PredNext: Explicit Cross-View Temporal Prediction for Unsupervised Learning in Spiking Neural Networks
Authors: Yiting Dong
Jianhao Ding
Zijie Xu
Tong Bu
Zhaofei Yu
Tiejun Huang
Venue: ICLR 2026 Poster (2026)
Keywords: Spiking Neural Network
Brain inspired
Neuromorphic computing
Unsupervised learning
Primary area: applications to neuroscience & cognitive science
TL;DR: Unsupervised Learning for Spiking Neural Networks via Cross-View Temporal Prediction

Abstract: Spiking Neural Networks (SNNs), with their temporal processing capabilities and biologically plausible dynamics, offer a natural platform for unsupervised representation learning. However, current unsupervised SNNs predominantly employ shallow architectures or localized plasticity rules, limiting their ability to model long-range temporal dependencies and maintain temporal feature consistency. This results in semantically unstable representations, thereby impeding the development of deep unsupervised SNNs for large-scale temporal video data. We propose PredNext, which explicitly models temporal relationships through cross-view future Step Prediction and Clip Prediction. This plug-and-play module seamlessly integrates with diverse self-supervised objectives. We firstly establish standard benchmarks for SNN self-supervised learning on UCF101, HMDB51, and MiniKinetics, which are substantially larger than conventional DVS datasets.  PredNext delivers significant performance improvements across different tasks and self-supervised methods. PredNext achieves performance comparable to ImageNet-pretrained supervised weights,  through unsupervised training solely on UCF101. Additional experiments demonstrate that PredNext, distinct from forced consistency constraints, substantially improves temporal feature consistency while enhancing network generalization capabilities. This work provides a effective  foundation for unsupervised deep SNNs on large-scale temporal video data.

Published as a conference paper at ICLR 2026
PREDNEXT: EXPLICIT CROSS-VIER TEMPORAL PRE- DICTION FOR UNSUPERVISED LEARNING IN SPIKING NEURAL NETWORKS
Jianhao Yiting Jianhao Ding Zijie Xu2.2.3, Tong Bu2.2.3, Zhaofei Yu3.2,3, Tiejun Huang1.2,3 School of Computer Science, Peking Universityl
State Key Laboratory of Multimedia Information Processing, Peking University
Institute for Artificial Intelligence, Peking University
(dongyi ting, 25063980788 putong30, yuzf12, jiuang) @pku edu.cn j pku. edu. en
ABSTRACT
Spiking Neural Networks (SNNs), with their temporal processing capabilities and biologically plausible dynamics, offer natural platform for unsupervised rep- resentation learning. However, current unsupervised SNNs predominantly em- ploy shallow architectures or localized plasticity rules, limiting their ability model long-range temporal dependencies and maintain temporal feature consis- tency. This results in semantically unstable representations, thereby impeding the development of deep unsupervised SNNs for large-scale temporal video data. We propose PredNext, which explicitly models temporal relationships through cross-view future Step Prediction and Clip Prediction. This plug-and-pla module seamlessly integrates with diverse self-supervised objectives. We firstly establish standard benchmarks for SNN self-supervised learning UCF101, HMDB51, and MiniKinetics, which are substantially larger than conventional DVS datasets. PredNext delivers significant performance improvements across different tasks and self-supervised methods. PredNext achieves performance comparable to ImageNet-pretrainee supervised weights, through unsupervised training solely UCF101. Additional experiments demonstrate that PredNext, distinct from forced consistency constraints, substantially improves temporal feature consistency while enhancing network generalization capabilities. This work provides a effective foundation for unsupervised deep SNNs on large-scale temporal video data.
INTRODUCTION
Low Consistency
High Consistency
SimSiam
80.0
simblntyy
78.0
www
SimSiam
PredNext
(SimSiam)
100
200
Epochs
(a)
(b)
Figure 1: Analysis of temporal consistency, (a) Evolution of inter-frame feature similarity during SNN training. (b) Distribution of video features in high-dimensional space, demonstrating more concentrated clustering for high-consistency temporal representations. Blue points represent fea- tures from different timesteps of the same video, while red points indicate cluster centers in nearby feature space locations. Green and red arrows denote intra-video feature attraction across frames and inter-video feature repulsion respectively.
Unsupervised learning has garnered significant attention in artificial intelligence for its capacity to extract meaningful representations from unlabeled data (Barlow, 1989; Bengio et al., 2012; Liu
"Corresonnding Author

---

Published as a conference paper at ICLR 2026
Current Sampled Clip
Next Sampled Clip
View
Croise Loss
Coerel Line
Cooine Line
Step Preduction Loss
Splt-Saprriied
Loo
Casine Lowe
+
View
Clip Prediction
Cip Predection Lues
Coolae Loss
Cosite Loss
Noxi Step
Prodiction
Projection
Projection
Projection
Projettin
Menioo: Potenial
Menbrare Precatial
SNN
SNN
SNN
SNN
SNN
B&WsC
BtWxC
H&WoC
H.WwCC
HeWWcC
HsWyC
Figure 2: PredNext algorithmic framework, PredNext incorporates Step Predi ciiion and Clip Prediction components for predicting features at the next step and in subsequent sam- pled clips from the same video, respectively. As an auxiliary module, PredNext can be seamlessly integrated into existing self-supervised learning methods. Red arrows indicate the Step Prediction pathway, while Blue arrows denote the Clip Prediction pathway.
al., 2021), substantially reducing dependence on extensive manual annotation. By revealing inher- ent structures and patterns in unlabeled data, this approach more accurately reflects natural human learning processes (Hinton Sejnowski, 1999; Chen et al., 2020; He et al., 2020). Spiking neural networks (SNNs), with their characteristics of simulating brain functioning principles (Maass, 1997; Diehl & Cook, 2015; Wu et al., 2018), constitute an ideal framework for unsupervised learning re- search (Gerstner & Kistler, 2002; Tavanaci et al., 2019). Nevertheless, current research unsuper- vised learning in SNNs has primarily concentrated on shallow architectures or synaptic plasticity- based methods (Dichl & Cook, 2015; Kheradpisheh et al., 2018; Dong et al., 2023). The challenges in extending these approaches to deep architectures, particularly when processing complex temporal data, predominantly arise from the limited capacity of current deep SNN models effectively cap- ture and leverage long-term temporal dependencies (Wu et al., 2018; Fang et al., 2021b). Efficient processing of large-scale, temporally rich data, especially video, essential for developing robust unsupervised learning systems capable of generating richer, more semantically meaningful feature representations for downstream applications.
The temporal processing capability of spiking neural networks stems from the intrinsic dynamics of spiking neurons, which serve as information carriers across timesteps (Zenke & Vogels, 2021; Neftci et al., 2019). Standard LIF neurons accumulate membrane potential to retain temporal information and emit discrete spikes when the potential exceeds threshold. However, this elementary integrate- and-fire mechanism proves inadequate for processing large-scale video data with complex temporal dependencies. Additionally, Unlike ANNs employing temporal downsampling (Tran et al., 2015; Carreira & Zisserman, 2017), SNNs typically preserve original temporal resolution, potentially sulting in feature instability without appropriate temporal aggregation. Consequently, we suggest that intrinsic neuronal dynamics alone are insufficient for complex temporal information process- ing, necessitating the integration of explicit temporal modeling mechanisms to enhance the temporal processing capabilities of SNNs.
Furthermore, we argue that effective temporal modeling should enhance consistency among features extracted across different timesteps. To illustrate this point, Figure I(a) illus- trates the evolution of feature consistency on (S et al., 2012) as training pro- gresses. The results demonstrate that as mod- els converge, semantic extraction capability im- proves significantly while feature distributions across timesteps become increasingly consistent. Ide- ally, as shown in Figure 1(b), high-connistency SNNs should extract stable high-level semantic fea- tures (action types, object categories) that remain invariant temporal fluctuations(Pan et al., 2021; Han et al., 2020b). While directly constraining temporal consistency might seem intuitive, however, experiments reveal that such enforced consistency constraints actually impair performance.
Table 1: Summary of commonly used DVS and video datasets. #dataset
#classes
#object #temporal #scale
DVS Guesture
L3K x 10s action
Real Scene Small
CIFARIO-DVS
10K 1.2s images
Camera Shift Small
N-Caltech101
9K x 0.3s images Camera Shift Small
UCF101
13K x 48 action Real Scene Medium
HMDB51 miniKineties
6.7K x 7s action Real Scene Medium
80K 10s action Real Scene Large
2

