A Brain-Inspired Gating Mechanism Unlocks Robust Computation in Spiking Neural Networks
Authors: Qianyi Bai
Haiteng Wang
Qiang Yu
Venue: ICLR 2026 Poster (2026)
Keywords: Spiking Neural Networks (SNNs)
Dynamic Gated Neurons
Noise Robustness
Brain-Inspired Computing
Primary area: applications to neuroscience & cognitive science
TL;DR: We propose the Dynamic Gated Neuron, a spiking model with adaptive conductance that serves as a biologically inspired gating mechanism to enhance noise robustness. Our work achieves superior performance across noisy tasks and speech recognition.

Abstract: While spiking neural networks (SNNs) provide a biologically inspired and energy-efficient computational framework, their robustness and the dynamic advantages inherent to biological neurons remain significantly underutilized owing to oversimplified neuron models. In particular, conventional leaky integrate-and-fire (LIF) neurons often omit the dynamic conductance mechanisms inherent in biological neurons, thereby limiting their capacity to cope with noise and temporal variability. In this work, we revisit dynamic conductance from a functional perspective and uncover its intrinsic role as a bio-inspired gating mechanism that modulates information flow. Building on this insight, we introduce the Dynamic Gated Neuron~(DGN), a novel spiking unit in which membrane conductance evolves in response to neuronal activity, enabling selective input filtering and adaptive noise suppression. We provide a theoretical analysis showing that DGN possess enhanced stochastic stability compared to standard LIF models, with dynamic conductance intriguingly acting as a disturbance rejection mechanism. DGN-based SNNs demonstrate superior performance across extensive evaluations on anti-noise tasks and temporal-related benchmarks such as TIDIGITS and SHD, consistently exhibiting excellent robustness. To the best of our knowledge, for the first time, our results establish bio-inspired dynamic gating as a key mechanism for robust spike-based computation, providing not only theoretical guarantees but also strong empirical validations. This work thus paves the way for more resilient, efficient, and biologically inspired spiking neural networks.

Published as a conference paper at ICLR 2026
A BRAIN-INSPIRED GATING MECHANISM UNLOCKS ROBUST COMPUTATION IN SPIKING NEURAL NET- WORKS
Qianyi Bail:;; Haiteng Wang Qiang Yu
'chool of Artificial Intelligence.
Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing. Tiajjin University, Tianjin, China
School of Computer Science and Technology, Tianjin University, Tianjin, China
School of Future Technology, Tianjin University, Tianji,, China (baiqi ainyii wanght_ yuqiang) @t ju edu. on
ABSTRACT
While spiking neural networks (SNNs) provide biologically inspired and energy- efficient computational framework, their robustness and the dynamic advantages inherent to biological neurons remain significantly underutilized owing to over- simplified neuron models. In particular, conventional leaky integrate-and -irfffre (LIF) neurons often omit the dynamic conductance mechanisms inherent in bi- ological neurons, thereby limiting their capacity to cope with noise and tempo- ral variability. In this work, revisit dynamic conductance from functional perspective and uncover its intrinsic role as bio-inspired gating mechanism that modulates information flow. Building on this insight, we introduce the Dy- namic Gated Neuron (DGN), novel spiking unit in which membrane conduc- tance evolves in response to neuronal activity, enabling selective input filtering and adaptive noise suppression. We provide a theoretical analysis showing that DGN possess enhanced stochastic stability compared standard L.FF models, with dynamic conductance intriguingly acting as disturbance rejection mechanism. DGN-based SNNs demonstrate superior performance across extensive evaluations anti-noise tasks and temporal-related benchmarks such as TIDIGITS and SHD, consistently exhibiting excellent robustness. To the best of our knowledge, for the first time, our results establish bio-inspired dynamic gating for robust spike-based computation, providing not only theoretical guarantees but also strong empirical validations. This work thus paves the way for resilient, efficient, and biologically inspired spiking neural networks.
key mechanism
INTRODUCTION
Spiking Neural Networks (SNNs) offer a biologically inspired alternative to traditional neural ar- chitectures by leveraging discrete, event-driven spikes for computation. Their energy efficiency, temporal expressiveness, and robustness to noise make them increasingly attractive for neuromor- phic applications (Maass 1997 Pfeiffer & Pfeil 2018 Roy al., 2019 He al., 2020 Yu al.) 2025). As third-generation networks, SNNs aim to bridge the gap between artificial computation and biological realism (Pei et al. 2019). However, despite their biological motivations, most exist- ing SNN models commonly referred to as Gateless SNNs -ackk internal gating mechanisms for modulating neuronal dynamics (Bellec et al.) 20188] Fang et al. 2021 Zhang al.) (2024a). Recent efforts, such the Gated LIF (GLIF) model (Yao et al. (022). introduce static, channel-wise gates but remain biologically implausible. As result, the field still lacks biologically grounded dy- namic gating mechanism for spiking neurons fundamental obstacle to developing more adaptive and robust SNNs.
