Robust Selective Activation with Randomized Temporal K-Winner-Take-All in Spiking Neural Networks for Continual Learning
Authors: Jiangrong Shen
Liang Zhao
Qi Xu
Yuqi Yang
Liangjun Chen
Gang Pan
Badong Chen
Venue: ICLR 2026 Poster (2026)
Keywords: Spiking neural networks
Primary area: applications to neuroscience & cognitive science

Abstract: The human brain exhibits remarkable efficiency in processing sequential information, a capability deeply rooted in the temporal selectivity and stochastic competition of neuronal activation. Current continual learning in spiking neural networks (SNNs) faces a critical challenge: balancing task-specific selectivity with adaptive resource allocation and enhancing the robustness with perturbations to mitigate catastrophic forgetting. Considering the intrinsic temporal dynamics of spiking neurons instead of traditional K-winner-take-all (K-WTA) based on firing rate, we explore how to leave networks robust to temporal perturbations in SNNs on lifelong learning tasks.
In this paper, we propose Randomized Temporal K-winner-take-all (RTK-WTA) SNNs for lifelong learning, a biologically grounded approach that integrates trace-dependent neuronal activation with probabilistic top-k selection. By dynamically prioritizing neurons based on their spatiotemporal relevance, RTK-WTA SNNs emulate the brain’s ability to modulate neural resources in spatial and temporal dimensions while introducing controlled randomness to prevent overlapping task representations. The proposed RTK-WTA SNNs enhance inter-class margins and robustness through expanded feature space utilization theoretically. The experimental results show that RTK-WTA surpasses deterministic K-WTA by 3.07–5.0\% accuracy on splitMNIST and splitCIFAR100 with elastic weight consolidation. Controlled stochasticity balances temporal coherence and adaptability, offering a scalable framework for lifelong learning in neuromorphic systems.

Published as a conference paper at ICLR 2026
ROBUST SELECTIVE ACTIVATION WITH RANDOMIZED TEMPORAL K- WINNER-TAKE-AN IN SPIKING NEU- RAL NETWORKS FOR CONTINUAL LEARNING
Jiangrong Shen Gang Pan
Liang Zhao?.3
Qi Xu Yuqi Yang
Liangjun Chen2.7
Badong 2
School of Computer Science and Technology, Xi'an Jiaotong University
National Key Laboratory of Hluann-Machigg Hybrid Augmented Intelligence, Xi'an Jiaotong University
College of Artificial Intelligence, Xi' an Jiaotong University
4 School of Computer Science and Technology, Dalian University of Technology
School of Cyberspace Security and Computer Science, Hebei University
State Key Lab of Brain-Machine Intelligence, Zhejiang University
Institute of Artificial Intelligence and Robotics, Xi' an Jiaotong University
ABSTRACT
The human brain exhibits remarkable efficiency in processing sequential informa- tion, capability deeply rooted in the temporal selectivity and stochastic competi- tion of neuronal activation. Current continual learning in spiking neural networks (SNNs) faces critical challenge: balancing task-specific selectivity with adaptive resource allocation and enhancing the robustness with perturbations to mitigate catastrophic forgetting. Considering the intrinsic temporal dynamics of spiking neurons instead of traditional K K-winner-takee all (K-WTA) based on firing rate, we explore how leave networks robust to temporal perturbations in SNNs on lifelong learning tasks. In this paper, we propose Randomized Temporal K- K-winner-take-al (RTK-WTA -TA) SNNs for lifelong learning, a biologically grounded approach that integrates trace-dependent neuronal activation with probabilistic top-k selection. By dynamically prioritizing neurons based on their spatiotemporal relevance, RTK- WTA SNNs emulate the brain's ability to modulate neural resources in spatial and temporal dimensions while introducing controlled randomness to prevent over- lapping task representations. The proposed RTK-W -WA SNNs enhance inter-class margins and robustness through expanded feature space utilization theoretically. The experimental results show that RTK-WTA surpasses deterministic K-WTA by 3.07-5.0% accuracy on splitMNIST and splitCIFAR 100 with elastic weight con- solidation. Controlled stochasticity balances temporal coherence and adaptability, offering a scalable framework for lifelong learning in neuromorphic systems.
INTRODUCTION
The human brain exhibits remarkable efficiency in processing sequential information, a capability deeply rooted in the temporal dynamics of neuronal activation Kandel & Hawkins (1992): Bassett al. 1201T). Biological neurons operate under principle of selective activation, where sparse subsets of cells fire in temporally coordinated patterns to encode specific stimuli or tasks Kudithipudi al. (2022). This sequential, task-dependent recruitment of neuronal populations is hallmark of continual learning in biological systems, enabling humans to acquire new skills over time while preserving previously learned knowledge. The selectivity is not static but evolves dynamically across time scales, governed by mechanisms such as short-term synaptic plasticity and adaptive K-winner- take-all (K-WTA) competition. Crucially, biological WTA circuits integrate both deterministic competition, where neurons with stronger inputs dominate, and controlled stochasticity, ensuring that activation patterns remain flexible and robust against overlapping task demands. This interplay
"oorreponning author; chenbd@mail .xjtu. edu.cn.

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between temporal precision and randomness allows the brain to avoid rigid specialization, instead maintaining a balance between task sask-speccfic efficiency and generalization across contexts.
In artificial intelligence, continual learning aims to emulate the biological ability to acquire new knowledge over time without sacrificing performance on previously learned tasks. However, unlike conventional training paradigms that assume access to all data simultaneously, continual learning must operate on sequential and often von-overaa task streams. This setting makes neural networks highly susceptible catastrophic forgetting, where optimizing for new task overwrites crucial representations established for earlier tasks. Unlike traditional artificial neural networks (A (ANNs), SNNs operate through discrete spikes representing the firing or non- non-firing of spiking neurons at specific time points which leads to richer and more complex spatiotemporal dynamics Maass ( (1997);
Subbulakshmi Radhakrishnan et al. (02T); Bu et al. (002)). This event-driven processing enables SNNs to capture temporal information more effectively, maintain memory more robustly, mimic the behavior of biological neural systems more closely, and support energy-efficient deployment (Zhao] et al. 2025 ; Bu et al. 20255; ; Xu et al. (2025). These biological and computational properties make SNNS a particularly promising substrate for continual learning, since the spatiotemporal dynamics could provide naturally separated, temporally distinct activation patterns that reduce representational overlap and mitigate interference across sequential tasks.
Continual learning in SNNs represents a paradigm shift in artificial intelligence by emulating the human brain's 's ability to acquire and retain knowledge over time while adapting to new information without catastrophic forgetting. To this end, moologically-insiiee strategies such synaptic plasticity Kaiser et al. (2020); Schmidgall et al. (2021). neural reorganization Han et al. (2025), weight consoli- dation Golden et al. (2022); Ning et al. (2023), and dynamic routing Putra & Shafique (2021) have been explored to optimize continual learning. These approaches leverage SNNs' inherent biological and computational properties such as spatiotemporal dynamics. However, most of these models ignore the robustness of SNNs during spatiotemporal activation. The random localized competition reduces representational overlap across tasks and has the potential to improve resistance to interfer- ence, thereby enhancing functional robustness of SNNs in dynamic environments. Likewise inspired by the biological robustness mechanisms, we further explore how to build robust SNNs capable of continual learning by leveraging temporal selectivity and stochastic K-WTA dynamics. Most of the traditional K- WTA implementations in SNNs adopt deterministic spatial competition, selecting neurons with the highest instantaneous firing rates membrane potentials. Those approaches neglect the temporal richness of spiking signals and fail to emulate the brain's 's ability to modulate activation patterns across time Shen al. (2023). Recent SNN studies have also highlighted that explicitly modeling tire-narying spatiotemporal dynamics is crucial for effective information integration Shen et al. (2025); Xu et al. By exploiting the temporal trace property and designing the trace-based K-WTA, SSenn et al. (2024) have demonstrated the more abundant representation ability of SNNs with the addition of temporal competition. However, the robustness of that model limited since the temporal trace selections are deterministic. The biological neurons rely on temporal traces of past activity to guide future responses, enabling them to prioritize inputs that align with ongoing temporal contexts. Integrating these traces with random top-k selection, where a stochastic subset of neurons is activated, could mirror the brain's i adaptive K-WTA mechanisms. By coupling temporal trace accumulation with controlled randomness, SNNs could dynamically allocate neural resources to tasks while mitigating catastrophic interference caused by overlapping activations McCloskey & French (1999).