---

Published as a conference paper at ICLR 2026
Based the preceding analysis, we propose PredNext, that explicitly models temporal relationships and enhances feature consistency in unsupervised spiking neural networks by predicting future fea- tures across contrastive views. As illustrated in Figure 2, PredNext operates as plug-and-pla andg-aa module that seamlessly integrates with existing self-supervised learning algorithms. The framework comprises complementary mechanisms: Step Prediction, which predicts representations at sub- sequent timesteps, and Clip Prediction, which predicts features from future temporal clips, while cross-view prediction enhances feature discrimination. PredNext based on the hypothesis that by explicitly modeling temporal relationships both within and between clips, features with higher semantic density should better predict future representations while excluding low-level dynamic information, thus naturally improving cross-temporal feature consistency.
Due to the searcity of unsupervised methods for SNNs, we adapted established self-supervised ap- proaches to SNN architectures as benchmarks and reproduced some video unsupervised learning methods. We conducted experiments using UCF101 et al., 2012) and MoniKineticsCCarrelhl & Zisserman, 2017) for pre-training, which offer greater scale and richer temporal dependencies than conventional DVS datasets(Li et al., 2017; Orchard et al., 2015)(as shown Table 1). Re- sults demonstrate that PredNext yields significant performance gains across self-supervised methods while substantially enhancing temporal consistency of extracted features. Our empirical study con- firms that superior feature extraction capability corresponds higher temporal feature consistency, while forcibly imposing consistency constraints degrades performance. Furthermore, experiments show that SNNs, like ANNs, benefit from larger-scale datasets in video processing tasks.
2 METHODS
2.1
SELF-SUPERVISED LEARNING IN SNNS
grad
similarity
grad
amllrity dissinilarity
grad
grad
similarlty
grad
simbarity
grad
cross COTT
grad
prod
SNN encodar
SNN enooder
SNN emcoder
SNN encoder
SNN encoder
SNN arooder
SNN encodlrr
moment.um emoodar
moving average
SimSiam
SimCLR
BYOL
BarlowsTwins
Figure 3: Implementation for self-supervised learning in SNNs, encompassing SimCLR, MoCo, SimSiam, BYOL, BarlowTwins. Temporal features are aggregated following SNN encoder.
Given the absence of systematic investigations into self-supervised learning for deep spiking neu- ral networks, we first adapted prevailing self-supervised methods to SNN architectures to establish comparative baselines for proposed PredNext approach. As depicted in Figure 3, we imple- mented SNN variants of both contrastive methods (SmmCLR(Chen et al., 2020), MoCo(He et al., 2020), Barlow SendowTwins(Zoonaar et al., 2021)) and negative-sample-free approaches (SimSiam(Chen & He, 2021), BYOL(Grill et al., 2020)).
Formally, let E D denote a clip of length sampled from dataset D. Through data augmen- tation H (z), we obtain two views of and at.. These views, processed through feature extractors and MLP projection heads, yield representations of and 2/. Self supervuuer learning aims to mini- mize distances between representations from different views of the same sample while maximizing distances between representations from different samples. For SNNs, we follow convention by com- puting the time-averaged representation 2; Σi= =t/ /T the final feature. SimCLR and MoCo implementations utilize the InfoNCE loss function:
L -og log exp(sim(21, 2,)0mm((2,,,)777 2/)/T)
k= exp(sim =x)/))
Here, sim(-.) denotes cosine similarity, F represents the temperature parameter, and is the batch size. SimCLR utilizes in-batch samples negative examples, whereas MoCo maintains dynamic
3

---

Published as a conference paper at ICLR 2026
feature queue for negative samples with a momentum encoder. SimSiam and BYOL employ predictor network h that maps representations between views while minimizing their distance:
2j
h(2))
L ||
[1(22)]l2
where, BYOL employs momentum encoder for target network updates, while SimSiam utilizes a weight-shared siamese network with stop-graid graddgett operations to prevent collapse. BarlowTwins, conversely, minimizes feature redundancy using the following loss function:
-1 -x)
Σ
Σ C
where C denotes the cross-correlation matrix of halch-normalized features, and À is the hyperpa- rameter balancing these competing objectives.
Our SNN reproduction for self-supervised learning methods utilizes a SEW ResNet architecture (Fang et al., 2021a) for feature extraction. Across all experiments, we employ the AdamW optimizer (initial learning rate: 2e-3, weight decay: le-4) with cosine annealing scheduling and a batch size of 6 256. For UCFI01 and HMDB51, extracted 7 = 16 frames with a stride of T = 2; for MiniKinetics, 114 x 114 crops with 120 epochs. We extract T 8 frames with a stride of t 8. Data augmentation follows protocols established in Feichtenhofer et al. (2021). Validation employs clips per video for inference. Comprehensive architectural and hyperparameter details are provided in the appendix.
use 128 128 crops with 200 training epochs, with
Algorithm PredNext Training Procedure
Require: Dataset data augmentation function H, feature extractor and projection head F, tem- poral prediction head Pr, Pc, self-supervised loss function Lisst, weight coefficient a
Ensure: Trained feature extractor F
1: for each mini-batch do
2: // Get features from two augmented views
3:
H(x), x2 H(r)
4:
of = F = ((a)) 1...=
5:
I/ Compute original supervipd loss
6:
Leat = self-supervised loss based on and
7:
I/ Compute PredNext predicted features
8:
p = Pr(zt),p² = Pr(2)) for = 1...T
9:
G = Po(z)).; = Po(zz)
// Compute PredNext loss
11:
Lpred = 0.25 -E (Q(p), 2ftm) + 2(pf.2ttm)) =)+")) + M(c,, + M M(G,, 2f))
I/ Compute total loss and update parameters
13:
I.= (1 ---) a) Teaat + Ixpred
Update parameters of F and Pr, Pc to minimize
15: end for
2.2 PREDNEXT
PredNext serves as plug-and-play auxiliary module seamlessly integrable with diverse self- supervised learning frameworks. As depicted in Figure 2, it introduces temporal prediction as an auxiliary objective while preserving the original self-supervised paradigm. Inspired by Predictive Coding theory(Huang & Rao, 2011; Spratling, 2017). PredNext explicitly models temporal rela- tionships through future representation prediction. This approach operates on the principle that semantically rich features should accurately predict their next semantical feature, whereas features capturing only low-level dynamics cannot generate effective predictions.
PredNext comprises three main components: SNN feature extractor and nonlinear MLP pro- jection head (jointly denoted as F), alongside two temporal prediction heads (Py and Pc) for next- timestep and next-clip predictions. The Step Predictor Pr establishes mappings between current and future timestep features, while the Clip Predictor Pa models relationships between current and future clip representations. Both predictors employ two-layer ML.Ps with dimensions matching the

---

Published as a conference paper at ICLR 2026
projection head output. For augmented clips xt and æj., obtain representations 2j F(x)) and =j F(x))) that serve both the original self-supervised objective and generating predictions through 5 - Pr((P()))) yr = (P and e; = Po(+E. F(a;)), e = Pc E, (x))))
Step Predictor's loss function minimizes the divergence between current features and cross-view future features:
pl
Q =)+m)
where denotes the prediction time step interval. While Clip Predictor's loss function is defined as:
M (ci, 25) C4
(5)
Where 2/ and 23 denote temporally aggregated features of the subsequently sampled clip. To en- hance learning effectiveness, we employ a symmetric design, with the final loss function:
Iepred E (G00 200
M M(s, 23) " M))
1
(6)
We employ cross-view prediction where features from one view (p/, ai) predict future features of another view (z,tm, 2)), with stop-gradient applied to the target features. This design enhances feature discrimination by requiring the model to disregard view-specific noise. Our ablation studies comparing same-view prediction (pī predicting 2; =" against cross-view prediction demonstrate that the latter yields superior generalization performance. PredNext's complete training procedure outlined in Algorithm The final optimization objective combines both learning targets:
L (1 a)) Leet + a Lpred
Where weight coefficient balances their relative importance.
Base settings: As PredNext is model-agnostic and functions as slug-and-play component across methods, we standardized its parameters throughout experiments. Following SimSiam (Chen & He, 2021), the temporal prediction head Pr and Pe comprises a 2-layer MLP with batch normal- ization, using 128-dimensional hidden layer while maintaining output dimensions consistent with F(z)'s feature representation.
Comparison with Predictive Coding Methods:
Table 2: Summary of commonly used DVS and video datasets.
Predictive coding approaches have attracted consid- erable research interest, particularly for temporal data processing. DPC/MemDPC(Han et al., 2019; 2020a) implement dense predictions on video se- quences and utilize dedicated temporal aggregator networks to process intermediate temporal variables. Lorre et al. (Lorr et al., 2020) developed a CPC-like approach for future timestep feature prediction. As shown Table 2, in contrast, PredNext employs cross-view prediction with more streamlined architecture that eliminates the need for complex auxiliary structures, functioning as a modular component integrable with existing methodologies.
no additional
methods
step pred clip pred
module needed
DPC
X
memDPC
X
CPC-tike(Lome's
X
PreedNext
3 EXPERIMENTS
3.1 DATASET AND IMPLEMENTATION
Datasets details In contrast to traditional DVS datasets, unsupervised learning paradigms neces- sitate large-scale datasets to extract meaningful representations. UCF101(Soomrrg al., 2012) and HMDD551(Kuhh)) et al., 2011) are medium-scall video benchmarks widely adopted in action recog- nition research. UCF101 encompasses 13, 320 video clips across 101 action classes, while HMDB51 contains 6, 766 clips with 51 classes. miniKinetics(Careee common subset of Kinetics-400, includes 200 classes with about 400 training and 25 validation in- stances per class, maintaining diversity and complexity while reducing computational requirements.
& Zisserman, 2017; Xie et al., 2018), an
Implementation details To ensure experimental rigor and comparative validity, we maintain con- figurations of PredNext aligned with established baselines. We employ SEW ResNet (Fang al.,
5

