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arxiv: 2512.02459 · v2 · submitted 2025-12-02 · 💻 cs.NE

Neural Architecture Search of Time-to-First-Spike-Coded Spiking Neural Networks for Efficient Eye-based Emotion Recognition

Qianhui Liu , Jing Yang , Miao Yu , Trevor E. Carlson , Gang Pan , Haizhou Li , Zhumin Chen This is my paper

Pith reviewed 2026-05-17 03:09 UTC · model grok-4.3

classification 💻 cs.NE
keywords neural architecture searchspiking neural networkstime-to-first-spikeeye-based emotion recognitionneuromorphic hardwareenergy efficiencyevolutionary algorithm
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The pith

A neural architecture search framework discovers time-to-first-spike spiking neural networks that recognize eye-based emotions accurately while using far less energy on neuromorphic hardware.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to show that searching for the right network structure is key to making spiking neural networks practical for emotion recognition from eye data on small devices. Standard training methods have been the focus before, but here the architecture itself is optimized using an evolutionary search helped by a conventional neural network. This matters because eye-wear needs low-power ways to detect emotions without constant battery drain, and spiking networks that fire only once per neuron can be very efficient if built correctly. Experiments show the resulting networks perform well and use less energy when run on specialized brain-like hardware.

Core claim

TNAS-ER is the first neural architecture search framework for TTFS-coded SNNs in eye-based emotion recognition, using an ANN-assisted strategy with an evolutionary algorithm and recall-based fitness to discover architectures that deliver high recognition performance alongside significantly improved efficiency and superior energy efficiency on neuromorphic hardware.

What carries the argument

TNAS-ER, which employs an evolutionary algorithm guided by a ReLU-based ANN counterpart to optimize TTFS SNN architectures, with weighted and unweighted average recall as fitness objectives.

If this is right

  • TNAS-ER networks achieve high recognition performance for eye-based emotions.
  • The searched architectures have significantly improved efficiency over previous approaches.
  • Deployment on neuromorphic hardware confirms superior energy efficiency.
  • The ANN-assisted search stabilizes the optimization of the spiking networks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This search strategy might apply to other tasks requiring precise spike timing, such as audio or motion recognition on edge devices.
  • Future work could test if these architectures work across different hardware platforms beyond the one evaluated.
  • Combining this with other training methods for SNNs could further boost performance without manual tuning.
  • Scaling the evolutionary search to larger datasets may reveal more general design principles for efficient SNNs.

Load-bearing premise

The assumption that a conventional ReLU neural network can reliably guide the search for optimal spiking network structures without overlooking important timing details specific to eye emotion data.

What would settle it

If experiments without the ANN guidance produce networks with similar or better performance and efficiency, or if the found networks show no energy advantage on neuromorphic hardware, the value of the assisted search would be called into question.

Figures

Figures reproduced from arXiv: 2512.02459 by Gang Pan, Haizhou Li, Jing Yang, Miao Yu, Qianhui Liu, Trevor E. Carlson, Zhumin Chen.

Figure 1
Figure 1. Figure 1: Comparison of TNAS-ER with state-of-the-art eye [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: TTFS SNN and its neural dynamics. membrane potential phases. As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: ANN-assisted TTFS SNN search strategy in TNAS-ER: (a) Supernet training with a ReLU ANN counterpart sharing the same [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Evolutionary search quality comparison between TTFS [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Subnet retrain quality comparison across different strate [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Hierarchical search space. Left: Macro-level backbone [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Performance comparison with SNNs obtained from [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Overview of YOSO, a TTFS-based neuromorphic hard [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
read the original abstract

Eye-based emotion recognition enables eyewear devices to perceive users' emotional states and support emotion-aware interaction. However, deploying such functionality on their resource-limited embedded hardware remains challenging. Time-to-first-spike (TTFS)-coded spiking neural networks (SNNs) offer a promising solution due to their extremely sparse and energy-efficient computation, where each neuron emits at most one binary spike. While prior works have primarily focused on improving TTFS SNN training algorithms, the role of network architecture has been largely overlooked. This is particularly critical, as spike timing in TTFS SNNs is tightly coupled with architectural design, and eye-based emotion recognition requires compact yet highly efficient networks. In this paper, we propose TNAS-ER, the first neural architecture search (NAS) framework tailored to TTFS SNNs for eye-based emotion recognition. TNAS-ER presents a novel ANN-assisted search strategy that leverages a ReLU-based ANN counterpart to guide architecture optimization and stabilize training of the TTFS SNN. TNAS-ER employs an evolutionary algorithm, with weighted and unweighted average recall jointly defined as fitness objectives for emotion recognition. Extensive experiments demonstrate that TNAS-ER achieves high recognition performance with significantly improved efficiency. Furthermore, we evaluate TNAS-ER on a neuromorphic hardware, confirming its superior energy efficiency and strong potential for real-world applications.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes TNAS-ER, the first neural architecture search framework for Time-to-First-Spike (TTFS) coded Spiking Neural Networks (SNNs) applied to eye-based emotion recognition. It introduces an ANN-assisted evolutionary search strategy that uses a ReLU-based ANN counterpart to guide architecture optimization and stabilize TTFS SNN training, with fitness defined via weighted and unweighted average recall. Experiments are reported to show high recognition performance, improved efficiency, and superior energy efficiency when evaluated on neuromorphic hardware.