Cohen 1989]
Recent advances in SNNs have yet to fully exploit this synergy. Most existing selective activation strategies focus spatial sparsity or static firing thresholds, overlooking the temporal dynamics. Deterministic K- W-A rules, while effective for isolated tasks, risk rigid binding of neurons to specific patterns, leaving networks vulnerable to input perturbations and catastrophic forgetting Lynchh
when tasks share similar characteristies Lynch et al. (2019). In contrast, a trace- based random top-k mechanism introduces two critical innovations: (l) temporal random selectivity, where neurons are prioritized based on their accumulated activity traces, ensuring alignment with the sequential structure of tasks, and (2) stochastic competition, which injects diversity into activation patterns to prevent task representations from converging to overlapping subspaces. This biologically grounded approach not only enhances robustness but also provides scalable framework for lifelong learning in SNNs, addressing a fundamental gap in current neuromorphic architectures.
We argue that this issue arises because the architecture does not recognize the sequential nature of learning. Existing SNN models often treat tasks as discrete, independent events, employing activation
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Data
Task
Pretrained
Hidden layer
Temporal Random K-WTA mechanism
Probabilistic Top-K Sampling
Task
Opportunistic activation
Tass 3
Persistent Supgression
Persiatent activation
decay
degree randomness
Embeddingg
Top-k neurons in trace (A.)
Random-Topp neurons in trace (A.)
(a)
Thm
(G)
(b)
X
sathe freez
nanihighest
Nigheab-iraca
Sampling at three time points with random probability highlighted.
(b)
Figure 1: (a) The architecture of the proposed randomized temporal winner-take-all (RTK-WTA) for SNNs. (b) An example to show the working mechanism of the RTK-WTA mechanism for SNNs.
strategies optimized for static inputs rather than evolving temporal contexts. This oversight leads to two critical failures, First, deterministic K-WTA mechanisms lack the adaptability to dynami- cally reweight neurons as task sequences unfold, forcing networks to overwrite previously learned representations. Second, without explicit decoupling of task-specific neural mathways-enable enabled by trace-modulated stochasticity- even sparse activations may interfere across tasks, accelerating catastrophic forgetting. Our work bridges this gap by unifying temporal trace dynamics with random top-k selection, enabling SNNs to emulate the brain's 's ability to balance specificity and ficxibility in sequential learning. The contributions are summarized follows:
We propose biologically inspired randomized temporal K- K-winner-take-all (RTK-WTA) for SNNs in continual learning scenarios. RTK-WTA integrates temporally accumulated neuronal traces with probabilistic top-k realize robust selective activation. This design dynamically prioritizes neurons based on temporal relevance while introducing controlled stochasticity to reduce overlapping activations and mitigate catastrophic forgetting.
Through theoretical and empirical analysis, RTK-WTA enlarges the effective spatiotemporal feature space of SNNs. By aligning neural activation with task-specific temporal dynamics, the method enhances the diversity of internal representations and facilitates the separation of overlapping task features, thus promoting stable knowledge retention across tasks.
Experiments on continual learning benchmarks including SpLitMNIST, SplitCIFAR 10, and SplicCIFAR- datasets demonstrate that our model consistently outperforms deter- ministic Top-K baselines by margins of to 10.05%. Furthermore, RTK-WTA exhibits strong resilience to input perturbations and task interference by explicitly decoupling neural pathways through trace-driven stochastic activation, enabling more robust and scalable continual learning.
METHODS
Figure a) illustrates the architecture of the RTK-WTA in SNNs, which employs trace- based probabilistic top-k selection according neuronal activation dynamics. This mechanism enhances

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the model's generalization capacity through expanded feature space utilization while maintaining alignment with selective activation mechanisms observed in biological neural systems.
Taking three temporal instances of neuronal activation selects three distinct neuron types as illustrated in Figure I (b): highest-rate, highest-trace, and non-highest neurons. Combined with the spiking firing of five neurons in the left part of Figure I (b), the rate-based K-WTA solely activates one neuron (5ta neuron) across three different time points (b1, 12, ta). While the only trace-based SNNs without random K-WTA activate two neurons (3rd and 5th neurons) across these time points. Our RTK-W WTA SNNs activate three neurons (1st. 3rd and 5eh neurons) across these time points. This demonstrates our mechanism's capacity to promote firing across neurons with varying states, thereby enabling exploration of broader feature space. Therefore, for continual learning scenarios, the activation diversity our RTK-W WTA SNNs maintains redundant neuronal activation patterns, forming complementary memory representations during sequential task learning. When new tasks interfere with partial neurons, the remaining neuronal populations preserve historical task information, thus mitigating catastrophic forgetting.
example, RTK-WTA mechanism
2.1 RANDOMIZED TEMPORAL TRACE-BASED K-WTA SELECTION
In this paper, we regard the trace as the indicator for random temporal K-WTA. The trace in spiking neurons provides an efficient way to online estimate the mean firing rate related to the spike train Morrison et al. (2008) which bridges the gap between time scale and action potential in the plasticity theory. The trace is updated with each spike-firing behavior and decays between spike-firing behaviors, which is more concise intrinsic information expression for spiking neurons compared to the pure spiking firing rates. Therefore, the accumulation manner of trace provides stable metric for stochastic selection, effectively mitigating task interference without sacrificing temporal resolution.
We begin by clearly defining the dynamics of the neuronal trace tr: [t] in the spiking neuron model discrete time step t:
ft, + tr,[t] tr.14
S 1].
where 7 is the decay constant of the neuronal trace and S, + E| 0, denotes the generated spike train time + 1. This trace provides a temporally integrated representation of neuronal spiking activity, offering robust and distinctive temporal features necessary for efficient continual learning.
Based on that, we propose the randomized trace-based Top-K (RTK) selection based on the neuronal traces at each time step in order to enhance the robustness and improve the generalization capability of SNNs for continual learning tasks. For a network of d neurons, the RTK selection generates binary mask:
Mask[[ (TK(r))4)
(2)
where the probability of selecting neuron i at time is defined as
(1- (a/(d- K), otherwise.
tri(t is top K trace;
((RTK(tr,(tt) =)) = (1))-(/(d-1))
(3)
where a E [0. 1] controls randomness during the trace-based top selection and K is the number of neurons selected at each time step.
The above randomized trace-based K- K-WTA provides a larger feature space than traditional K-WTA by extending the top K selection from spatial dimension to spatiotemporal dimension and, meanwhile, bringing suitable randomness to explore a larger manifold on neuron selection and avoid dropping into a local minimum.