---

Published as a conference paper at ICLR 2026
Table 3: Comparative results after fine-tuning under different self-supervised methods. Top- I and Top-5 accuracies are reported. Models were trained using various pre-training datasets and evaluated on different fine-tuning datasets. * indicates results reproduced according to our setup.
finetune datasets Initial weights
ucf101
hmdb51
miniKinetics
method
top-1
top-5
top-1
top-5
top-1
top-5
Supervised
random init
44.07
70.84
18.04
45.69
40.53
68.59
Supervised
ImageNet init
64.42
87.36
34.31
67.84
50.48
76.53
Supervised
ImageNet mini Kinetics init
70.02
91.62
44.97
78.37
pre-train
ucf101
miniKineties hmdh51
finetune
ucf101
hmdb51
ucf101
miniKineties
method (Initial weights)
top-1
top-5
top-1
top-5
top-/
top-5
top-1
top-5
top-1
top-5
SimCLR
57.04
83.82
30.59
64.97
59.03
85.96
35.42
67.97
50.61
77.16
MoCo
49.70
79.70
28.04
62.22
45.63
76.55
20.72
46.86
42.65
70.23
BYOL
56.41
83.18
29.35
64.58
59.27
86.23
36.74
68.24
51.23
77.69
BarlowTwins
56.15
84.25
30.33
64.12
58.04
85.83
36.53
68.17
51.28
77.61
SimSiam
50.81
81.07
28.10
63.46
43.77
74.89
19.08
45.75
41.52
69.75
SimSiam (ImageNet)
70.32
91.56
39.65
74.35
68.70
91.91
36.67
73.53
/SimSiam(p = 1)
52.05
81.75*
28.56*
64.30*
SVRL(SimSiam-basel
52,81
82.15*
29.22*
64.38*
PredNextsimcc..e
59.47
85.28
31.58
66.19
61.06
87.21
36.80
68.37
53.61
78.59
PredNextsM.Co
54.98
82.87
29.60
64.31
51.60
79.65
25.69
51.37
46.51
73.64
PredNextay
58.58
83,82
31.57
64.51
62.01
88.26
37.25
69.28
54.37
79.61
PredNext Barlow/T wins
59.76
84.85
31.18
66.01
62.75
88.66
37.65
69.35
54.68
79.85
PredNextying Sham
54.93
82.77
30.00
64.37
50.65
79.01
25.03
51.04
46.31
73.68
PredNext PredNextggmnSSom Sim.Stem
72.24
91.81
41.50
75.42
71.66
92.07 38.63
74.25
(emageNet)
2021a) as the feature extraction backbone across all experimental conditions. For UCF101 and HMDB51, we crop video frames at 128 x 128 resolution, sampling 16 frames with a stride of 2. MiniKinetics processing utilizes 112 x 112 resolution with 8 frames with stride of 8. During evaluation, we perform inference on 3 uniformly sampled clips per test video. Optimizer hyper- parameters remain consistent with baseline model configurations. More experimental parameters details are included in the appendix. While optical flow typically enhances performance in video understanding tasks(Han et al., 2020b; Carreira & Zisserman, 2017), we exclude this modality as our investigation primarily focuses on temporal feature consistency in SNNs under unsupervised learning paradigms, and we reserve multimodal integration for subsequent research endeavors.
3.2 RESULTS OF UNSUPERVISED REPRESENTATION EVALUATION
We first evaluated the performance of various self-supervised learning methods in baseline spiking neural network implementations, then incorporating PredNext as an auxiliary module to quantify performance enhancements. Following the experimental protocol established in (Han et al., 2019), we utilized UCF101 and MiniKinetics as pre-training datasets and report performance after fine- tuning on different target datasets.
Table presents performance across pretraining and fine-tuning configurations. Even basic SNN self-supervised methods achieve substantial results on action recognition tasks. PredNext consis- tently yields significant improvements across all methods, demonstrating its effectiveness in en- hancing temporal representation learning. Notably, PredNext achieves performance comparable to pretrained supervised weights, through unsupervised training solely on UCF101. More- over, models trained on larger pretraining datasets consistently show superior performance, confirm- ing that SNNs, like ANNs, benefit significantly from data scale (without MoCo, SimSiam). Interest- ingly, even trained with same datasets, unsupervised models outperformed those trained with super- vision (SimSiam on UCF (CO), highlighting the research significance of video unsupervised learning in providing stronger generalization. Furthermore, larger datasets provide more effective parameter initialization- modelss initialized with ImageNet weights and pre-trained solely UCF101 achieve performance SSimSiam(ImageNe on UCF101) comparable to supervised learning on MiniKinetics.
ImageNet- ImageNet-pretrained
We observe that SimSiam and MoCo exhibit relatively lower performance compared to the other three methods. We attribute this to the following reasons: SimSiam lacks negative samples com- pared other approaches, leading to relatively unstable training, whereas B BOL enhances stability

---

Published as a conference paper at ICLR 2026
SimSiam
BYD1
SimCUR
MoCo
Bar low Twins
80.0
78.0
78.5
76.0
75.0
SimSaa
76.0
76.0
Barlow/Raiie
Prod
74.3
74.0
Picdhost
(Sim5aml
PredMMet//troo)
Pr
Predhwatt/Waca))
[farrior Twinns)
72.0
100
200
72.0
100
200
72.0
100 Epochs
200
72.0
150 Epochs
200
72.0
100
200
Epochs
Epachs
Epochs
100
13-
7.5
75
7.55
50-
5.0
60-
-25-
-22
-25
-15-
-3.5
-5.0
-200
92500
-100
-18.0
-3240
64.00 7338.
15-
160
190
7.5
18.0
7.5
50-
25
60-
3.0
-50
-72
-22
-25
-300
"10.//55022002 23300
-300 218.885522. 2l000
-00
-224
-38 Sedion 75755
Figure 4: Analysis of temporal feature visualization. Top row: evolution of temporal consistency error during training across methods. Middle and bottom rows: UMAP visualizations of video features from baseline self-supervised methods and their PredNext-enhanced variants, respectively.
through momentum encoder. On the other hand, MoCo requires maintaining memory bank as a negative sample repository, which proves challenging for datasets like UCF101 to sustain a large and consistent bank for effective training.
3.3 CONSISTENCY CURVES AND MANIFOLD
To examine PredNext's influence on SNN temporal feature representations, analyzed feature consistency across methods. Figure illustrates the evolution of feature consistency during training. We define feature consistency error the average cosine distance between representations from different time steps of the same video:
N
T
T
1 Econsistency N N
(1- (1-cos ff.
(8)
1= t= a= 1,g6t
where J represents video i's feature at time 1, N denotes the sample count, and T in- dicates time steps per video. Lower values indicate lower temporal feature consistency.
Consistency Visualization As Figure 4(top row) demon- strates, consistency errors decrease during training across all methods, indi- cating progressive learning of stable temporal features before eventual saturation or deterioration. Methods incorporating PredNext maintain comparable early- stage convergence rates to baselines but avoid the post-saturation decline, ultimately achieving significantly lower consistency errors. This confirms hypothesis that explicit temporal prediction modeling guides networks toward semantically richer, temporally consistent representations.
Table 4: Comparative results of forced consistency constraint ex- periments. 3 denotes constraint intensity; error represents temporal feature consistency deviation.
SimSiam SimSiam PredNest
UCF101
Forced Consistency
(ImageNet) (ImageNet)
B
0.1
0.5
0.8
top-1 consistency 0.773
70.32
72.24
70.45-o.13
65.69
60.35
0.819+0.018
0.803 0.852 0.884+0.11
To further visualize learned representations, sionality reduction on test set samples, as shown in Figure 4 (middle and bottom rows). Original self-supervised methods generate temporally dispersed features, with representations from different time steps often widely separated. In contrast, BeedNext-enhameee methods significantly improve feature clustering, with same-video feature points exhibiting substantially tighter grouping.
applied UMAP (McInnes et al., 2018) for dimen-