Significance. If the central experimental claims hold, the work would be significant for enabling compact, low-power emotion recognition on embedded eyewear devices by addressing the overlooked role of architecture in TTFS SNNs. Strengths include the explicit tailoring of NAS to the TTFS regime, the multi-objective fitness formulation, and direct neuromorphic hardware validation, which together provide a concrete path toward real-world deployment of sparse spiking models.

major comments (2)
  1. [Methods / ANN-assisted search strategy] The ANN-assisted search strategy (described in the methods) relies on a ReLU ANN surrogate lacking any temporal dimension to guide TTFS SNN optimization. This raises a correctness risk for the central claim: because eye-based emotion recognition depends on precise first-spike timing, it is unclear whether the discovered architectures genuinely exploit TTFS dynamics or merely perform well after conversion; an ablation comparing native TTFS performance against post-conversion accuracy would be needed to confirm the search preserves timing-sensitive motifs.
  2. [Experiments / Results] The experimental section reports high recognition performance and efficiency gains but provides limited quantitative detail on baselines, absolute metrics (e.g., accuracy, energy per inference), and statistical significance of improvements over prior TTFS or ANN approaches. Without these, the claim of “significantly improved efficiency” and “superior energy efficiency” cannot be fully assessed as load-bearing support for the framework’s advantage.
minor comments (2)
  1. [Problem formulation] Notation for the fitness objectives (weighted vs. unweighted average recall) should be defined explicitly with equations to avoid ambiguity when readers compare against standard multi-class metrics.
  2. [Figures] Figure captions and axis labels on neuromorphic hardware results could be expanded to include exact energy figures and comparison models for immediate readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and insightful comments on our manuscript. We have carefully addressed each major concern below and will incorporate the suggested improvements in the revised version to strengthen the presentation and validation of our claims.

read point-by-point responses

  1. Referee: [Methods / ANN-assisted search strategy] The ANN-assisted search strategy (described in the methods) relies on a ReLU ANN surrogate lacking any temporal dimension to guide TTFS SNN optimization. This raises a correctness risk for the central claim: because eye-based emotion recognition depends on precise first-spike timing, it is unclear whether the discovered architectures genuinely exploit TTFS dynamics or merely perform well after conversion; an ablation comparing native TTFS performance against post-conversion accuracy would be needed to confirm the search preserves timing-sensitive motifs.

    Authors: We acknowledge the referee's concern about the non-temporal nature of the ReLU ANN surrogate. In TNAS-ER, the ANN serves as an efficient proxy to stabilize fitness estimation and guide the evolutionary search across the architecture space, while all final architectures are trained and evaluated natively using the TTFS SNN training procedure that explicitly models first-spike timing. This design choice enables practical search scalability without sacrificing the timing-sensitive evaluation at the end of the pipeline. To directly address the correctness risk and demonstrate that the search preserves TTFS-specific motifs, we will add a dedicated ablation study in the revised manuscript comparing native TTFS performance of the discovered architectures against their post-conversion accuracy from the ANN counterparts. revision: yes

  2. Referee: [Experiments / Results] The experimental section reports high recognition performance and efficiency gains but provides limited quantitative detail on baselines, absolute metrics (e.g., accuracy, energy per inference), and statistical significance of improvements over prior TTFS or ANN approaches. Without these, the claim of “significantly improved efficiency” and “superior energy efficiency” cannot be fully assessed as load-bearing support for the framework’s advantage.

    Authors: We agree that additional quantitative details and rigorous comparisons are necessary to fully substantiate the efficiency claims. In the revised manuscript, we will expand the experimental results section with comprehensive tables that report absolute metrics including recognition accuracy, energy per inference on neuromorphic hardware, and direct comparisons against relevant prior TTFS SNN and ANN baselines. We will also include results aggregated over multiple independent runs with means and standard deviations to establish statistical significance of the reported improvements. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical NAS framework with experimental validation

full rationale

The paper introduces TNAS-ER as a practical evolutionary NAS method that uses a ReLU ANN surrogate to guide TTFS SNN architecture search for eye-based emotion recognition. All central claims rest on reported experimental results (recognition performance, efficiency metrics, neuromorphic hardware measurements) rather than any mathematical derivation, equation, or first-principles result. No self-definitional loops, fitted inputs presented as predictions, or load-bearing self-citations appear in the provided text. The approach is self-contained through external benchmarks and hardware evaluation, consistent with the reader's assessment of minimal circularity risk.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract does not specify any free parameters, axioms, or invented entities. The TNAS-ER framework and its ANN-assisted strategy are introduced but details are absent.

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Reference graph

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