Then the spiking output after applying RTK-WTA is
(* = S(t] Mask|t],
In continual learning settings, this selective and stochastic activation across different time steps enables differentiated neural responses for sequential tasks, mitigating overlap in active subspaces
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and reducing interference. Thus, by combining with the K-WTA neuron selection in spatiotemporal, creates implicit task separation in the temporal domain without explicit task labels.
2.2 GENERALIZATION ANALYSIS OF RTK-WTA SNNS
The recursive update rule in equation captures the short-term memory of spiking events: each spike adds to the trace, while the trace decays over time if no spikes occur. Unlike conventional neurons that reset their state completely between time steps, the trace persistence allows the network to maintain short-term memory of recent spikes while gradually forgetting older events. It implicitly performs an exponential moving average over the spike history. The decay constant controls the effective memory window smaller values cause faster forgetting for rapid pattern detection, while larger values enable longer temporal integration. To make this integration explicit, we consider its unrolled form over a temporal window of length T. This leads to the definition of an integrated trace vector:
T
1 1
Tr
(1-
1=1
This representation accumulates past spikes with exponentially decaying weight, emphasizing recent activity. Recent spikes (when approaches T) contribute more significantly to the current trace value than distant spikes. The weighting scheme creates continuous spectrum of time sensitivity the same spike pattern occurring at different temporal positions will produce distinguishable trace states, enabling dosition-invariial pattern recognition. Compared to the recursive trace update, this formulation provides a static, interpretable form of the trace for use in theoretical analysis, such as feature space volume and margin estimation.
We now analyze the generalization enhancement by considering the temporal integration of traces.
The overall temporal feature representation for all neurons is Ty(()) (Tr ..... Tr(T)) ) Due to the RTK-WTH WTA mechanism, the effective number of possible activation patterns over the temporal window expands significantly.
At each time step L, the effective number of neuron activation combinations, i.e., the possible configurations of K neurons being selected out of d with stochastic probabilities are approximated by
(1- aK dl K (t)
No
Here (x) is the total number of possible K-sized subsets from d neurons (structural degrees of freedom), and the term in brackets approximates the expected selection probability per neuron when considering both top-K and non-top-K candidates under randomness
In deterministic Top-K, only small subset of the (+) configurations are reachable due to consistent selection bias toward fixed group of neurons. However, RTK-WTA introduces variability across time, allowing significantly larger set of combinations to be explored. This probabilistic diver- sification reflects the spatiotemporal nature of CRINs-wWhee not only spatial configurations are considered by firing rate that integrates temporal information across all time steps, but also dig into the spatiotemporal dynamics by selectively activating neurons in each time step in a random way to extend the representation space.
When this mechanism is accumulated over a temporal window of T steps, the spatiotemporal feature space becomes exponentially rich:
[
aK
(6)
(1 a) K)
where each time step contributes multiplicatively to the total space due to the evolving trace states. Such expansion directly translates to enhanced generalization capability, as the SNNs can develop more specialized and nuanced temporal feature detectors.
In spiking networks, such dynamic trace- based modulation not only increases feature dimensionality but also enables the model to encode fine-gn mne-grained temporal correlations across inputs. Therefore, the stochastic selection via RTK-WTA enhances generalization by both increasing representational diversity and enriching the spatiotemporal discriminative capacity.
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Generalization capability can be theoretically linked to the minimal inter-class margin in the fea- ture representation space. Consider n different classes, represented by class class-specific trace vectors Wi...., Wn after training. The minimal inter-class margin dw between any pair of classes (W,, W)) defined as
dw min W. Wzl2-
(f)
With the temporal RTK-WTA mechanism, the effective inter-class margin dRTK is scaled by the increased feature space volume. Specifically:
V eff
KT-1
aRTK dw
x
(8)
n2
which is significantly larger compared to deterministic Top-K scenarios, thus substantially reducing the generalization error.
Thus, by integrating trace-based temporal dynamics and randomized neuron selection, the proposed RTK-WTA SNNs not only increase the effective feature space but also improve the robustness of learning dynamics, jointly contributing to enhanced generalization capabilities of SNNs.
3 RESULTS
In this section, we evaluate the performance of the proposed RTK-WTA SNNs for continual learning tasks. The ablation studies, robustness validation, and neuronal selective activation analysis are conducted to validate the effectiveness of our model.
3.1 PERFORMANCE COMPARISON
Our evaluation framework incorporates multi- ple baselines to enable meaningful architectural comparisons, including Flymodel [hhen et al. (2021). SDMLP Bricken al. (2023), EWC Bricken et al. (2023); Kirkpatrick et al. (2017) and SA-SNN Shen al. (2024). The SDMLP baseline provides a particularly insightful ref- erence point- as an ANN with fully connected Top-K sparse activation, it shares our RTK-WTA SNN's core architectural principles while oper- ating in the non-spiking domain. This structural similarity allows us to isolate the benefits of our spiking temporal dynamics while avoiding the confounding effects of convolutional or atten- tion operations. The validity of SDMLP comparative benchmark is further supported by its established use continual learning litera- ture Van de Ven et al. (2020).
100
800
Classes 50
Figure 2: The performance comparison between our model and some other baseline models on the splitCIFAR10 dataset. The horizontal axis repre- sents the number of classes encountered as tasks increment, and model performance measured using accuracy.
We comprehensively evaluate the proposed RTK-WTA mechanism against state-of-fe offie--f-theatt art continual learning methods under similar archi- tectures across three benchmark datasets: SplitMNIST, SplitCIFAR10, and splitCIFAR As shown Table D. our method achieves superior performance both standalone and E.C-enhanced con- figurations, demonstrating its effectiveness in mitigating catastrophic forgetting while maintaining computational efficiency.
Standalone Performance.
The proposed RTK-WTA mechanism outperforms all baseline methods across all datasets. On splitMNIST, achieves 60.37% accuracy, surpassing the best baseline (trace- based SA-SNN: 60.06%) by 0.31%. The advantage becomes pronounced complex tasks: for splitCIFAR 100, RTK- WTA achieves 32.91 % accuracy, exceeding the second- best method (Trace- based SA-SNN: 22.86%) by 10.05%. This aligns with theoretical analysis in the method section:

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Table Performance comparison among different models with the similar architectures.
Methods
SplitMNISTT
spliCCFARRT00
None
19.96 (+/-0.01)
25.29 (+/1.977
4.28(+/-0.38)
Joint
97.72 (+/-0.04)
92.19
65.28(+/-0.15)
EWC-SNN
19.89 (+/-0.00)
(+-2.6))))))5)
3.93(+/-0.24)
MAS-SNN
19.91 (+/-0.01)
(4-4.66)))
3.09(+/-0.45)
SDMLP
46.67 (+/-3.92)
73.27 (+/-1.28)
FlyModel
76.97 (+/-1.26)
70.09 (+/-0.51)
17.25 (+/-0.42)
Rate-based SA-SNN
50.22 (+/-0.91)
76.88 (+/-2.12)
21.37(+/-0.77)
Trace-based SA-SNN
60.06 (+1-2.16)
77.73 (+/1.995)
22.86 (+/-0.65)
Randomized Rate K-WTA
48.15 (+/-2.02)
76.11 (+/-0.78)
20.76 (+/-1.51)
RTK-WTA
60.37 (4/-
78.37 (.99
32.91 (+/-0.66
SDMLP EWC
79.61 (+/- 2.46)
78.64 (+/ 0.30)
21.31 (+/-0.72)
SA-SNN + EWC
82.18 (+/- 1.14)
80.39 (+- 1.84)
36.47 (+/ 2.13)
Randomized Rate K-WTA + EWC
80.12 (+/- 0.95)
79.97 (+/-1.01)
22.21(++-2.37)
RTK-WTA EWC
85.25 (+/- 0.67)
80.56 (+/- 0.66)
41.46 (+/- 0.23)
the expanded feature space volume in @quaaiion@enabllee better preservation of task-specific features through stochastic neuron selection. Notably, rate-based approaches (e.g. Random Rate-basee Top K) underperform by 12.2% on splitCIFAR confirming the critical role of temporal dynamics in trace-based methods for high-dimensional tasks.