---

Published as a conference paper at ICLR 2026
Table 5: Video retrieval performance comparison. R@ 5, 10, 20 denote recall rates corre-
sponding rank thresholds. Evaluations performed on UCFI01 and HMDB51 datasets, All models pretrained on UCF101 split 1.
UCF101 pretrain
UCF101
HMDB51
methods
R R@1
R@5
R@ 10
R@ 20
R @
R@5
R@ 10
R@ 20
SimCLR
34.58
55.72
65.50
74.70
12.22
34.71
49.67
64.71
SimCLR SimCLRpredNest eat
37.09
56.01
66.38
75.20
13.60
35.36
50.32
66.86
SimSiam
27.84
48.53
59.79
71.56
11.70
32.68
45.95
60.98
SimSiam PredNent
36.27
55.70
65.13
74.15
13.20
35.16
47.32
64.05
SimSiampredNNer 53.19
69.39
76.53
83.11
15.95
40.46
53.53
68.43
(ImageNet)
Forced Consistency Constraints Furthermore, we conducted control experiment with forced con- sisteney constraints by directly adding an explicit constraint to loss function, compelling feature similarity across different time steps of the same video:
forced = Lost + B- Er,,, cos( ff)]
(9)
This approach diverges from PredNext by eliminating prediction heads and prediction processing. As shown in Table 4, this direct constraint indeed rapidly reduces consistency errors, even faster than PredNext. However, analysis of the relationship between feature consistency and downstream task performance reveals that despite generating more consistent features, forced constraints yield inferior fine-tuning performance compared to PredNext's representations.
Therefore, these findings demonstrate that superior feature extraction capability corresponds with higher temporal feature consistency and stability. However, simply enforcing consistency through constraints does not necessarily lead to better feature extraction capabilities. High-quality features capture semantic information in videos (such action types, object categories), which should natu- rally remain relatively stable over time periods. Forced consistency constraints potentially suppress critical temporal dynamics, yielding oversimplified representations with low discriminative capacity.
3.4
VIDEO RETRIEVAL
Query
Neareet
Figure 5: Visualization of retrieval results. Query videos (in left) with corresponding Top-3 re- trieval results. Results for three query samples shown, with one sample per row.
Retrieval Results To further evaluate the semantic representation capabilities, we conducted video retrieval evaluations following (Han et al., 2019). Using UCF101's split validation set as queries and the corresponding training split as retrieval candidates, we uniformly sampled 10 frames per video and extracted temporally aggregated features from pretrained models. The retrieval process employed Nearest Neighbor(NN) search. we identified the K closest videos to each query and cal- culated category matching performance (Recall K). Table 5 presents video retrieval performance across self-supervised methods using Recall @ 1.5, 10.20 metrics. Results demonstrate that PredNext integration yields significant improvements across all retrieval benchmarks, confirming its capacity to facilitate more precise semantic representations.
NN Visualization Figure provides visualization examples retrieval from PredNext's features. Query (Figure 5 (left)) videos with their corresponding Top-3 retrieval results (Figure 5 (right)) illustrate that PredNext can retrieve semantically consistent videos despite significant visual varia- tions in varied camera angles, player appearances, and visual contexts.
8

---

Published as a conference paper at ICLR 2026
56
Performance
Performancs
Performance
54
52
52
30
52
50-
trames-16
frames-10
StanSiam
p01
50
48
128
512
1024
Figure 6: (a) Impact of prediction stop length on model performance. (b) Influence of prediction head hidden layer dimensionality model efficacy. (c) Effects of temporal length and sampling rate on model performance.
Table 6: Ablation studies. Performance comparison following removal of step prediction and clip prediction components. Experiments conducted SimSiam and SimCLR. All models pretrained on UCF101 splitl.
SimSiam
SimCLR
step prediction
clip prediction
ucf101
hmdb51
ucf101
hmdb51
top-1
top-5
top-1
top-5
top-1
top-5
top-1
top-5
x
50.81
81.07
28.10
63.46
57.04
83.82
30.59
64.97
x
51.33
81.26
28.76
63.73
57.86
84.14
30.65
65.03
/
54.40
82.37
29.54
64.12
59.21
84.96
31.18
66.01
54.93
82.77
30.00
64.37
59.48
85.28
31.57
66.34
ABLATION STUDIES
Impact of Prediction Head Pr. Pe Table illustrates the impact of Prediction Heads Pr and Pe on model performance. Both prediction components independently enhance performance, while their combination in PredNext yields further improvements. Clip prediction demonstrates more substantial effects than step prediction, which we attribute its coverage of temporal information across a longer time range, facilitating acquisition of richer temporal representations.
Impact of Prediction Step Length Prediction step length determines the temporal distance for feature prediction. Figure 6(a) illustrates performance across varying step lengths. Optimal perfor- mance typically occurs at step length 1, with declining performance at longer intervals. We analyze that when m > 1, adjacent timesteps lose the ability to interact for prediction, as larger m val- ues cause the model to skip nearby temporal moments, resulting significantly sparser predictive interactions compared to = 1 and consequently leading to performance degradation.
Impact of Cross-view Prediction Table 7 compares four prediction strategies: cross-view predic- tion, sime-view prediction, and their standalone implementations without original self-supervised objectives. Cross-view prediction consistently outperforms alternatives across all methods. By pre- dicting features across different augmentations, models must isolate semantically meaningful fea- tures, while sime-view-only prediction leads to representation collapse.
Impact of Prediction Head Size Figure 6 (b) illustrates how prediction head Pr, Pc hidden dimensionality affects model performance. Testing dimensions from 64 1024 reveals that performance improves with in- creasing dimensionality but stabilizes beyond 256 dimensions. This in- dicates that the prediction head re- quires sufficient representational capacity for effective temporal modeling but becomes parameter- inefficient beyond certain thresholds. We selected 512 dimensions as the optimal onfiguration, balancing performance with computational efficiency. Notably, the prediction head introduces min- imal additional parameters compared to the feature extraction backbone and is utilized exclusively during training, introducing computational overhead during inference.
Table 7: Comparative results between same-view and cross-view prediction. "only" indicates training without original self-supervised objectives. objeetttsssdd.jcciivvvs....
dataset cross-view same-view cross-view same-view
only
only
UCF101
54.93
53.66-1.27
52.37 -2.6
5.03_ 49.90
HMDB51
30.00
29.67 0.33 29.41
0.59
3.07 26.90
Impact of Time Lengths and Sampling Stride Figure 6 (c) illustrates how clip length and sam- pling stride influence model performance. Evaluating combinations of sequence lengths (10, 16 frames) and sampling intervals (1, 2, 4) reveals consistent performance improvements with both increased sequence length and wider sampling intervals. This pattern suggests that sequences span-

---

Published as a conference paper at ICLR 2026
Table 9: Comparison with other SNNANN methods on UCF101. denotes stronger data aug- mentation. pretrain indicates whether ImageNet pretraining is used. Due to varying model capac- ities, the effectiveness of ImageNet pretrained weights differs. ANN indicates ANN-based models. f indicates results reported by unofficial split and size.
prefrain Acc in ImageNet
method
Un-/Sup
model
pretrain
Top1
Top5
vanilla
supervised
ResNet I8(ANN)
40.7
63.8
vanilla
supervised*
ResNet I8(ANN)
X
53.2
78.3
vanilla
supervised*
ResNet 34(ANN)
X
54.2
77.4
vanilla
supervised*
ResNet 50(ANN)
54.3
77.5
ResNet I8(ANN)
ReSpike (Xiao et al. (2025))
supervised
73.2
77.5
93.9
+MS-ResNet18
Formmersst (Yu et al. (2024))
supervised*
SVFormer-st
82.9
80.2
LSM+STDP
hand-crafted
ASM-16.2M
70.21
(Panda & Srinivasa (2018))
+supervised
STS ResNet
supervised
STS ResNet
X
42.1
(Samadzadeh et al. (2023))
SimSiam
unsupervised
ResNet I8(ANN)
X
49.3
78.6
SimSiam PredNest
unsupervised
SEW ResNet18
X
54.9
82.8
SimCLR PredNezt
unsupervised
SEW ResNet18
59.5
85.3
SimSiam PredNent
unsupervised
SEW ResNet18
63.2
72.2
91.8
SimSiam SimSiamPredNNer PredNent
unsupervised SEW ResNet34
67.0
74.1
93.1
SimSiam
PredNext SinSiamPreeNNest
unsupervised SEW ResNet50
67.8
74.2
93.1
ning broader temporal ranges provide richer contextual information, enabling more comprehensive semantic understanding of actions.
Impact of weighting coef- ficient Since our method jointly optimizes the original self-supervised loss and pre- diction loss, we investigate impact of varying weight- ing coefficients Table 8 presents the performance of PredNext on UCF101 and HMDB51 under different values. Results shows increasing the pre- diction loss weight(( = 0.5) yields significant performance improvements, excessively high predic- tion weights(a 0.8), result in performance degradation, ultimately converging the cross-view only setting at a = 1. Complete reliance on the prediction task may overlook important infor- mation from the original self-supervised task. Given that performance across all experiments, we adopt this value as the default setting throughout our study.
Table 8: Comparative results between different weight coeffi- cient a, where = 0 corresponds to the original SSL method and equals the cross-view only setting.
a
methods
dataset
0.2
0.4
0.5
0.6
0.8
1.0
SimSiam Pred&eart UCF101
50.8 52.4 53.4
54.9
53.8
52,2
52.4
SimCLR ProdNent UCF101
57.0 57.4 57.9
59.5
58.6
55.5 52.4
SimSiam
PrenniiMPeeeNestt ProdNest HMDB51
28.1 28.3 28.9 30.0 29.4 29.5 29.4
0.5 consistently exhibits superior
Comparison with other SNN/ANN methods We compare PredNext with other SNN/ANN meth- ods reporting results on UCF101. Table presents the performance of different methods. We ob- serve that model performance correlates significantly with pretrained weight effectiveness. Without ImageNet pretraining, PredNext even outperforms ANN-based supervised baselines (we find that weak augmentation causes ANN collapse on UCF101, thus we employ stronger augmentation than reported basic setting). With ImageNet pretraining, PredNext performs lower compared to meth- ods with larger parameters and ANN supervision, which we contribute to SNN pretrained weights achieving lower performance (63.2% vs. 73.2%). Meanwhile, PredNext performance scales with model size, improving from SEW-ResNet18 to ResNet34. However, on SEW- ResNNe5O, marginal pretrained weight quality differences prevent further leveraging parameter scale advantages. No- tably, PredNext without pretraining weight surpasses self-supervised ANN methods with identical architecture (ResNet-18), demonstrating advantages in SNN self-supervised learning performance.
5 CONCLUSION
We present PredNext, a method enhancing unsupervised spiking neural networks through future feature prediction that strengthens temporal consistency. Experimental evidence demonstrates that PredNext delivers significant performance improvements over unsupervised SNN methods while substantially enhancing temporal coherence in network representations.
10