"Thee authors contributed equally. Pursuing a master's degree under the supervision of Prof. Q. Yu 1 Corresponning author.

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Protein phosphorylation and gene expression have demonstrated that ion channel conductance in biological neurons are not static; they can be dynamically modulated in response to sustained neural activity [Kaczmarek] 1987 Chad & Eckert 1986;; Morgan & Curran 1991 Sheng & Greenberg
1990). For instance, depolarization can trigger the expression of immediate early genes such as fos and ras, leading changes in potassium conductance (Smeyne al. depolarization has been observed to reduce calcium currents (Sharp et al, 1993). Intracellular cal- cium often mediates these processes, serving as a second messenger that links neuronal activity to conductance modulation (Kaczmarek & Levitan 1987). These findings have inspired computational models that incorporate dynamic regulation of membrane conductance. Beyond their role in home- ostatic adaptation, such conductance modulations can be viewed as intrinsic gating mechanisms, wherein membrane properties dynamically shape neuronal responsiveness based on prior activity. This biologically grounded form of gating operates independently of synaptic transmission and plays a central role in regulating neural computation.
1992), while prolonged
Building on series of biologically grounded studies conductance- neurons-including the influential work by Gütig (Gülig & Sompolinsky 2009), which formulated their dynamic equa- tions and revealed their time-wan wwwp--nvarraan property
revisit this class of models to bridge the gap between biologically inspired dynamics and their underexplored integration into spiking neural network frameworks. We reintroduced the dynamic conductance mechanism into the LIF neuron model and proposed the Dynamic Gated Neuron (DGN) model. In DGN model, membrane con- ductance are dynamically modulated as a function of incoming activity. This process implements a bio-inspired gating mechanism that adaptively modulates the persistence of internal states based on input dynamics, allowing spiking neurons control the retention and decay of past informa- tion fon-functionally analogous to gating operations in recurrent architectures such as the forget gate in LSTMs (Hochreiter & Schmidhuber 1997). While models like LSTMs and GRUs (Cho et al.] 2014) have achieved remarkable performance through engineered gating schemes, their designs are largely disconnected from biological mechanisms. By grounding gating dynamics in neurophysi- ological principles, our approach bridges this gap, offering a unifying theoretical framework that links spiking neural models with artificial gated recurrent units. This biologically inspired perspec- tive not only enhances the interpretability of gating functions in artificial systems, but also promotes the development of more robust and adaptive architectures informed by the dynamics of real neural circuits.
Unlike traditional LIF neurons, which simplify neural dynamics by using fixed decay rates and static conductance parameters, our model introduces input-dependent modulation of membrane conduc- tance. This enables neurons to selectively retain relevant information while suppressing irrelevant or noisy inputs, thereby implementing bio-inspired gating mechanism. We evaluate the proposed model within multi-layer spiking neural networks and it achieves strong classification performance while demonstrating stronger resistance to noise and perturbation. Our contributions are summa- rized as follows:
DGN: We propose the Dynamic Gated Neuron (DGN) model, generalized spiking neuron model with fully derived membrane potential formulation. Central to DGN is dynamic conductance mechanism that functions as a bio-inspired gating mechanism, enabling adap- tive control over information flow and memory retention within the neuron. DGN under- scores the critical role of dynamic conductance, a biological mechanism, in the modeling of neuronal dynamics.
Bridging Biologically Inspired Dynamics and Artificial Gating Mechanisms: We iden- tify functional parallels between dynamic conductance modulation in model and gating mechanisms in LSTM networks, offering a biologically grounded perspective that helps bridge the gap between brain-inspired computation and artificial neural networks.