Integration with EWC. When combined with EWC, method (Random Trace-based TopK + EWC) achieves state-of offte-of-thc-rtt results: 85.25% on SplitMNIST (+3.07% over SA-SNN + EWC) and 41.46% SplitCIFAR 100 (+5.0% over SA-SNN EWC). This synergy arises from two complemen- tary mechanisms: (1) EWC protects critical synaptic weights identified by Fisher information, while (2) RTK-WTA SNN's stochastic masking (equation 3 prevents overfitting to noisy or task-specific connections. The gradient noise variance Var(A& x K(d-K) further regularizes optimization, enabling robust learning across tasks. In contrast, deterministic Top-K variants (e.g.. Trace-based SA-SNN + EWC) exhibit degraded performance on splitCIFAR (36.47% vs. 41.46%), their fixed neuron selection amplifies weight interference.
a(1-o)
Moreover, the performance gap between RTK-WTA and baselines correlates with margin analysis: the inter-class margin dRIK expands by [I-a][d-K] compared to deterministic Top-K. This explains why RTK-WTA achieves 32.91% % on SplitCIFAR100 versus 22.86% for Trace-based SA- SNN the larger margin better separates 100-class representations.
oK
In addition, when benchmarked on SplitCIFAR10, 10, the class accuracy curve of our RTK-WTA in Figure Tigure[sshwwe shows the robust retention of the learned knowledge in the incremental learning process, and shows the competitive performance of our model compared with the baseline. As time goes by, it always maintains the recognition ability of all learning classes. The sustained high accuracy across all learned classes (ranging from initial to most recent tasks) suggests effective prevention of catastrophic forgetting, while the competitive new task learning speed indicates maintained plasticity.
3.2 INFLUENCE OF DIFFERENT LEVELS OF RANDOMNESS IN RTK-WTA
The impact of the randomness coefficient a in our RTK-WTA SNNs systematically evaluated across four configurations: standalone/EWCC-ee Ceeehancce randomized rate-based and trace-based models. As shown in Figure 3, all methods exhibit a consistent trend: performance first improves with increasing a, peaks at a 0.1, then declines a exceeds this optimal value. This non- non-mononn monotonot behavior validates our theoretical framework connecting controlled randomness to spatiotemporal feature learning in SNNs.
Performance Under Different Levels of Randomness. The performance peak at = 0.1 aligns with (d-K) K) reaches
our derived feature space expansion. When a 0.1, the expansion factor (d)/(1addddall an optimal balance: sufficient diversity to capture spatiotemporal patterns (improving accuracy by 1.3% over a for Random-k k_spp),, yet preserving the core Top-K selection mechanism.
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Table The robustness comparison of different models on different datasets.
CIFARTO dataset
Noise levels
0
0.1
0.2
0.5
0.6
0.8
Rate-based SA-SNN
76.88
69.59
66.80
65.40
43.58
37.77
Rate-based SA-SNN EWC
79.86
75.10
72.14
69.75
57.80
53.65
Trace-based SA-SNN
77.73
76.76
76.73
74.66
61.85
58.95
Trace-based SA-SNN EWC
80.39
79.88
79.45
78.86
70.84
59.69
Randomized Rate K-WTA
76.11
74.64
73.3
66.42
65.45
54.28
Randomized Rate K-WTA EWC
79.97
77.91
74.97
71.26
70.87
62.77
RTK-WTA RTK-WTA + EWC
78.37
76.92
77.25
75.78
66.6
61.36
80.56
80.12
80.14
77.96
73.06
68.85
CIFARTOW 100 dataset
Noise levels
0
0.1
0.2
0.5
0.6
0.8
Rate-based SA-SNN
21.37
20.03
19.11
14.36
11.61
9.16
Rate- SA-SNN + EWC
21.88
20.36
19.65
16.75
13.59
9.88
Trace-based SA-SNN
22.86
23.10
21.06
16.12
12.28
9.23
Trace-based SA-SNN EWC
36.47
33.56
31.24
26.32
17.63
10.56
Randomized Rate K- KTWA
20.76
20.15
19.45
16.21
13.93
9.82
Randomized Rate K-WTA EWC
22.21
20.45
20.56
17.31
16.04
10.06
RTK-W WTA
32.91
31.49
31.14
28.63
23.19
13.34
RTK-WTA EWC
41.46
39.67
37.64
32.42
26.37
17.69
When 0.1, performance declines sharply (e.g., -14.47% drop for Random-k.n k_sep at 0.5). This occurs because excessive randomness violates the theoretical condition for effective K- WTA operation. At = 0.5, nearly half of the selected neurons come from non- MoonTop-KK K regions, disrupting the temporal coherence resulting the irregular effective neuron selection.
86
Acsumey
Performance Changes Under Different Ran- domness with EWC. The EWC-enhanced vari- ants exhibit flatter degradation curves, with RTK- -TA SNNs with EWC maintaining 76.31% % accuracy even at a 0.5 (-4.25% from peak). This demonstrates EWC's comple- mentary role: by constraining Fisher-important weights, it mitigates the destabilizing effect of high a, though cannot fully compensate for fun- damental K-WTA mechanism breakdown. The
Ranber bor ENC
RIX 3icc TAENC
Abpla
Figure 3: The influence of different levels ran- domness in RTK-WT WTA SNNs. WNNsCComparttive analy- sis reveals that all methods attain superior perfor- mance when is set to 0.1.
a(l-a) Σ.,, gradient noise variance of Var(A0nois)) K[d-K)
9L
0tr.lt] 08
0tr.
= 0.2, beyond which the a(1 - a:) term decreases while misselection dominates.
Therefore, our results establish
0.1 as the empirically optimal setting, achieving 1.64% improve- ment over deterministic Top-K (a = 0) and about 5.8x slower degradation than rate-based methods
a 0.5. That balances the dual needs of feature diversity and temporal stability, making our WTA-RTK SNNs adaptable to various neuromorphic scenarios without parameter tuning overhead.
3.3 ROBUSTNESS OF RTK- WTA SNNS
We evaluate the robustness of the proposed SNN with KTK-WTA SNNs against deterministic Top- variants under two scenarios: (1) training with noise and testing without noise, and (2) training without noise and testing with noise. Noise is injected during training by randomly corrupting a fraction (Noise Level) of the Top-K neuronal connections, simulating unstable synaptic transmission.
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80
Assumicy
60
0.1
0.2
0.5
0.8
0.5
0.5
Noisa Lovel
Noisa Level
Figure 4: The robustness of the proposed RTK-WTA SNNs. on training dataset. (b) Adding different levels of noises test dataset.The results show that our RTK-WTA+EWC consistently outperforms all other methods across all tested noise levels.
(a) Adding different levels of noises
The results splitMNIST under incremental class learning demonstrate the superiority of RTK-WTL in handling both training and testing noise.