---

Published as a conference paper at ICLR 2026
ETHICS STATEMENT
Our paper does not involve any ethical issues. Our methods and experiments adhere to academic ethical standards without involving any sensitive data or privacy concerns.
REPRODUCIBILITY STATEMENT
We provide detailed experimental settings and hyperparameter configurations in the appendix to ensure that other researchers can reproduce our results. We plan publicly release our code and pretrained models to facilitate further research and applications within the community.
ACKNOWLEDGMENTS
This work is supported by the National Natural Science Foundation of China (62422601), Bei- jing Municipal Science and Technology Program (Z241 100004224444))) Beijing Nova Program (20240484703), National Key Laboratory for Multimedia Information Processing, and Beijing Key Laboratory of Brain orsin-inspired Spiking Large Models.
REFERENCES
Unaiza Ahsan, Rishi Madhok, and Irfan Essa. Video jigsaw: Unsupervised learning spatiotem- poral context for video action recognition. In 20/9 IEEE Winter Conference Applications of Computer Vision (WACV), pp. 179-189. IEEE, 2019.
Yeganch Bahariasl and Saeed Reza Kheradpishch. Self-supervised contrastive learning in spiking neural networks. In 2024 13th Iranian/3rd International Machine Vision and Image Processing Conference (MVIP), pp. 1-5. IEEE, 2024.
Horace B Barlow. Unsupervised learning. Neural computation, 1(3):295-311, 1989.
Yoshua Bengio, Aaron Courville, and Pascal Vincent. Unsupervised feature learning and deep learning: A review and new perspectives. CoRR, abs/1206.5538, (26655)2002, 2012.
Guo-qiang Bi and Mu-ming Poo. Synaptic modifications in cultured hippocampal neurons; depen- dence on spike timing, synaptic strength, and postsynaptic cell type. Journal of neuroscience, 18 (24): 10444-00444,,, 1998.
Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinctics dataset. In proceedings the IEEE Conference Computer Vision and Pattern Recognition, pp. 6299-6308, 2017.
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pp. 1597-1607. PmLR, 2020.
Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 15750-15758, 2021.
Jinwoo Choi, Chen Gao, Joseph CE Messou, and Jia-Bin Huang. Why can't dance in the mall?
learning to mitigate scene bias in action recognition. Advances in Neural Information Processing Systems, 32, 2019.
Peter U Diehl and Matthew Cook. Unsupervised learning of digit recognition using dependent plasticity. Frontiers in computational neuroscience, 9:99, 2015.
Yiting Dong, Dongcheng Zhao, Yang Li, and Yi Zeng. An unsupervised based spiking neural network inspired by biologically plausible learning rules and connections. Neural Networks, 165: 799-808, 2023.
11

---

Published as a conference paper at ICLR 2026
Yiting Dong, Dongcheng Zhao, and Yi Zeng. Temporal knowledge sharing enable spiking neural network learning from past and future. IEEE Transactions on Artificial Intelligence, 5(7):3524- 3534, 2024.
Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothée Masquelier, and Yonghong Tian. Deep residual learning in spiking neural networks. Advances in Neural Information Processing Systems, 34:21056-21069 2021a.
Wei Fang, Zhaofei Yu, Yanqi Chen, Timothée Masquelier, Tiejun Huang, and Yonghong Tian. In- corporating learnable membrane time constant to enhance learning of spiking neural networks. In Proceedings of the IEEE/CVF international conference computer vision, pp. 2661-2671, 2021b.
Christoph Feichtenhofer, Haoqi Fan, Bo Xiong, Ross Girshick, and Kaiming He. A large-scale study on unsupervised spatiotemporal representation learning. In Proceedings of the IEEF/CVF conference on computer vision and pattern recognition, pp. 3299-33999 2021.
Wulfram Gerstner and Werner M Kistler. Spiking neuron models: Single neurons, populations, plasticity. Cambridge university press, 2002.
Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzynska, Susanne West- phat, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, et al. The" something something" video database for learning and evaluating visual common sense. In Proceedings of the IEEE international conference on computer vision, pp. 5842-5850, 2017.
Jean-Ba Clan-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271-21284 2020.
Jesse Hagenaars, Federico Paredes- Vallés, and Guido De Croon. Self-supervised learning of event- based optical flow with spiking neural networks. Advances in Neural Information Processing Systems, 34:71 14779977 2021.
Tengda Han, Weidi Xie, and Andrew Zisserman. Video representation learning by dense predictive coding. In Proceedings of the IEEE/CVF international conference on computer vision workshops, pp. 0-0, 2019.
Tengda Han, Weidi Xie, and Andrew Zisserman. Memory-augmented dense predictive coding for video representation learning. In European conference on computer vision, pp. 312-329, Springer, 2020a.
Tengda Han, Weidi Xie, and Andrew Zisserman. Self-supervised co-training for video representa- tion learning. Advances in neural information processing systems, 33:5679-5690, 2020b.
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and patlern recognition, pp. 9729-9738, 2020.
Geoffrey Hinton and Terrence J Sejnowski. Unsupervised learning: foundations of neural compu- fation. MIT press, 1999.
Yanping Huang and Rajesh PN Rao. Predictive coding. Wiley Interdisciplinary Reviews: Cognitive Science, 255):580-593, 2011.
Saeed Reza Kheradpisheh, Mohammad Ganjtabesh, Simon Thorpe, and Timothée Masquelier. Stdp-based spiking deep convolutional neural networks for object recognition. Neural Networks, 99:56-67, 2018.
Hildegard Kuehne, Hueihan Jhuang, Estíbaliz Garrote, Tomaso Poggio, and Thomas Serre. Hmdb: large video database for human motion recognition. In 201/ International conference on computer vision, pp. 2556-2563. IEEE, 201
12

---

Published as a conference paper at ICLR 2026
Hongmin Li, Hanchao Liu, Xiangyang Ji, Guoqi Li, and Luping Shi. Cifarl0-dvs: event-stream dataset for object classification. Frontiers in neuroscience, 11:244131, 2017.
Tianlong Li, Wenhao Liu, Changze Lv, Yufei Gu, Jianhan Xu, Cenyuan Zhang, Muling Wu, Xiao- qing Zheng, and Xuanjing Huang. Spikeclip: A contrastive language-image pretrained spiking neural network. arXiv preprint arXiv:2310.066
2023.
Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, and Jie Tang. Self- supervised learning: Generative or contrastive. IEEE transactions on knowledge and data engi- neering, 35()):857887, 2021.
Guillaume Lorre, Jaonary Rabarisoa, Astrid Orcesi, Samia Ainouz, and Stephane Canu. Temporal contrastive pretraining for video action recognition. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp. 662-670, 2020.
Yuqi Ma, Huamin Wang, Hangchi Shen, Xuemei Chen, Shukai Duan, and Shiping Wen. Neuro- moco: neuromorphic momentum contrast learning method for spiking neural networks. Applied Intelligence, 55(2):97, 2025.
Wolfgang Maass. Networks of spiking neurons: the third generation of neural network models. Neural networks, 10(9):1659-1671, 1997.
Leland Mclnnes, John Healy, and James Melville. Umap:
Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:180..03426, 2018.
Emre O Nefiei, Hesham Mostafa, and Friedemann Zenke. Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks.
IEEE Signal Processing Magazine, 36(6):51 11-66,,1-63,. 2019.
Garrick Orchard, Ajinkya Jayawant, Gregory K Cohen, and Nitish Thakor. Converting static image
datasets to spiking neuromorphic datasets using saccades. Frontiers in neuroscience, 9:437, 2015.
Alexander G Ororbia. Contrastive signal-dependent plasticity: Self-supervised learning in spiking neural circuits. Science Advances, 10(43)eeadn6076. 2024.
Tian Pan, Yibing Song, Tianyu Yang, Wenhao Jiang, and Wei Liu. Videomoco: Contrastive video representation learning with temporally adversarial examples. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1205-11214, 2021.
Priyadarshini Panda and Narayan Srinivasa. Learning to recognize actions from limited training examples using a recurrent spiking neural model. Frontiers in neuroscience, 12:126, 2018
Kaushik Roy, Akhilesh Jaiswal, and Priyadarshini Panda. Towards spike-based machine intelligence with neuromorphic computing. Nature, 575(7784):077-617, 2019.
Daniel Ruderman and William Bialek. Statisties of natural images: Scaling in the woods. Advances in neural information processing systems, 6, 1993.
Ali Samadzadeh, Fatemeh Sadat Tabatabaei Far, Ali Javadi, Ahmad Nickabadi, and Morteza Haghir Chehreghani. Convolutional spiking neural networks for spatio-temporal feature extraction. Neu- ral Processing Letters, 55(6):6979 (999969955500 2023.
Daniel J Saunders, Devdhar Patel, Hananel Hazan, Hava T Siegelmann, and Robert Kozma. Locally connected spiking neural networks for unsupervised feature learning. Neural Networks, 119: 2019.
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. Ucf101: dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv: 1212.0402, 2012.
Michael W Spratling. A review of predictive coding algorithms. Brain and cognition, 1 112:92-97, 2017.
Amirhossein Tavanaei, Masoud Ghodrati, Saeed Reza Kheradpisheh, Timothée Masquelier, and Anthony Maida. Deep learning in spiking neural networks. Neural networks, 111:47-63, 2019.
13