We present a complete theoretical analysis of the anti-perturbation properties arising from dynamic conductance mechanisms. In ad- dition, we conduct anti-noise experiments on benchmark datasets using the DGN model, which consistently demonstrates strong performance across both audio and neuromorphic tasks. Notably, our model achieves state-of ofte---f-theattt art top-1 accuracy of 90% on the TIDIG- ITS dataset using a small network.
Robustness Analysis and Accuracy Results:
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2
RELATED WORK
2.1 BIOLOGICAL AND COMPUTATIONAL PARAMETRIC NEURON MODELS
The Hodgkin-Huxley (HH) model (Hodgkin Huxley 1952) introduced biophysically detailed, conductance-based description of neuronal dynamics, capturing action potential generation via voltage- gateggggttedddo ion channels. Despite its accuracy, the computational cost of solving HH equations limited its adoption in spiking neural networks (SNNs), which typically rely on oversimplified mod- els such as LIF (Koch & Segev 1998) and SRM (Gerstner et al.) 2014). Izhikevich's 's comparative analysis (Izhikevich] 2004) further highlighted the trade-offs between biological plausibility and computational efficiency in neuronal modeling. While conductance- based models offer richer dy- namic properties, their integration into SNNs remains rare. In contrast, our work leverages dynamic conductance not merely for biophysical fidelity, but as a functional gating mechanism, bridging biological modeling with modern computational frameworks. To enhance the temporal modeling capacity of spiking neurons, recent studies have extended the classical LIF framework by incor- porating more flexible parameterizations, leading to the emergence of Computational Parametric Spiking Neurons (Yao et al. (2022). Representative models include the Adaptive LIF (ALIF) neuron (Bellec et al., 2018). which introduces deeeeitydeeendeent threshold adaptation; GLIF (Yao et al.) 2022), which embeds gating mechanisms to modulate membrane potential dynamics; Heteroge- neous LIF (Perez-Nieves et al. 202T), which enables learnable membrane time constants; and FS- neuron (Stöckl & Maass 2021). which treats all membrane-relan
parameters trainable. A recent model introduces a double- threesold mechanism to enable both positive and negative spike genera- tion (Zhou et al. 2024). HetSyn (Deng et al. (025) shifts temporal integration from the membrane potential to the synaptic current. While these models improve expressiveness through structural extensions or trainability, our approach is more biologically grounded: incorporates dynamic con- ductance as functional gating mechanism, enabling adaptive regulation of information flow and memory retention with competitive performance across tasks.
2.2 ROBUSTNESS ON SNNS
To enhance the robustness of SNNs against noise and adversarial perturbations, prior works can be broadly categorized into three types: structural modeling, saaninggbbased strategies, and biologically- inspired mechanisms. Structural approaches focus on neuron-level properties; for instance, adjust- ing firing thresholds and temporal windows significantly affects adversarial robustness (El-Allami et al. 2021). and precise spike timing has been shown to stabilize temporal representations (Ding al. 2023). Other works have investigated the role of membrane potential leakage in LIF neurons, demonstrating that proper tuning can suppress high-frequency perturbations (Sharmin et al. 2020 Chowdhury al. 202T). Recent methods further propose learnable and heterogeneous leak factors to adaptively regu ate information retention across time steps (Fang et al. [201 Perez-Nieves et al., 2021 [Dig et al. (024a).. Training- based methods improve robustness by injecting adversarial ex- amples during learning (Kundu et al. [222]) or applying Lipschitz regularization to limit gradient sensitivity (Ding et al. 2022), but they typically rely on static input encoding and overlook temporal dynamics. Biologically inspired strategies mimic mechanisms observed in neural systems, such as introducing stochastic gating to emulate biological randomness (Ding et al., 2024b), or leveraging frequeney-basee -based encoding simulate selective attention by filtering high-frequency components different time steps (Xu et al. 2024). Together, these efforts highlight the importance combining robust training objectives with biologically-aligned temporal regulation improve SNN robustness. Building on these insights, our DGN model proposes novel robustness-cnhancin framework that more closely aligns with biological neural dynamics, which is our dynamic conductance scheme.