As shown in the Figure (a), RTK-WTA SNNs with EWC exhibit significantly smaller performance degradation as noise increases. For instance, our TTK-WTA SNNs with EWC decline by only 14.56% (85.25% + 70.69%) from Noise=0 to 0.8, whereas deterministic trace-based K-WTX with EWC without randomness drops by 12.91 % (82.47% -> 69. (955.. This resilience stems from the expanded feature space volume, which enables RTK-WTA to learn noise-invariant representations by stochastically masking neurons during training. The trace decay dynamics in RTK-WTA SNNs further stabilize temporal integration, reducing sensitivity to corrupted connections. In contrast, rate-based K-WTA SNNs with EWC SNNs suffer catastrophic failure at high noise (42. 19% at Noise=0.8), static rate coding lacks temporal adaptability.
Figure 4 (b) reveals that RTK- WTA SNNs generalize better to unseen noise. our RTK WTA SNNS with EWC maintain 24.04% accuracy margin over trace-based K-WTA SNNs with EWC at No (61.21% vs. 60.48%), despite both using trace-based selection. This advantage arises from gradient noise induced by RTK-WTA, where the variance term promotes convergence to flatter minima, inherently robust to input perturbations. The stochastic masking during training mimics test-time noise, effectively acting as implicit regularization. Meanwhile, deterministic trace-based K-WTA SNNs with EWC without randomness overfit to clean training data, leading to sharper minima and faster performance collapse under noise.
Furthermore, we conduct robustness validation experiments under increasing noise perturbation levels on CIFAR-10. 10, CIFARION 100 and Tiny ImageNet (See supplementary materials). Only the results of adding noise to the training set are displayed in Table 2 These results validate that RTK-WTA SNNs consistently outperform deterministic WTA baselines and show higher resilience across all tested noise levels.
The results align with our theoretical analysis that RTK-WTA SNNs' stochastic neuron selection expands the effective feature space and enhances the robustness of SNNs for continual learning. Combined with temporal trace dynamics, this mechanism mitigates the double curse of noise- corrppeed training connections and shifted test distributions. Practically, RTK-WTI SNNs requires no additional modules or hyperparameters beyond a, making scalable for neuromorphic applications.
3.4 NEURONAL SELECTIVELY ACTIVATION ANALYSIS.
Visualization of the Neuronal Distribution. Analysis of neuronal selectivity distributions in Figure reveals that while the rate-based K-WTA SNNs effectively activate task-relevant neurons across all categories, they require a substantially larger population of highly selective neurons. In contrast, the trace-based K-WTA SNNs achieve superior performance with fewer specialized neurons, demonstrating significantly improved neural resource utilization efficiency by considering the temporal dynamics. Furthermore, our RTK-WTA SNNs maintain remarkable consistency with trace-based K- K-WTA SNNs in the neuronal activation patterns, indicating that the incorporation of stochastic mechanisms not only preserves balanced neuronal selectivity distributions but also enhances performance while maintaining comparable resource efficiency.

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1000
1030
000
100
Neuoo kvder
600
402
400
200
12
15
71
12
71 12
15
Figure 5: Neuronal selectivity analysis among different SNNs, each color represents the activated neurons associated with one of the 10 class labels.
Quantitative Analysis of Neuronal Activation Coverage. quantitative metric termed Neuronal Activation Coverage is proposed, which represents the proportion of neurons that exhibit significant activation (i.e.. an activation count exceeding predefined threshold) throughout the entire continual learning process. We computed the neuronal activation coverage on the CIFAR-10 classification task, using an average firing rate of at least 0.3 spikes per sample as the threshold to determine significant activation for each neuron. The results show that RTK-WTA significantly increases neuronal utilization across the network (45.2% vs. 28.5% for deterministic Trace-based K-WTA). This indicates that a more diverse set of neurons participates in feature representation for different tasks, rather than relying on a fixed subset of repeatedly activated neurons.
4 CONCLUSION
In this work, we introduced Randomized Temporal Winner-Take-All (RTK-WTA), a biologically inspired selective activation mechanism for spiking neural networks in continual learning. By leveraging temporally accumulated neuronal traces and probabilistic competition, RTK-WTA enables context-aware, sparse activation that dynamically adapts evolving task sequences. This mechanism not only expands the effective spatiotemporal feature space but also enhances representational diversity and robustness, allowing the network to better mitigate catastrophic forgetting. Empirical results across multiple benchmarks, including SplitMNIST, SplitCIFAR-10, and SplitCIFAR 100, show that our approach outperforms traditional deterministic Top-K baselines by a significant margin while demonstrating strong resilience to noise and task interference. The success of RTK-W WTA highlights the potential of integrating temporal selectivity with stochastic activation to achieve biologically plausible and scalable lifelong learning in neuromorphic systems. Restricted to relatively shallow networks in its current form, extending this framework to deeper architectures to enable robust representation learning for complex continual tasks is a priority for future work.
ACKNOWLEDGEMENT
This work was supported by National Key Research and Development Program of China (Grant No. No.2023YFFB4055000 National Natural Science Foundation of China under Grant NN.66236, 62476035, 62436005, 62473303, U24B20140), China Postdoctoral Science Foundation under Grant Number (No. GZB20250394, 2025T180427, 2025M771543).
ETHICS STATEMENT
All authors disclosed no relevant relationships. No potential conflict of interest reported by the authors. This article does not contain any studies with human participants or animals performed by any of the authors, therefore ethical approval not required.
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REPRODUCIBILITY STATEMENT
Efforts to ensure reproducibility include: Detailed experimental configurations, model architectures, and hyperparameters provided in the appendix. The source code will be released publicly upon acceptance of this paper.
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A APPENDIX
A.1
RELATED WORK
More detailed related works are summarized as follows:
SNNs for Continual Learning The pursuit of continual learning in SNNs draws inspiration from biological systems that demonstrate lifelong learning capabilities Kandel & Hawkins (1992). Recent advances include Antonov et al. (022)** 's unsupervised framework combining Langevin dynamics with STDP for synaptic importance estimation and Skkacchkossky et al. (2022) 's Bayesian online learning rule for dynamic environments. Evolutionary strategies were employed by Hammouamri ct al. (2022) to optimize auxiliary networks for dynamic threshold adjustment, while Tadros et al. (2022) introduced local plasticity with coding conversion to mitigate biases. These approaches benefit from SNNs' inherent temporal dynamics Maass (1997) and energy efficiency Pei et al. (2019). though challenges remain in achieving biological- flexibility Kudithipudi et al. (2022).
Winner-take-all circuits in SNNs implement biological inhibition principles observed in neural systems Lin al.] (2014); Douglas & Martin (2004). [ynch al.] (2019) developed a stochastic implementation achieving O(1) convergence through adaptive inhibition, while (Su et al.] (2019) derived theoretical limits for k-WTA circuits. The biological plausibility is further supported by Shukla et al. 2019)'s 's work combining hard WTA for Gibbs sampling and soft WTA for approximate inference. Challenges in temporal implementation have prompted solutions using STDP rules Markram et al. (1997) and trace variables Morrison et al. (2008). addressing issues like homogencous spike patterns Shen et al. (2021) and neuron death Ahmaa & Scheinkman| (2019). Notably, the sparse activation property of SNNs based on temporal trace-based K-WTA [Shen et al. (2024) naturally reduces memory interference and enhances the feature representation ability of SNNs, resembling biological context gating mechanisms Kay & Laurent (999).. These developments mirror biological neuronal variability Izhukevich| (2004) while advancing neuromorphic computing.
In addition to architectural or activation-level mechanisms, recent works have examined optimization and initialization strategies to stabilize deep SNN training Ding et al.) (2025).