---

Published as a conference paper at ICLR 2026
Graham W Taylor, Rob Fergus, Yann LeCun, and Christoph Bregler. Convolutional learning of spatio-temporal features. European conference on computer vision, pp. 140-153. Springer, 2010.
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri. Learning spa- tiotemporal features with 3d convolutional networks. In Proceedings of the IEEE international conference on computer vision, pp. 4489 4497, 2015.
Rui Wang, Dongdong Chen, Zuxuan Wu, Yinpeng Chen, Xiyang Dai, Mengchen Liu, Yu- Yu-Gang Jiang, Luowei Zhou, and Lu Yuan. Bevt: Bert pretraining of video transformers. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 14733-14743, 2022.
Chen Wei, Haoqi Fan, Saining Xie, Chao-Yual Yuan Wu, Alan Yuille, and Christoph Feichten- hofer. Masked feature prediction for self-superviser visual pre-training. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 14668-14678, 2022.
Laurenz Wiskott and Terrence Sejnowski. Slow feature analysis: Unsupervised learning of invari-
ances. Neural computation, 14(4):715-770, 2002.
Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, and Luping Shi. Spatio-temporal backpropagation for
training high-performance spiking neural networks. Frontiers in neuroscience, 12:331, 2018.
Shiting Xiao, Yuhang Li. Youngeun Kim, Donghyun Lee, and Priyadarshini Panda. Respike: resid- ual frames-based hybrid spiking neural networks for efficient action recognition. Neuromorphic Computing and Engineering, 5(1) 40099, 2025.
Saining Xie, Chen Sun, Jonathan Huang, Zhuowen Tu, and Kevin Murphy, Rethinking spatiotem- poral feature learning: Speed-accuracy trade-offs in video classification. In Proceedings of the European conference on computer vision (ECCV), pp. 305-321, 2018.
Liutao Yu, Liwei Huang, Chenlin Zhou, Han Zhang, Zhengyu Ma, Huihui Zhou, and Yonghong Tian. Svformer: direct training spiking transformer for efficient video action recognition. In International Workshop on Human Brain and Artificial Intelligence, pp. 161-180. Springer, 2024.
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny. Barlow twins: Self-supervieed learning via redundancy reduction. In International conference on machine learning, pp. 12310- 12320. PMLR, 2021.
Friedemann Zenke and Tim P Vogels. The remarkable robustness of surrogate gradient learning for instilling complex function spiking neural networks. Neural computation, 33(4):899-925, 2021.
14

---

Published as a conference paper at ICLR 2026
A LLM USAGE
In this paper, we restricted the use of LLMS solely for language refinement, without employing these models for paper composition, experimental design, or conceptual development. All core scientific contributions were independently developed by the authors without LLM assistance.
B RELATED WORK
B. SPIKING NEURAL NETWORKS
Spiking neural networks (SNNs) are novel neural network models that simulate information process- ing mechanisms in biological neural systems. Unlike traditional artificial neural networks (ANNs), SNNs transmit and process information through discrete spike signals, offering higher biological interpretability and temporal processing capabilities (Maass, 1997; Gerstner & Kistler, 2002; Roy et al., 2019). In recent years, with advances in hardware technology and algorithmic innovations, SNNs have made progress in image recognition, speech processing, and robotic control (Tavanaci et al., 2019; Wu et al., 2018). However, due to their discontinuous nature, SNNs face challenges in training and optimization, particularly evident in complex tasks such video understanding. Especially in video understanding tasks, SNNs must process substantial temporal information and complex spatial structures, placing higher demands on their temporal feature learning capabilities (Dong et al., 2024; Fang et al., 2021b). Consequently, enhancing SNN performance in video under- standing has emerged as significant research focus.
B.2 VIDEO UNSUPERVISED LEARNING
Video unsupervised learning aims to learn meaningful temporal and spatial feature representations from unlabeled video data. In recent years, contrastive learning- baaning-based methods have achieved sig- nificant progress in video unsupervised learning (Han et al., 2019; Ahsan et al., 2019; Feichtenhofer et al., 2021). These approaches optimize models through contrastive loss functions using constructed positive and negative sample pairs, enabling capture of temporal dynamics and spatial structural in- formation in vidcos. DPC (Han et al., 2019) iteratively predicts future features by inputting each timestep's s features into an external temporal processing module. VideoJigsaw (Ahsan et al., 2019) learns temporal information through video block reorganization. CoCLR (Han et al., 2020b) learns video representations by aligning optical flow with video content. Lorre et al. (Lorre et al., 2020) employ CPC-like methods that predict future features. The series models Feichtenhofer et al. (2021) introduce contrastive methods to the video domain with temporal correlation components. VideoMoCo (Pan et al., 20 learns through adversarial samples using the MoCo method. Addi- tionally, generative models have been widely applied in video unsupervised learning, learning latent video representations by reconstructing video frames generating future frames (Wei et al., 2022; Wang et al., 2022). However, most existing video unsupervised learning methods are designed pri- marily for ANNs, leaving the effective application of these methods to SNNs urgent problem requiring resolution.
B.3 UNSUPERVISED LEARNING IN SNNS
Research on unsupervised learning in spiking neural networks (SNNs) has been relatively limited, though it has begun attracting attention in recent years (Diehl & Cook, 2015; Dong et al., 2023; Ma et al., 2025). Existing work primarily focuses on implementing unsupervised learning SNNS through plasticity rules and local learning algorithms (Diehl & Cook, 2015; Dong et al., 2023; Oror- bia, 2024: Saunders et al., 2019). For instance,
Plasticity (STDP), a learn- ing rule based biological neuronal plasticity, has been widely applied in unsupervised learning with SNNs (Bi & Poo, 1998). Additionally, some studies have attempted to apply unsupervised learning methods such contrastive learning to SNNs (Ma et al., 2025; Bahariasl & Kheradpisheh, 2024), or adapt deep methods originally developed for ANNs (Li et al., 2023). Other approaches focus on relationships between events and images (Hagenaars al., 2021). However, existing re- search primarily concentrates on shallow networks without a systematic benchmark methodology, while focusing on image data rather than addressing temporal video data processing.
Timing-
15

---

Published as a conference paper at ICLR 2026
C MORE RETRIEVAL VISUALIZATION RESULTS
We provide additional video retrieval visualization examples here. As observed, PredNext success- fully retrieves semantically consistent videos even when significant variations exist camera angles, athlete appearances, and visual environments. Even in instances of retrieval errors, the retrieved re- sults typically maintain some semantic relevance the query video.
Figure 7: Visualization of more retrieval results. Query videos with corresponding Top-3 retrieval results. Results for three query samples shown, with one sample per row.
16

---

Published as a conference paper at ICLR 2026
D KNN TRAINING CURVE
To demonstrate the pretraining process, we evaluated the feature representation capability of our models during pretraining using KNN classifiers, which can assess features without downstream task fine-tuning, We conducted evaluations on UCFI01 splitl. Figure shows the top accuracy curves of KNN classifiers throughout the pretraining process. As observed, PredNext significantly enhances the model's 's feature representation capabilities.
SimSlam
SIMCLR
BarlowTeiis
30.0
40.0
similsity
20.0
20.0
BYOL ProdNest(BrOL)
20.0
SINCLR ProdNenDSieCLB)
20.0
MoCo Prod Mo
20.0
30.0
(SimSiam)
10.0
10.0
20.0
[Barfoerlwee
Fpochs
Epochs
Epochs
Epochs
Epochs
SimSiam
SimCLR
Barlow
70.0
70.0
60.0
Siniathy
$2.0
60.0
50.0
60.0
50.0
50.0
Simdiam PresMooxt (Simtiam)
BYGL
AB.0
SimMLA
40.0
MaCo PredStea((MoCo
BarlowTwins Predhiost (Berrow/T mns
PredNeci(PPD))
Pre:Nees/SIm2.. R)
30.0
30.0
100 Epochs
200
100 Epochs
230
100
200
1000 Epochs
100 Epochs
200
Epachs
Figure 8: Visualization of KNN training Curve, showing Top1 (top) and Top5 (bottom) accuracy
curves, respectively.
E COMPUTATIONAL ANALYSIS
We provide device resource comparison PredNext on SimSiam and SimCLR methods Table 10. PredNext introduces only marginal increases in training time, GPU memory usage, Memory, GPU Memory and FLOPs. This demonstrates that PredNext maintains low computational overhead while improving performance, making it suitable for large-scale training in practical applications.
Table 10: Computational Analysis of PredNext on SimSiam and SimCLR methods. T denotes the total number of input frames.
SimSiam PredN eext
SimCLR PredNext
SimSiam
SimCLR
GPU devices
4
Training Time
1.39min/epoch
1.4 1.43min/epoch
1. 1.20min/epoch
1.36min/epoch
GPU Memory
x2.2Gx4
12.4Gx4
12x1
12.4Gx4
Memory Peak
40GB
43GB
37GB
47GB
FLOPs
1.188GXT
1.13GXTT
1.18GGXT
1.193GXT
Data Workers
16
16
16
16
Throughput
1 114.3framesss
132.5frames/s 1frammsssss/s
116.9frames/s
17

