Q5] Typographical Errors.** Thank you for pointing that out! The errors have been corrected in the revised version.\n\nReference \\\n[1] Karilanova, Sanja, et al. \"Zero-shot temporal resolution domain adaptation for spiking neural networks.\" arXiv preprint arXiv:2411.04760 (2024). \\\n[2] Huang, Yifan, et al. \"Flexible and scalable deep dendritic spiking neural networks with multiple nonlinear branching.\" arXiv preprint arXiv:2412.06355 (2024). \\\n[3] Mei, Zaidao, Mark Barnell, and Qinru Qiu. \"Unsupervised Adaptation of Spiking Networks in a Gradual Changing Environment.\" 2022 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2022. \\\n[4] Boudiaf, Malik, et al. \"Parameter-free online test-time adaptation.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022. \\\n[5] Lim, Hyesu, et al. \"TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation.\" The Eleventh International Conference on Learning Representations, ICLR 2023, 2023. \\\n[6] Liang, Jian, Dapeng Hu, and Jiashi Feng. \"Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation.\" International conference on machine learning. PMLR, 2020. \\\n[7] Jang, Minguk, Sae-Young Chung, and Hye Won Chung. \"Test-Time Adaptation via Self-Training with Nearest Neighbor Information.\" 11th International Conference on Learning Representations, ICLR 2023, 2023. \\\n[8] Yuan, Longhui, Binhui Xie, and Shuang Li. \"Robust test-time adaptation in dynamic scenarios.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. \\\n[9] Lee, Jonghyun, et al. \"ENTROPY IS NOT ENOUGH FOR TEST-TIME ADAPTATION: FROM THE PERSPECTIVE OF DISENTANGLED FACTORS.\" 12th International Conference on Learning Representations, ICLR 2024. 2024. \\\n[10] Chen, Kecheng, et al. \"Test-time Adaptation for Foundation Medical Segmentation Model without Parametric Updates.\" International Conference on Computer Vision, 2025.\n[11] Lee, Chankyu, et al. \"Enabling spike-based backpropagation for training deep neural network architectures.\" Frontiers in neuroscience 14 (2020): 497482."}]

Author rebuttal:

Meta-review: ## Summary of Scientific Claims and Findings    
This paper proposes SPACE, the first test-time adaptation (TTA) algorithm specifically designed for Spiking Neural Networks (SNNs). It addresses SNNs’ vulnerability to domain shifts by leveraging spike-aware consistency regularization, aligning spike-based local feature maps across augmented views of single test samples without requiring source data. SPACE is evaluated on multiple datasets and diverse SNN architectures—including CNNs, Transformers, and ConvLSTMs—demonstrating superior robustness and performance compared to state-of-the-art ANN and SNN TTA baselines. The method is theoretically grounded in consistency regularization and information bottleneck principles and is argued to be practical for deployment on neuromorphic hardware.  

  
## Strengths    
- Novelty: The first source-free, single-instance TTA method tailored to the discrete, temporal, and event-driven nature of SNNs.    
- Theoretical grounding: Provides both information-theoretic and geometric interpretations of feature alignment to justify robustness improvements.    
- Comprehensive empirical evaluation: Extensive experiments across multiple datasets, architectures, and corruption types.    
- Demonstrated superiority: Outperforms multiple state-of-the-art ANN and SNN TTA methods with statistical significance.    
- Practicality: Discusses efficient implementation on neuromorphic platforms and addresses computational overhead concerns.    
- Clear motivation: Argues convincingly for the urgency of robustness and adaptation in SNNs as they approach ANN-level accuracy.  
  
##  Weaknesses
- Initial lack of comparison to recent state-of-the-art ANN TTA methods (RoTTA, DeYO) which was later addressed in rebuttal.    
- Some theoretical aspects, such as formal robustness bounds, are not fully developed but partially supported by related literature (AugMix).    
- Limited discussion on the sensitivity of the method to augmentation strength and hyperparameters in the initial submission.    
- Some clarity issues regarding architectural details (e.g., definition of feature extractor vs. classifier, local vs. global feature maps) that were clarified post-review.    
  
## Discussion and Rebuttal Summary    
  
### Reviewers and Their Key Concerns:    
- **Reviewer hnfy:**    
  - Requested analysis of computational cost and clarification on practical deployment on neuromorphic hardware.    
  - Asked about impact of spike encoding (rate vs. temporal coding) and time steps on adaptation efficacy.    
  - Requested clarification on equation implementation details and motivation regarding SNN vs. ANN accuracy gap.  
  
- **Reviewer sQFP:**    
  - Suggested comparisons to additional state-of-the-art source-free domain adaptation methods (e.g., SHOT, RoTTA, TAST).    
  - Questioned the theoretical guarantees on robustness and augmentation sensitivity.    
  - Raised concerns about computational cost and energy efficiency in neuromorphic deployment.  
  
- **Reviewer t7Fe:**    
  - Requested statistical validation of results and further clarification on motivation for TTA over strong data augmentation at training.    
  - Asked about smoothing spike trains for temporal alignment.  
  
- **Reviewer 4hgi:**    
  - Requested empirical support for SNNs’ vulnerability to domain shift relative to ANNs.    
  - Noted initial lack of ANN baselines and clarity issues on the SPACE framework’s architectural components.    
  - Asked about similarity function choices and kernel function details.  
  
### Authors’ Responses:    
- Provided detailed computational cost analysis, demonstrating SPACE’s efficiency and feasibility on neuromorphic platforms.    
- Conducted new experiments analyzing spike encoding impacts and showing method’s robustness across different SNN time steps and architectures.    
- Clarified feature alignment implementation, theory, and similarity metrics, emphasizing stability and avoidance of overfitting.    
- Added comparisons to additional ANN TTA baselines (RoTTA, DeYO), showing SPACE’s superior performance and efficiency.    
- Performed Wilcoxon signed-rank tests confirming statistical significance of improvements.    
- Clarified architectural definitions of feature extractor and classifier, and local vs. global feature maps.    
- Addressed motivation concerns by showing recent SNN-ANN accuracy convergence and necessity of adaptation for real-world deployment.    
- Explained why smoothing spike trains leads to overfitting and why spike count alignment is more effective.    
- Maintained responsiveness to reviewer feedback; reviewer sQFP raised their score after rebuttal, and others acknowledged clarifications while retaining borderline stances.  
  
### Final Decision:    
The authors substantially addressed reviewers’ major concerns with additional experiments, theoretical clarifications, and broader empirical baselines. The method’s novelty, practical importance, and strong empirical results justify acceptance. Remaining minor clarity and motivation points are addressed sufficiently in rebuttal. I recommend acceptance of this paper for its novel, well-motivated, and empirically strong contribution to the test-time adaptation of spiking neural networks, an important step toward their robust real-world deployment.
Decision: Accept (poster)