Selective Activation Mechanisms Biological selective activation operates through multi-scale mechanisms. At the circuit level. Douglas & Martin (2004)* 's soft WTA model demonstrates how pyramidal-interneuns competition enables input-ddptivve selection, while/Buzsáák (2010)'s 's reader- actor framework explains assembly selection via dynamic synapsembles. Molecular-Ievel evidence comes from Imokawa & Brockes (2003)'s work on thromboxane activation regeneration. These processes are modulated by context-dependen mechanisms(Levinsss et al. (2020) that inspire artificial implementations Kudithipudi et al. (2022). Crucially, biological systems achieve this through sparse activation patterns Stevens (2015) and specialized inhibitory circuits Lin et al. (2014), principles now informing SNN designs Shen et al. (003).
Biological selective activation principles have inspired three technical implementations in neural networks. Wang et al.] (2023)'s ' AMU with SAPW/SAPI strategies reduces computation while maintaining accuracy, paralleling biological energy conservation [mam & Cleland (2020). Geifman & El- (217))ss 's rejection mechanism enables risk-controlled prediction, akin to biological decision circuits Buzsáki 2010). Hardware solutions like Wang et al. (2020)' 's self-selective memory devices achieve efficiency gains while avoiding common pitfalls like neuron death |Fduu et al. (2022). These artificial systems complement conventional approaches Kirkpatrick al. (2017): y Rebuffi al. (2017)
by incorporating biological constraints McCloskey & Cohen 1989), though challenges remain in fully capturing temporal dynamics Dan & Poo (2004).
A.2 SUPPLEMENTARY EVALUATION OF NOISE ROBUSTNESS
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Table 3: The noise experimental results on Tiny-imageNet dataset.
Tiny ImageNet dataset
Noise levels
0
0.1
0.2
0.5
Rate-based SA-SNN
24.71
24.15
23.54
20.34
Rate-based SA-SNN + EWC
28.12
27.68
26.78
21.50
Trace-based SA-SNN
31.25
30.77
30.59
22.03
Trace-based SA-SNN EWC
35.50
33.89
32.97
27.00
Randomized Rate K-WTA
29.06
28.36
27.09
20.80
Randomized Rate K-WTA EWC
31.49
30.24
26.8
22.23
- RTK-WTA + EWC
37.42
36.87
37.34
35.07
43.17
40.19
39.22
36.54
The supplementary experimental results of the noise robustness on Tiny-ImageNet dataset are supplemented shown in Table
A.3 ENERGY ESTIMATES
In SNNs, fewer spikes generally equate lower energy usage. We provide quantitative comparison of energy consumption across different models when performing inference on a single CIFAR-100 image with time window of 16 steps.
Table 4: Energy consumption comparison between SNN and ANN models
SNN Metrie
RTK-WTA
Randomized Rate K-WTA
Trace-based SA-SNN
Total Spike Activity
2.27E-08
2.18E-08 J
2.27E-08 J
TOP-K Selection
6.72E-09
1.70E-10 J
6.72E-09) J
Randomization Control
6.40E-09 J
2.00E-09) J
Total Energy (per image)
3.58E-08 J
2.40E-08 J
2.94E-08 J
Rate-based SA-SNN
Pure SNN
SDMLP (ANN)
ANN Metric
2.18E-08
2.07E-08
6,344,000 ops
MACs
1.J
0
12,722,590 ops
FLOPs
2.92E-05
Total
2.20E-08 J
2.07E-08
2.92E-05 J
Total Energy
As Table 4 shows, RTK-WTA consumes only 3.58E-08 which is comparable the SNN baselines.
such as Trace-based SA-SNN (2.94E-08 J). More importantly, the energy cost is orders of magnitude lower than standard ANN models (e.g.. SDMLP: 2.92E-05 J), indicating the approach remains highly energy- mefry-efficientt despite incorporating trace dynamics.
A.4 ABLATION EXPERIMENT
We conduct systematic ablation studies to precisely disentangle the contribution of each component. The results are summarized in the Table 5 (all methods use EWC).
(a) Role of Stochastic Selection: Comparing RTK-WTA with its deterministic version (setting
a=0), the removal of stochasticity leads to performance drop, particularly evident on the complex
Table 5: Ablation experiment results on different datasets.
Method
MNIST CIFARTO
CIFAR 100
RTK-W (Full Model)
85.25
80.37
41.46
(a) probabilistic selection
82.18
80.09
36.47
(b) - trace
80.12
80.02
22.21
probabilistic selection & trace
80.22
79,86
19.88
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Table 6: The supplementary experimental results incremental MNIST dataset.
Incremental MNIST dataset
Method
Avg-acc
Rate-based SA-SNN
40.9
Trace-based SA-SNN
40.3
Rate-based SA-SNN + EWC
69.4
rrace-based SA-SNN EWC
78.9
Randomized Rate K-WTA
44.2
RTK-WTA Randomized Rate K-WTA + EWC
45.2
72.5
RTK-WTA + EWC
79.5
SplitCIFRRON 100 with decrease of 5.0%. This confirms that stochastic selection is erucial for mitigating interference between complex tasks.
(b) Role of Trace Modulation: Replacing the Trace-based selection with Rate-based selection causes a sharp performance decline, with dramatic drop of 19.25% on SplitCIFAR 100. This firmly establishes the critical role of trace modulation in capturing temporal dynamics and enhancing continual learning performance.
The ablation studies clearly quantify the independent contribution of each component and their synergistic effect, providing strong support for our methodological design.
A.5 EVALUATION IN CLASS-INCREMENTAL SCENARIOS
We conduct additional experiments on class-incremental continual learning, which presents a more realistic and demanding setting than the task-incremental scenario originally reported. Our results on incremental MNIST (iMNIST), as shown Table 6 demonstrate that RTK- -TAA maintains its superiority
deterministic K-WTA basclines even under Class-IL conditions, achieving accuracy improvement of approximately 4.9%. Furthermore, when combined with EWC, our method continues to deliver the best performance. These findings strongly support the generalizability and robustness of the RTK-W -WTA mechanism across different continual learning paradigms.
A.6 T-SNE VISUALIZATION ANALYSIS.
(a) RTK-WTA
(b) Trace-based K-WTA
Figure 6: t-SNE visualization results comparing RTK-WTA and trace-based K- K-WTA
We perform I-SNE visualizations the feature space for CIFAR-I0's 10-class classification task during testing. Figure 6 (a) shows class clusters for RTK-WTA, while Figure T (b) displays those for the deterministic K- WTA baseline. The visualization clearly demonstrates that RTK-WTA forms more discriminative and well-separated class clusters in the feature space, exhibiting superior feature separation compared to the deterministic K-WTA baseline, which shows partial distribution aliasing.
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Table 7: Experimental results on Tiny ImageNet dataset.
Tiny ImageNet dataset
Method
Acc (%)
Rate-based SA-SNN
19.26
Trace-based SA-SNN
23.22
Rate-based SA-SNN + EWC
17.83
brace- SA-SNN EWC
28.78
Randomized Rate K-WTA
22.25
RTK-WTA Randomized Rate K- K-WTA EWC
26.42
25.56
RTK-WTA + EWC
32.94
A.7 SUPPLEMENTARY END-TO-END EXPERIMENTS
To comprehensively evaluate the scalability and generalization capability of our method, con- ducted additional experiments using an end-to-end SNN-ViT architecture the more challenging Imayeet dataset. With 200 classes and higher image resolution, TinyImageNet provides significantly larger and more complex benchmark for continual learning compared to the CIFAR series.
