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arxiv: 2604.12365 · v1 · submitted 2026-04-14 · 💻 cs.NE

Recognition: unknown

Adaptive Spiking Neurons for Vision and Language Modeling

Chenlin Zhou, Dongyang Ma, Jiaqi Wang, Jin Cheng, Qingyan Meng, Sihang Guo, Yonghong Tian, Zhengyu Ma

Authors on Pith no claims yet

Pith reviewed 2026-05-10 14:25 UTC · model grok-4.3

classification 💻 cs.NE
keywords Adaptive Spiking NeuronSpiking Neural NetworksVision ModelingLanguage ModelingMembrane PotentialTrainable ParametersInteger Training
0
0 comments X

The pith

Trainable parameters let spiking neurons learn adaptive firing dynamics for vision and language tasks.

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

The paper first lays out a functional perspective that guides how to design better spiking neurons. It then introduces the Adaptive Spiking Neuron, which adds trainable parameters so the neuron can adjust its own membrane potential behavior and firing rate. ASN uses an integer-based training scheme that keeps computation efficient while still allowing the model to fire spikes at inference time. A reader would care because spiking networks promise low energy use but have lagged behind ordinary networks on complex vision and language problems. If the claim holds, the same neuron design can serve as a drop-in component for both modalities without extra instability.

Core claim

We propose the Adaptive Spiking Neuron (ASN), which incorporates trainable parameters to learn membrane potential dynamics and enable adaptive firing. ASN adopts an integer training and spike inference paradigm, facilitating efficient SNN training. To further enhance robustness, we propose a specialized variant of ASN, the Normalized Adaptive Spiking Neuron (NASN), which integrates normalization to stabilize training. We evaluate our neuron model on 19 datasets spanning five distinct tasks in both vision and language modalities, demonstrating the effectiveness and versatility of the ASN family.

What carries the argument

The Adaptive Spiking Neuron (ASN), a neuron model that adds trainable parameters to control membrane potential dynamics and produce adaptive firing.

If this is right

  • ASN supports efficient training through integer operations and spike-based inference.
  • The same neuron works across vision and language tasks on 19 different datasets.
  • The NASN variant adds normalization that reduces training instability.
  • A functional perspective on neuron design supplies rules for creating future spiking neurons.

Where Pith is reading between the lines

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

  • The trainable-parameter approach could extend to other spiking-neuron variants beyond the two presented.
  • Because the method already covers two modalities, it may reduce the need for modality-specific neuron redesigns.
  • If the integer training scheme preserves accuracy at scale, it would lower the compute cost of building large spiking models.

Load-bearing premise

That trainable parameters added to spiking neurons will create stable adaptive firing and raise performance without overfitting or training collapse across tasks.

What would settle it

A controlled experiment in which ASN and a standard non-adaptive spiking neuron are trained on the same vision or language dataset and the ASN version shows no accuracy gain or exhibits repeated training divergence.

Figures

Figures reproduced from arXiv: 2604.12365 by Chenlin Zhou, Dongyang Ma, Jiaqi Wang, Jin Cheng, Qingyan Meng, Sihang Guo, Yonghong Tian, Zhengyu Ma.

Figure 1
Figure 1. Figure 1: Overview of Adaptive Spiking Neuron(ASN). (a) schematics of a biological spiking neuron. (b) depicts a neuron lacking [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Neuron comparison on scaling. The base model is [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
read the original abstract

Regarded as the third generation of neural networks, Spiking Neural Networks (SNNs) have garnered significant traction due to their biological plausibility and energy efficiency. Recent advancements in large models necessitate spiking neurons capable of high performance, adaptability, and training efficiency. In this work, we first propose a novel functional perspective that provides general guidance for designing the new generation of spiking neurons. Following the insightful guidelines, we propose the Adaptive Spiking Neuron (ASN), which incorporates trainable parameters to learn membrane potential dynamics and enable adaptive firing. ASN adopts an integer training and spike inference paradigm, facilitating efficient SNN training. To further enhance robustness, we propose a specialized variant of ASN, the Normalized Adaptive Spiking Neuron (NASN), which integrates normalization to stabilize training. We evaluate our neuron model on 19 datasets spanning five distinct tasks in both vision and language modalities, demonstrating the effectiveness and versatility of the ASN family. Our ASN family is expected to become the new generation of general-purpose spiking neurons.