METHODOLOGY
3.1 DYNAMIC GATED NEURON MODEL
Extensive research neuronal conductance mechanisms has established diverse conductance-basse
models (FitzHugh 1961; MMoris & Lecar 1981 Hille 2000:: Wilsson & Cowan 1972 Güttg &
Sompolinsky 2009), enhancing biological plausibility beyond traditional LIF frameworks. The
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(a)
(b)
Pnr
W
W
zf-1
zi z
zi
reset
reset
Neuron Variable
Spiking Function
Connection
Figure 1:
Schematic of the models. (a) standard LIF model (pm e-:: (b) the
proposed DGN model described in Eq. (ps = +
neuronal dynamics of basic conductance-basee neuron can be described by the following formula:
N
dV
dt -v. Σ (E -V)
dg: dt
+ C; Σ 5(t -
(2)
where is the leak conductance, represents the conductance of i-th synapse. N signifies the number of presynaptic afferent. 1 indicating the arrival time of the j-th presynaptic spike of the i-th afferent neuron before time Tç is the synaptic time constant. C, represents learning weights of conductance. E& represents the equilibrium potential of the i-th synapse. E; has excitatory synaptic values and inhibitory synaptic values.
Further analysis of manductance-based neuron models revealed that membrane conductance (g + Σg, E,) exhibits synaptic-driven mechanisms precisely regulate the decay rate of membranc potential, thereby mod- ulating neuronal memory efficiency and temporal integration properties. Inspired by this, we present Dynamic Gated Neuron (DGN) model that implements biologically grounded gating structure to reconcile neuronal biophysics with computational efficiency. This framework introduces dy- namic conductance as fundamental gating mechanism, emulating biological neurons' adaptive signal integration while preserving critical information retention properties. The model's mathemat- ical formulation controls membrane potential dynamics through interacting gating components: input-dependent synaptic conductance and intrinsic leak conductance. The temporal evolution of membrane potential V obeys the differential equation:
plasticity modulated by presynaptic spiking patterns. This
dDD dt
D; + +2
(3)
N dt -gi Σ C.D.JV Σ W.D.
dV
(4)
where 2f is the input spike of the i-th synapse in time t. D; is the exponentially decaying synaptic current to soma of the i-th synapse. W; is learning weights of input current. Detailed derivations provided in Appendix(A.I.] For practical implementation of SNNs
of these neuronal dynamics based on connected spiking neurons, coupled with spike firing and spike resetting processes, the dynamics of the DGN model typically rendered discrete iterative format:
= D
(5)
p'= - gr Al A C.D:)
(6)
N
V D. V!-1 At Σ
W.D, - 0='-1
2' = - (((V--0) -1))
(8)
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(a)
Schematic of the LSTM
(b)
Schematic of the DGN
CT-3 -3
C*
Vt-1
V
tanh
Oc
z
Z
tanh
h-11
ht'
D
x²
X
Figure 2: Schematic diagram of the model structure LSTM and DGN. f: decay function. @: reset processing.
where Al represents the time interval between time steps in discrete form. yp represents numerical truncation function, such as the Sigmoid function. θ represents Heaviside step function. An output spike will be generated once the membrane potential V reaches the neuronal firing threshold as per Eq. 8 The membrane potential at the next time step will be soft reset as Eq. 7
This study compares the biologically inspired DGN with the LIF model to elucidate their structural distinctions (Fig. I [J). Unlike the LIF model's fixed leakage conductance (gi) and linear synap- tic superposition, the DGN introduces dynamic conductance factors C₁ to establish a dual- -ath-aay regulatory architecture. It preserves the current injection pathway (W,D;) while adding dynamic conductance term (C; D,), forming a gated mechanism governed by gi + Σ C,D, D;- This configu- ration can adaptively regulate the membrane potential decay rate in real time, therehy overcoming the limitations of LIF in simulating synaptic plasticity and increasing the efficiency of information transfer. This gated mechanism highlights DGN's advantages in balancing biophysical accuracy and computational performance. Appendix 4.3 demonstrates that the gating mechanism sustains excellent performance even with fewer parameters.