The results presented in Table confirm the RTK-WTA mechanism can be effectively integrated into deep spiking neural networks, achieving a performance improvement of approximately 4.2% over deterministic Top-K baseline. Moreover, RTK-WTA consistently outperforms all baseline methods this challenging setting. Notably, when combined with EWC, RTK-WTA attains an accuracy of 32.94%, substantially surpassing the strongest trace-based deterministic bascline (Trace-based SA-SNN + EWC at 28.78%).
These findings strongly validate the excellent scalability and generalization capacity of the RTK-WTA mechanism, confirming its efficacy handling large-scale, multi-class continual learning tasks.
A.8 SUPPLEMENTARY THEORETICAL ANALYSIS
We analyze the information gain introduced by the stochasticity in the RTK-WTA selection, while holding the trace mechanism constant.
To provide more formal foundation for the benefit of random trace-based top-k selection in RTK- WTA, we present a derivation based on mutual information (MI) under entropy constraints.
In detail, further analyze the information gain introduced specifically stochasticity in the RTK-WTA selection, holding the trace mechanism constant.
Let X Id be input feature vector of traces in RTK-WTA. Y denotes the output label task. S c (1....,d) denotes selected neuron indices (top-k), while = =Xs are the activations of the selected neurons.
We analyze the mutual information 1(2; Y), which quantifies how much information the se- lected neurons convey about the task output. We apply standard lower bound I(ZYY) > Esix log ( H(Y) where 2 is variational approximation of the true posterior.
This implies maximizing the likelihood correct prediction encourages high mutual information between selected features and output.
Instead of deterministic top-k selection, since we design the RTK-WTA SNN with a stochas- tic selector X(S)X), the mutual information under stochastic selection becomes 1(Xs; = [s=-P(sjx)[[(X;; Y)).
We optimize the selector via: maxp(six) Es(T(Xs; Y)) (H((S()))). -
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This balances information preservation with selection diversity (controlled via entropy). ZTK-WTA approximates this
sampler. Let:
SoftTop (i) if E Top-d
P(i E S|x)
otherwise
where a controls the sampling temperature. When a - 0, it satisfies uniform random selection (high entropy) while a oc, it satisfies the deterministic top-k (zero entropy). Thus, RTK-WTA enforces selectivity while promoting diversity across tasks.
Let ZRTK X) represent RTK-WTA selection, and Zx top-K((X) represent the deterministic K-WTA.
Then R(TKK Y) T(Zk; Y), under continual learning conditions, where overlapping tasks create interference.
RTK-WTA enables richer representational modes and higher task specifieity through stochasticity.
Therefore, the proposed RTK-WTA SNNs can be interpreted as an entropy-regularized top-k mech- anism that increases mutual information by expanding the selection space over time, avoiding representational collapse and improving robustness to interference.
A.9
USE OF LLMS
Large Language Models(LLMs) were used solely to assist with polishing the text.
A.
CODE OF ETHICS AND ETHICS STATEMENT
The research conducted in the paper conform, in every respect, with the ICLR Code of Ethics https://icll wwtpc//ppwwccoccooooooooocccceee
A.11
EXPERIMENTAL SETUP
All experiments were conducted on a performmooe computing cluster with uniform hard- ware/software configurations to ensure strict reproducibility. The specific configuration is shown in the Table
Table 8: Hardware and Software Environment
Item
Configuration
Operating System
Linux (Ubuntu 22.04 LTS)
CPU
Intel Xeon Gold 6248R (3.0GHz, 48 cores) NVIDIA RTX 4090 (24GB VRAM)
GPU
Python
3.10.16
PyTorch
2.6.0+cu115 18
CUDA
11.8
cuDNN
9.1.0
1.12 MODEL TRAINING CONFIGURATION
Our training strategy combines unsupervised RTK-WTA pre-activation with gradient-based updates (when supervised labels are available), and integrates EWC for synaptic consolidation. We use surro- gate gradient descent for training spike-based based modules, following standard SNN training practices in Wu et al. (2018). The detailed architectural diagrams, training schedules, and evaluation protocols are described as follows:
For MNIST experiments, employed two-layer fully connected network architecture, consisting of an input layer followed by a hidden layer with 1000 ReLU-activated nodes, with task-specific output layers (one per binary classification task, totaling 5 tasks) trained end-to-end.
For CIFAR10 and CIFAR100 experiments, we first extracted image embeddings using ResNet-18 backbone, then fed them into two-layer fully connected network. The CIFAR10 model featured
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Published as a conference paper at ICLR 2026
binary classification output layers (5 tasks total), while the CIFAR100 model utilized a larger hidden layer (2000 nodes) and 4-class output layers (25 tasks total). We conducted two experiments on the Tiny ImageNet dataset: the first employed pre-trained ViT model for feature extraction, where the resulting embeddings were processed by a two-layer fully-connected neural network to enable comparative analysis of different methods; the second adopted end-to-end approach by constructing a custom ViT-style feature extractor that utilizes convolutional layers for 16x16 patch embedding, incorporates learnable [CLS] tokens and positional encodings, processes features through 4-layer Transformer encoder (with 3 attention heads per layer, 384 feed-forward dimension, and 0. dropout rate), and ultimately outputs normalized [CLS] token features (992-dimessional) with subsequent classifier training following the same procedure before to enable simultaneous optimization of both feature extractor and classifier. Various methods were implemented combination with either EWC regularization or sparsity-inducing mechanisms (such as Top-K Trace) to alleviate catastrophic forgetting during continual learning.
Besides, for the MNIST experiments, we directly used raw images as input to train and compare different continual learning methods under sequential task paradigm. Similarly, the end-to-end TinyImageNet experiments processed raw images through instantiated End2EndContinuall.cu (comprising both feature extractor and classifier), following the same sequential task training proce- dure. In contrast, the 11FAR10/100 and ImnyeetgeNet embeddings experiments adopted two-phase training pipeline: (1) a feature extraction phase where pretrained models generated image embeddings, followed by (2) classifier training phase with frozen feature extractors to enable continual learning in the fixed representation space.
The parameter settings of our proposed method are detailed in Figure and Figure 10 Among them,the À parameter controls the weight of the EWC loss term in the total loss function, where lower values permit greater parameter updates. The 3 parameter regulates the smoothing intensity of the diagonal elements in the Fisher information matrix. The Adaptation Speed (T) controls the decay rate of neuron threshold adjustments, with higher values resulting slower adaptation, which is tuned to balance plasticity and stability: The 1.2-Norm scale(7)) is used to control the scaling magnitude after weight normalization, balancing the weight magnitudes across neural network layers.