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

3 major / 1 minor

Summary. The manuscript proposes a novel functional perspective to guide the design of spiking neurons. From this, it derives the Adaptive Spiking Neuron (ASN) that incorporates trainable parameters to learn membrane potential dynamics and enable adaptive firing. ASN adopts an integer training and spike inference paradigm for efficiency. A normalized variant (NASN) is introduced to stabilize training. The ASN family is evaluated on 19 datasets across five tasks in vision and language modalities, with claims of demonstrating effectiveness and versatility as a new generation of general-purpose spiking neurons.

Significance. If the central claims hold with proper validation, the work could advance spiking neural networks by supplying a design principle for adaptive neurons that maintain efficiency while scaling to multi-modal large models. The cross-modality evaluation on 19 datasets would support broader applicability of SNNs beyond vision-only tasks. However, the absence of supporting derivations, baselines, and ablations in the text makes the significance difficult to gauge at present.

major comments (3)
  1. [Abstract] Abstract: the assertion of effectiveness on 19 datasets spanning five tasks supplies no baselines, quantitative metrics, ablation results, or training details, so the central performance claims cannot be evaluated.
  2. [Abstract] Abstract: the novel functional perspective is presented as providing general guidance for designing spiking neurons, but no derivation, equations, or demonstration of independence from the proposed trainable parameters is given, leaving the guidance circular.
  3. [Abstract] Abstract: no analysis or ablation isolates the effect of the trainable membrane parameters on adaptive firing (versus simply adding capacity), nor shows how NASN normalization interacts with the integer training/spike inference split to prevent instability or overfitting on long-sequence language tasks.
minor comments (1)
  1. [Abstract] Abstract: specify the exact five tasks and the 19 datasets to allow readers to assess the scope of the evaluation.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their insightful comments, which have helped us identify areas for improvement in the manuscript. We provide detailed responses to each major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the assertion of effectiveness on 19 datasets spanning five tasks supplies no baselines, quantitative metrics, ablation results, or training details, so the central performance claims cannot be evaluated.

    Authors: The abstract is intended as a summary. However, to allow evaluation of the claims, we will revise the abstract to include key baseline comparisons and quantitative metrics from our experiments. revision: yes

  2. Referee: [Abstract] Abstract: the novel functional perspective is presented as providing general guidance for designing spiking neurons, but no derivation, equations, or demonstration of independence from the proposed trainable parameters is given, leaving the guidance circular.

    Authors: We will revise the manuscript to include a detailed derivation of the functional perspective, including equations, and demonstrate its independence from the specific trainable parameters in ASN to avoid any perception of circularity. revision: yes

  3. Referee: [Abstract] Abstract: no analysis or ablation isolates the effect of the trainable membrane parameters on adaptive firing (versus simply adding capacity), nor shows how NASN normalization interacts with the integer training/spike inference split to prevent instability or overfitting on long-sequence language tasks.

    Authors: We agree that additional analysis is needed. We will include new ablations that isolate the contribution of the trainable membrane parameters and examine the interaction of NASN normalization with the integer training and spike inference approach, particularly for language tasks to address concerns about instability and overfitting. revision: yes

Circularity Check

0 steps flagged

No circularity: design proposal with independent empirical validation

full rationale

The paper first introduces a novel functional perspective as general guidance, then follows it to define ASN with trainable membrane parameters and an integer-training/spike-inference split, plus NASN with added normalization. No equations, self-citations, or uniqueness theorems are invoked that reduce the claimed adaptive-firing benefit or performance gains to the inputs by construction. The 19-dataset evaluation across vision and language tasks supplies external falsifiable evidence, so the derivation chain remains self-contained rather than tautological.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 2 invented entities

Review performed on abstract only; free parameters and axioms are inferred from stated claims without access to equations or methods sections.

free parameters (1)
  • trainable parameters for membrane potential dynamics
    Explicitly incorporated in ASN to learn dynamics and enable adaptive firing.
axioms (1)
  • ad hoc to paper A novel functional perspective provides general guidance for designing new spiking neurons
    Stated as the first step before proposing ASN.
invented entities (2)
  • Adaptive Spiking Neuron (ASN) no independent evidence
    purpose: Spiking neuron with trainable parameters for adaptive membrane dynamics and firing
    New model family proposed in the work
  • Normalized Adaptive Spiking Neuron (NASN) no independent evidence
    purpose: Variant of ASN that integrates normalization for training stability
    Specialized variant proposed to enhance robustness

pith-pipeline@v0.9.0 · 5487 in / 1316 out tokens · 31390 ms · 2026-05-10T14:25:37.850334+00:00 · methodology

discussion (0)

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