3.2 GATING STRUCTURE ANALYSIS IN CONDUCTANCE DYNAMIC SYSTEMS
The proposed Dynamic Gated Neural (DGN) model demonstrates fundamental topological homol- ogy with Long Short-Term Memory (I (LS networks in both structural architecture and core infor- mation processing mechanisms, as illustrated in Fig. 2
Specifically, the self-adapting decay coeffi- cient mathematically emulates the memory filtration function of LSTM's forget gate (f*)) (Hochreiter & Schmidhuber] 1997). The mechanism of accumulating currents through dynamic presynaptic in- tegration is computationally similar to the input gating operation (I') (Gerstner et al. 2014). DGN's spike reset mechanism exhibits mathematical congruence with LSTM's cell state update equations, both employing nonlinear gating variables to control state transitions. This homology underscores profound insight: the gating mechanism is a universal computational principle for regulating in- formation flow across both artificial and biological neural systems. The L.TM model was designed with gating mechanism address the vanishing gradient problem in vanilla RNNs ( (Bengio al.) 1994)). while the gating mechanism of our DGN, inspired by dynamic conductance behavior in biological systems (Kaczmarek) 1987, Chad Eckert 1986)), thereby provides a more biologi- cally grounded instantiation of this computational paradigm. DGN not only validates crucial role of gating in spatiotemporal information processing but also serves as a crucial step toward closing the long-standing gap between brain-inspired computation and artificial neural networks, providing a biologically grounded perspective on the functional origins of gating itself.
3.3 STABILITY OF CONDUCTANCE DYNAMIC SYSTEMS
This section establishes the thcoretical framework for analyzing noise robustness in DGN model through stochastic differential equation (SDE) approaches 2007). By linearizing the nonlinear conductance dynamics under small perturbation assumptions, derive closed-fore expressions for steady-state voltage variances in both DGN and classical LIF model. Comparative analysis of these variance solutions reveals the superior noise suppression capability of the DGN architecture.
In order to compare fairly with other models, we directly analyze the case adding perturbations to the presynaptic input current (D; in Eq. 3. The investigation begins with stochastic input pertur-
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bations modeled as Gaussian white noise superposed on deterministic signals:
(t) μr + o,E(t), (E()) 5(t -t) t')
where H4 denotes deterministic input components and og quantifies noise intensity. The perturbed dynamic conductance G(t) = Go + Σ ((t) induces voltage dynamics, where Go = + Σ Cip.. The membrane potential control formula is:
dV di -(()V +
Σ w.i.()) Go + Σ Waps +( (- E CIOE((I)V + Ewoos(()) (10)
Deterministic
Perturbation term
Linear noise approximation (Van Kampen 1992) applied by decomposing
Viwady (t
with [8V| < Vsuady, where Vsieady is the steady-state solution of the deterministic term. Performing Taylor expansion on nonlinear terms C,), V and retaining only first-order contributions while discarding higher-order small terms (oV -(()). The nonlinear perturbation term is linearized as:
(t) = C,ar((t) Vscendy
(11)
After truncating higher-order terms, the original SDE reduces to linear SDE:
dV
Go
Σ Wi14 + Σ ae (W; C₁ Vusedy ) E(t)
(12)
dt
Using Itô calculus (lt 1944).
the steady-state variance for DGN resolves to:
2
Σ of
W
C. > W3Hg
(V²)
(13)
DGN
2Go
For classical LIF neurons with constant leak gr. the corresponding variance reduces to:
CEN 11=1
W,,,)²
(V /IF
(14)
2gi
The derivation process of the above formula is detailed in A.1.3 Critical examination of Eq. 13 ver- sus Eq. [4 demonstrates two synergistic noise suppression mechanisms in DGN. The denominator Go implements input-dependent leakage scaling, where intensified inputs μ4 amplify effective con- ductance to suppress voltage fluctuations. The numerator contains a compensatory term Go that introduces negative feedback proportional to synaptic weights W, and coupling coefficients C.- When W; and C; are positively correlated, this feedback cancels synaptic noise propagation through W. achieving partial noise rejection. In contrast, the LIF model's fixed leakage JI and absence of compensatory terms result static noise scaling that cannot adapt input statistics.
W
These analytical results quantitatively demonstrate that DGN neurons outperform LIF models in noise resilience through dynamic conductance modulation. The dual mechanism-addppti leakage scaling and synaptic noise cumpensation-enablee effective voltage stabilization during concurrent signal and noise processing. This theoretical framework provides fundamental insights into how conductance dynamics enhance neural computation robustness under stochastic perturbations.