---

Published as a conference paper at ICLR 2026
F DISSCUSSION ON TEMPORAL DYNAMICS IN SNNS
The temporal dynamics in spiking neurons are crucial for the entire network. However, we ar- gue that solely relying on neuronal dynamics to implicitly learn temporal characteristics does not fully exploit the potential of spiking neurons, On one hand, SNN architectures typically borrow from ANN image recognition network designs, which makes networks more prone to spatial bias. video models(Goyal et al., 2017; Choi et al., 2019). On the other hand, SNNs lack the progressive temporal aggregation mechanisms present in ANN 3D(Crreeira & Zisserman, 2017) convolutional networks, preventing temporal dimensions from undergoing gradual downsampling through pooling layers or larger-stride convolutions as spa- tial dimensions do, thereby limiting sufficient temporal information extraction. Therefore, to explicitly enhance temporal consistency through architectural design, thereby alleviating the net- work's spatial bias while improving temporal aggregation capability to more fully leverage the tem- poral processing capacity of spiking neurons.
Similar observations have been made in
aim
G THEORETICAL ANALYSIS
In the original method, computation focuses on modeling relationships between sample instances. In this work, we further attend to computational interactions between frames and clips, which unique characteristics of temporal data.
Video data contains two types of information:
(i) semantic content S, such as action categories and object identities, which remains relatively stable over time;
(ii) low-level noise N, such as illumination variations and camera shake, whose temporal correlation decays rapidly.
These two information types exhibit fundamentally different temporal correlation characteris- tics(Taylor al., 2010; Goyal et al., 2017): semantic content demonstrates long-range correlation ps(m) 2 e esm, while noise exhibits exponential decay (m(m) x e -"Ay-""Wiskoo & Sejnowski (2002), where eg & Av- This implies that "sport action" persists across multiple frames, whereas "instantaneous glare at particular moment" quickly disappears.
PredNext's temporal prediction objective Eped is equivalent maximizing mutual information 1 =2;;=") x' denotes the temporally aggregated representation of next clip. As- suming semantic and noise statistics are approximately independent. This assumption generally reasonable for video data, as short-term noise and long-term semantics occupy separated signal fre- quency spectra(Rudermal & Bialek, 1993): 2 = ps (m) + pm()). For prediction step m, the noise mutual information [nn'; +m) -2x approaches zero, while the semantic mutual informa- tion [s";; s (++) ) remains substantial. Consequently, the optimization process naturally prioritizes encoding predictable semantic content while filtering unpredictable noise. Predictability serves as an implicit regularizer that filters out unpredictable noise. This also explains why enforced consis- tency proves detrimental: the forced constraint Lioreed Ei,,, cos(Ji, indiscriminately suppresses all temporal variations.
18

---

Published as a conference paper at ICLR 2026
H SETTING DETAILS
We provide detailed experimental specifications to facilitate the reproduction of our work.
H.1 EXPERIMENTAL DETAILS
For all experiments, we employed SEW ResNet as the feature extraction backbone network and implemented models using the PyTorch framework. Synchronized batch normalization layers were utilized across all experiments due to multi-GPU training. Automatic mixed precision (AMP) train- ing was employed across all experiments to enhance training efficiency.
Pre-training used AdamW optimizer with an initial learning rate 0.002 and weight de- cay of 1e-6, applying cosine annealing learning rate scheduling. For UCFI01, conducted 200 epochs training with a 20-epoch warmup process; for MiniKinetics, 120 epochs with 12-epoch warmup. Training utilized mini-batches of size 128. Data augmentation included random cropping (scale: (0.2, 0.766), ratio: (0.75, 1.3333)), horizontal flipping( p: 0.5 ), color jittering( brightness: 0.6, contrast: 0.6, saturation: 0.6, hue: 0.1), and random gray( p: 0.2). For UCF101, videos were cropped to 128 x 128 resolution with 16 frames randomly sampled at a stride of 2: for MiniKinetics, videos were cropped to 112 x 112 resolution with frames randomly sampled
stride of 8.
Fine-tuning We employed the Adam optimizer with an initial learning rate of 0.0003 without weight decay, applying cosine annealing scheduling. For UCF101 and HMDB51, videos were cropped to 128 128 resolution with 16 frames randomly sampled at stride 2: for MiniKinetics, videos were cropped 112 x 112 resolution with frames randomly sampled at stride Train- ing used mini-batches of size 128 for 100 epochs on UCF101 and HMDB51, and 50 epochs on MiniKinetics. Evaluation uniformly sampled 3 clips per sample.
H.2 MODEL DETAILS
For SimCLR, projection layer output dimension 256 with temperature coefficient 0.5. For MoCo, projection layer output dimension was 256, momentum coefficient 0.99, queue size 4096, and temperature parameter 0.5. For BYOL, projection/prediction layer output dimension was 2048, with prediction layer hidden dimension 512 and momentum coefficient 0.99. For BarlowTwins, projection layer output dimension was 1024. For SimSiam, projection/prediction layer output di- mension was 2048, with prediction layer hidden dimension 512. PredNext's 's prediction heads Pr and Pe both used hidden layer dimension 512, with output dimensions matching the projection layer output dimensions of their respective base sulf-supervised methods.
19