Table Training Parameters for CTK-WTA-EWC
Parameter
MNIST
CIFARIO
CIFARI00
Iterations
20,000
20,000
8,000
Batch Size
64
512
256
Learning Rate (1))
0.05
0.05
0.08
X(EWC)
400
200
100
((EWC)
0.08
0.08
0.08
Adaptation Speed (7)
1.0 x 106
2.0 x 106
2.0 x 106
1.2Norm Scale (7)
1.00
0.25
0.25
Optimizer
SGD
SGD
SGD
Table 10: Training Parameters for Randomized Rate K- K-WTA+EWC
Parameter
MNIST
CIFAR10
CIFAR100
Iterations
20,000
20,000
8,000
Batch Size
64
512
256
Learning Rate (17)
0.05
0.05
0.08
X(WWC)
400
200
100
(EWCC)
0.08
0.08
0.08
Adaptation Speed (T)
1.5 x 106
2.0 x 106
2.0 x 106
Optimizer
SGD
SGD
SGD
19


Reviews:
[{"review_id": "E5Azytfs2Y", "reviewer": "Reviewer_X1JP", "summary": "This paper addresses two core challenges in CL for SNNs: (1)catastrophic forgetting caused by overlapping task activations, and (2) insufficient robustness to input perturbations. \nDrawing inspiration from the brain’s temporal selectivity and stochastic neuronal competition, it proposes the Randomized Temporal K-Winner-Take-All（RTK-WTA） mechanism. RTK-WTA mechanism integrates temporally accumulated neuronal traces with probabilistic top-k selection to dynamically allocate neural resources while introducing controlled randomness.", "questions": "(1) Have you considered more complex CL scenarios (such as class-incremental or domain-incremental), and how RTK-WTA performs in these scenarios?\n(2) Could you provide a more systematic analysis of the relationship between the randomness coefficient α and task characteristics, to guide parameter tuning?\n(3)Can the neuronal selectivity analysis be extended to better support the claims of improved robustness and diversity?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "(1) Originality: It integrates temporal neuronal traces with probabilistic top-k selection for SNN continual learning, differing from traditional rate-based or static trace-based K-WTA, and aligns well with biological neural mechanisms;\n(2) Quality: The motivation is reasonable; Theoretical derivations strengthen the work’s rigor. But the experimental design is not comprehensive enough;\n(3) Clarity: The writing of this paper is relative clear. For example, this paper has clear definitions of core concepts and step-by-step explanations of the RTK-WTA mechanism;\n(4) Significance: RTK-WTA provides a new idea and practical solution for robust continual learning in SNNs.", "weaknesses": "(1) The experimental scenario of continuous learning is limited: The experiment only adopted the \"task splitting\" continuous learning paradigm, and did not verify more challenging scenarios.\n(2) NEURONAL SELECTIVELY ACTIVATION ANALYSIS(3.4) is too simple and lack the depth.\n(3) Although experiments identify α=0.1 as the optimal value, there is no quantitative analysis of how α correlates with task characteristics (e.g., task overlap, data dimensionality), making it hard to guide parameter tuning for new tasks;", "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": "B6yBdBe74c", "reviewer": "Reviewer_6ET9", "summary": "The paper proposes a biologically inspired Randomized Temporal K-Winner-Take-All (RTK-WTA) mechanism for spiking neural networks (SNNs) aimed at continual learning. The method introduces controlled stochasticity in neuron selection by combining temporal traces with probabilistic top-k activation, improving adaptability and robustness against noise. Experiments on SplitMNIST, SplitCIFAR-10/100, and Tiny-ImageNet demonstrate enhanced performance and resilience compared to deterministic K-WTA baselines.", "questions": "1.Can the proposed RTK-WTA mechanism be extended to deeper or hierarchical SNN architectures? How would temporal randomness interact with multi-layer trace dynamics?\n\n2.Have the authors considered testing on larger datasets to further evaluate scalability and generalization performance?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "1.The proposed RTK-WTA mechanism is both innovative and simple to implement, integrating biological plausibility with computational efficiency.\n\n2.The method exhibits strong noise robustness, as shown in Table 2 and additional Tiny-ImageNet results, maintaining high accuracy under perturbations.\n\n3.The approach effectively balances sparse activation, adaptability, and robustness, offering a promising direction for scalable continual learning in neuromorphic systems.", "weaknesses": "1.In Table 2, key results demonstrating robustness could be bolded or highlighted to emphasize the superiority of the proposed method.\n\n2.Further validation on larger or more complex datasets (e.g., Tiny-ImageNet or ImageNet-scale continual tasks) would strengthen the claim of generality and scalability.", "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": "mrhEFIwQcf", "reviewer": "Reviewer_dDpf", "summary": "The paper introduces a new biologically inspired mechanism called Randomized Temporal K-Winner-Take-All (RTK-WTA) to improve continual learning in spiking neural networks (SNNs). TK-WTA combines: the traces of recent spiking activity, and randomized neuron selection to dynamically choose neurons in a way that emulates biological neural selectivity and variability. Results show RTK-WTA achieves better performance on continue learning tasks than baseline methods.", "questions": "1. The paper claims that RTK-WTA expands the spatiotemporal feature space, yet the underlying theoretical reasoning is only briefly mentioned. Could the authors provide a more rigorous theoretical explanation and empirical validation (e.g., through visualization or quantitative analysis) to support this claim?\n\n2. How does RTK-WTA concretely enhance representational diversity within the network? It would be helpful if the authors could include additional experiments or visualizations to demonstrate this effect.\n\n3. To better isolate and understand the contribution of each component, could the authors perform ablation studies comparing (a) deterministic vs. probabilistic selection, and (b) models with and without trace-based modulation?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper attempts to connect the proposed RTK-WTA mechanism to biological processes of neural selectivity and stochastic activation, which strengthens the connection between neuroscience and neuromorphic computing and enhances its conceptual depth and interdisciplinary relevance.\n\n2. The proposed RTK-WTA method is novel and biologically grounded. Instead of relying on static, deterministic top-K firing rules, the model integrates temporal spike traces with controlled randomness, reflecting how biological neurons combine time-dependent excitation with stochastic competitive firing. This design allows the network to leverage temporal coherence in spiking activity while avoiding rigid specialization, which is a thoughtful and meaningful step toward more brain-like continual-learning systems.\n\n3. The authors evaluate the proposed method on multiple standard continual learning benchmarks (SplitMNIST, SplitCIFAR-10, SplitCIFAR-100), demonstrating consistent improvements over baselines. The results convincingly show that RTK-WTA improves both performance and robustness.", "weaknesses": "1. The paper may lack a clear and structured background section to help readers unfamiliar with continual learning understand the fundamental concepts, challenges, and motivation behind this work. Additionally, the figures lack detailed, self-contained captions, making it difficult to interpret their meaning without referring back to the main text.\n\n2. The discussion of prior literature, particularly in the context of continual learning within SNNs, is relatively insufficient. Important prior works are mentioned but not critically analyzed, making it difficult to discern how the proposed method advances beyond existing approaches or fills specific research gaps. As a result, the novelty and significance of the contribution are not made very explicit.", "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": "qPkUoSd4vN", "reviewer": "Reviewer_27Cb", "summary": "This paper introduces Randomized Temporal K-winner-take-all (RTK-WTA), a continual learning (CL) framework for Spiking Neural Networks (SNNs). The core idea is to integrate temporally accumulated neuronal traces with probabilistic top-k selection. Experimental results show that RTK-WTA outperforms state-of-the-art SNN continual learning methods across various tasks.", "questions": "1. Please explain the key role that the SNN classification head plays in the continual learning tasks and the reason why not employ an end-to-end SNN feature extractor or at least a pre-trained SNN backbone.\n2. Please add more background introduction in the main text.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. This paper provides detailed theoretical analyses of the effectiveness of the proposed RTK-WTA. It shows that the proposed RTK-WTA increases the effective feature space and improves the robustness of learning dynamics.\n2. RTK-WTA outperforms state-of-the-art SNN continual learning methods, especially on the splitCIFAR100 benchmark.", "weaknesses": "1. As detailed in Fig. 1 and Sec. A.7, feature embeddings are extracted by a pre-trained ANN backbone (ResNet-18, ViT), with the SNN component reduced to only the final classification head. This design obscures the true role and benefit of the SNN and the RTK-WTA mechanism. The most challenging part of the task (hierarchical feature extraction from raw data) is offloaded to a static, pre-trained ANN. It is unclear why the authors did not employ an end-to-end SNN feature extractor or at least a pre-trained SNN backbone.\n2. The background information in the main text is insufficient. The main text has only a limited background introduction, while the related work section is in the appendix. I recommend placing the related work section in the main text.", "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)