Reviews:
[{"review_id": "4XPIOWPGlO", "reviewer": "Reviewer_dmJN", "summary": "This paper studies self‑supervised learning (SSL) for spiking neural networks  in video representation learning. The authors propose two pretext tasks tailored to spiking video encoders—Step Prediction and Clip Prediction and systematically benchmark several mainstream SSL paradigms adapted to SNNs on UCF‑101 and HMDB‑51. The work evaluates downstream performance on action classification and video retrieval, and provides an analysis of feature distribution and temporal consistency.", "questions": "1. How many GPUs are required to train each main model? \n2. While the results of different SSL methods are present in Table 3 and Figure 4, there lack a detailed analysis and explaination on what causes the results to differ. Can you elaborate on the comparison between SSL methods?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Presents (to my knowledge) one of the first systematic comparisons of multiple SSL families when ported to SNN video encoders.\n2. The Step Prediction and Clip Prediction pretext tasks appear well‑aligned with SNN dynamics. Solid empirical evaluation across two standard video benchmarks with both classification and retrieval. The ablations indicate both pretext components contribute to the gains \n3. Analysis on the distribution and temporal consistency of video features in Fig 4 is interesting and helps explain how SSL affects time‑evolving SNN features.\n4. Establishing baselines and guidance for SSL with SNNs is useful for the neuromorphic community and may reduce the barrier to entry for future work.", "weaknesses": "1. Absolute performance vs. ANN baselines. Your best SNN numbers (e.g., **72.2%** on UCF‑101 and **41.5%** on HMDB‑51) lag a modest but non‑trivial margin behind a supervised **ANN ResNet‑18** initialized from ImageNet (≈**76.3% / 48.9%** as reported in **[1]**). This gap matters for ICLR’s broader audience.\n2. Compute, memory, and scalability are under‑specified. SNN-based models scale poorly on modern GPUs due to it's recurrent temporal dimension. Training SNNs over time steps often incurs high **memory and wall‑clock** costs. The paper currently omits **GPU hours** and **peak memory**, for your main settings (e.g., $b=256$, $T=16$). Actionable: Report GPU hours and peak memory per configuration; Add training throughput and inference latency. If efficiency is a motivation, consider reporting sparsity metrics (average spike rate) or proxy energy estimates on commodity hardware.\n\n[1] Xiao et al, ReSpike: Residual Frames-based Hybrid Spiking Neural Networks for Efficient Action Recognition, Neuromorphic Computing and Engineering (2025)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "L302jsEUgY", "reviewer": "Reviewer_85ng", "summary": "The PredNext paper presents a novel module to enhance unsupervised representation learning in Spiking Neural Networks (SNNs), specifically for temporal video data. The core innovation is explicitly modeling temporal relationships by forcing the network to perform cross-view future feature prediction through two heads: Step Prediction and Clip Prediction. PredNext's predictive objective guides the network to learn stable, semantically rich features that are naturally consistent, a superior alternative to merely imposing forced consistency constraints, which the authors show degrades performance. The module ntegrates with existing self-supervised methods, and achieves significant performance gains on large-scale video benchmarks, even matching ImageNet-pretrained supervised weights.", "questions": "1. The performance appears to peak sharply at one-step prediction ($m=1$). Can the authors clarify whether this is a model limitation or a training stability issue? Have they explored architectures or loss designs that allow for longer temporal prediction without collapse?\n\n2.  Since $\\alpha$ directly controls the trade-off between self-supervised and predictive losses, how sensitive is PredNext to its value? Would an adaptive or scheduled weighting improve stability?\n\n3. The paper notes that the prediction heads are only used during training, but could the authors quantify the actual increase in computation and memory footprint? Understanding this cost would help assess scalability to larger networks or datasets.\n\n4. Why were biologically inspired unsupervised methods (e.g., STDP or predictive plasticity-based models) excluded from benchmarking? Including at least one such baseline would better contextualize PredNext’s contribution within SNN research.\n\n5.  PredNext improves temporal consistency but risks over-smoothing. How do the authors ensure that important temporal variations (e.g., motion or action cues) are not suppressed during training?\n\n6. Since SNNs are well-suited to event-driven data, do the authors expect PredNext to transfer effectively to modalities like DVS or multimodal fusion (e.g., vision + optical flow)? If so, how would the framework adapt?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "The paper presents an original and well-executed contribution to unsupervised learning in spiking neural networks by introducing explicit cross-view temporal prediction to enhance temporal consistency. The idea is conceptually novel within the SNN context, bridging predictive coding principles with modern self-supervised methods like SimSiam and MoCo. The experiments are thorough, spanning multiple large-scale video datasets, and the reported gains are consistent and meaningful. The writing is clear, the methodology is reproducible, and the visualizations effectively support the claims. Overall, the work makes a significant and timely contribution by demonstrating that deep SNNs can achieve competitive unsupervised performance on complex temporal data, pushing the field beyond small-scale, biologically inspired setups.", "weaknesses": "The paper’s main weakness is that PredNext mostly improves short-term smoothness rather than learning true long-range temporal structure. Performance drops quickly when predicting beyond one step, showing limited temporal modeling capacity. \n\nThe training objective is also sensitive to the loss weight ($\\alpha$), but the paper provides no analysis of how this affects stability or generalization. Comparisons are limited to ANN-based self-supervised baselines, leaving out SNN-native or biologically inspired methods such as STDP-based learning. This makes it hard to judge the real advantage of PredNext for spiking systems. \n\nThe two extra prediction heads add non-trivial computation during training, which could limit scalability.\n\nFinally, the experiments focus only on visual data and do not test multimodal or event-based inputs, where SNNs typically excel.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zAC6dSjiFf", "reviewer": "Reviewer_vPjs", "summary": "The paper proposes PredNext, a plug-and-play auxiliary module for unsupervised SNNs that adds two explicit temporal prediction heads: Step Prediction  and Clip Prediction , trained with cross-view targets. The method is integrated into standard SSL frameworks (SimCLR, MoCo, SimSiam, BYOL, Barlow Twins) and evaluated on UCF101, HMDB51, and miniKinetics. PredNext consistently improves SNN baselines on classification and retrieval and reports analyses of “temporal feature consistency,” arguing that explicit prediction improves consistency more productively than forced consistency constraint.", "questions": "Please refer to the four questions included in **Weaknesses** for details.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1) Even without deep familiarity with DVS video, I could follow the technical pipeline and reasoning; clarity and readability are clear strengths of this submission.\n\n2) The cross-view PT/PC heads are simple and slot into multiple SSL recipes for SNNs. This idea is a practical contribution for the community.\n\n3) The paper moves beyond small DVS benchmarks to UCF101, HMDB51, and miniKinetics, with coherent pretrain/finetune protocols.\n\n4) Across SimCLR/MoCo/SimSiam/BYOL/Barlow Twins, PredNext improves top-1/5 and retrieval R@K; ablations illustrate the impact of PT vs. PC and step length.\n\n5) The paper formalizes a consistency error and contrasts explicit prediction vs. direct consistency penalties, a useful negative result.", "weaknesses": "1) Fig. 1(b): The “distribution of video features in high-dimensional space” appears as a schematic; it’s unclear whether it represents t-SNE or merely an illustration. Define what **blue vs. red** denote, what **green arrows** represent, and  what the inter and intra- cluster distance encodes.  Fig. 2 remains visually cluttered. Arrows cross and re-enter modules, making the flow hard to follow. Please improve the labeling.\n2) Please **number** all equations. In the “final optimization objective,” specify whether **α** is fixed across datasets or tuned per dataset. Provide an ablation varying **α** to assess the relative importance of each view’s learning target and report the setting you recommend.\n3) **Positioning vs. strong ANN baselines is incomplete.** Table 3 has **no ANN SSL baselines** under the same spatiotemporal setup (e.g., ResNet-18/34  or a Video Transformer). Since the paper argues SNNs better capture long-range temporal dependencies/consistency, please add **ANN-SSL counterparts** trained under the identical clip lengths, strides, and augmentations. This will solidify the “SNN advantage” claim. \n4) **Scaling.** How do results change with **SEW-ResNet-34/50** and with a **spiking transformer** backbone?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "F9Sasd3uzH", "reviewer": "Reviewer_BJ1t", "summary": "This paper proposes PredNext, a novel self-supervised learning framework for deep Spiking Neural Networks (SNNs) on video data. PredNext explicitly models temporal relationships through cross-view future feature prediction, incorporating both step-wise and clip-level prediction mechanisms to enhance semantic consistency and improve feature generalization. Designed as a modular plug-and-play component, PredNext can be seamlessly integrated with a variety of existing self-supervised methods (e.g., SimCLR, BYOL, SimSiam) without modifying backbone architectures. The authors further establish the first systematic benchmark for self-supervised SNNs on large-scale video datasets, including UCF101, HMDB51, and MiniKinetics. Experimental results demonstrate that PredNext consistently yields substantial performance gains and achieves performance comparable to supervised ImageNet pretraining, enabling effective unsupervised temporal representation learning for deep SNNs on complex video tasks.", "questions": "1. In Line 3 of Algorithm 1, it appears that the notation suggests $x_i$=$x_j$. If the authors intend to express that the outputs of the data augmentation function differ due to randomness applied at different times, the current formulation may be misleading and could benefit from clarification.\n\n2. In Line 4 of the algorithm, the symbol T seems to be used to represent the number of time steps. However, if I understand correctly, throughout the paper T denotes data augmentations (or is associated with the augmentation function), while N should represent the temporal length. Similar symbol inconsistencies appear in several places. If my interpretation is incorrect, I would appreciate the authors’ clarification.\n\n3. Could the authors provide a theoretical analysis to support the problem formulation and the effectiveness of PredNext, instead of relying solely on empirical observations? Even a supplementary theoretical justification in the appendix would significantly strengthen the submission, especially considering ICLR’s expectations.\n\n4. Could the authors more clearly emphasize the direct relationship between this work and SNNs? The current presentation seems to focus more on modeling spatiotemporal features between frames, rather than leveraging the intrinsic autoregressive temporal dynamics of spiking neurons within frames, which is typically a core property of SNNs.\n\n5. The authors claim that previous work did not train unsupervised SNNs to deeper network depths. However, this is not entirely accurate, and the proposed method also does not extend beyond SEW-ResNet18 in depth. Do the authors have additional evidence or analysis supporting this claim? In particular, a statistical comparison of unsupervised methods applied to deeper SNNs or larger parameter scales would be crucial to validate the novelty emphasized in the manuscript.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. Traditional SNNs rely solely on the autoregressive spatiotemporal dynamics of LIF neurons to capture temporal information, which recent studies have suggested is insufficient. This paper provides new insights into this issue, which is highly commendable.\n\n2. This work contributes valuable and important baselines by adapting several classical unsupervised learning algorithms to SNNs. This represents a meaningful contribution to the community.\n\n3. The authors conducted extensive experiments that convincingly demonstrate the effectiveness of the proposed method.", "weaknesses": "1. It is recommended to revise the introductory description around line 79. Specifically, the authors should emphasize that the temporal processing capability of SNNs originates from the intrinsic dynamics of spiking neurons rather than solely from the membrane potential. Moreover, not all neurons produce linear outputs below the threshold, so the current wording may be misleading.\n\n2. Figure 2 appears overly complex and does not leverage color cues to guide the reader’s attention, which may hinder comprehension of the algorithm. It is very likely that readers will find this figure difficult to understand by the end of the Introduction. This issue may be alleviated by briefly explaining the roles of  $P_T$ and $P_C$ either within the figure or in close proximity to it.\n\n3. Some items in Table 1 could be formatted in bold to highlight key information more effectively.\n\n4. In Table 2, it is suggested to use checkmarks and crosses in a consistent font style or ensure they originate from the same LaTeX package for visual uniformity.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)