4 EXPERIMENTS
4.1
COMPARISON WITH THE STATE-OF-THE-ART
Speech recognition tasks involve time-correlated contexts, making SNNs ideal due to their self- recurrent connections. To evaluate the efficiency of our DGN model, we conduct assessments on two categories of speech-related datasets: conventional audio classification benchmarks (Ti46Alpha (Mark Liberman et al.) 1993) and TIDIGITS (R. Gary Leonard 1993)) and neuromorphic speech datasets (SHD and SSC) (Cramer et al. 2020), generated through event-based encoding via IochleaAMSIb sensor processing. Details of the network architecture and training protocols provided in Appendix A.2.2 Our experiments focus on both feedforward and recurrent SNNs with DGN model across all four datasets.

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Comparison of model performance on Tih6,, TIDIGITS, SHD, and SSC datasets. Rec=N/Y represents feedforward networks (N) and recurrent networks (Y), respectively. indicatess
Table 1:
results we reproduced using public code, while bold entries indicate the best performance.
Datasets
Method
Rec
Hidden Layers
Accuracy( %
LIF HM22BPNeuurS
N
800-800
90.98
Ti46Alpha
DGN(Ours)
N
100
95.69
RNN"
Y
100
91.89
LSTM*
Y
100
96.05
Y
LIF DIS--SNNs-BIPaa Neural Comput. (hung & Li 2021
400-400-400
95.90
LIF + SrSc-SNNs BIP Front. Neurosci. Zhang al. 2024b
800
96.44
DGN(Ours)
100
96.31
LIF BAE-MPDALince Neurosei.
XTAV
620-11
97.40
TIDIGITS
LIF Multilayer FE- FELLeaMNNNLS
100-100
98. 10
LIF BPTEUCNN ea 2023
400-11
98.10
DGN(Ours)
100
98.59
RNN"
100
97.09
LSTM
100
97.88
DGN(Ours)
100
99.10
LIF data aUgTNNLS CCamer el al.
3020
128
49.70
AC-LL 2024 Zhong al 202a
128-128
83.08
DGN(Ours)
128
85.18
RNN
100
76.53
SHD
LSTM Cramer
Y
128
89.20
LIF data augTNNISS
Y
1024
84.50
Heterogeneous LIF Commun Penex- Nevee-aavves.oo ot
2011
128
83.50
ALIFINNLS Yin all 2020
128-128
84.40
TC-LIF AAAI Zhangg
128-128
88.91
DGN(Ours)
Y
128
87.78
DGN(Ourr))
128 12-1
88.98
LIFINNES
N
128- 128
38.50
TC-LIFAMM
AAAI (Zhang ctal. 20246
128-123
63.46
DGN(Ours)
128-128
67.54
RNN
128-128
72.91
SSC
LSTMMNNLS Cramer et 2020
128-128
73.10
LIFINNLS Cramer all X0X
Y
128- 128
52.00
Y
Heterogencous LIFNar.
Peres-Nieves
128
60.80
ALIF GaussinGradient
Intell ein
128
74.20
TC-LIF AAAI Zhinget al 200cm
128
61.09
DGN(Ours)
128
66.18
DGN(Ourr)
128-128
75.63
As shown in Tab. 1 our proposed DGN model exhibits superior performance across temporal quential data processing tasks through its biologically inspired gating mechanism. The feedforward DGN network with single 100-node hidden layer attains 98.59% classification accuracy the TIDIGITS dataset, surpassing comparably structured multilayer spiking neuron networks. Notably, the dual-layer recurrent DGN achieves 75.63% accuracy on the SSC dataset, outperforming LSTM despite using fewer parameters, while both DGN and LSTM surpass other neurons lacking gating mechanisms. Our proposed DGN show excellent performance in both feedforward and recurrent networks, and their accuracy is comparable to even better than several current SOTA methods in the field of SNNs, despite using fewer neurons and simpler network structure. These results show that the introduced gating mechanism effectively improves the expressive power of a single neuron. This elegant solution empowers individual neurons with gated control functionality, while avoiding the incorporation of overly complex architectures. The effectiveness of the dynamic gating mechanism enhancing the efficiency of neuronal information transmission to process complex time series data has been effectively demonstrated.

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Original
Additvve
Subtractive
Maud
FGSM
PGD
SIM 1.0
Sample
0.8
Sample
0.5
0.4
Sample 2
Figure 3: Noise sample visualization on SHD dataset. Each row corresponds to one SHD sample, and each column represents noise type: Original, Additive, Subtractive, Mixed, FGSM, PGD, and BIM. The horizontal axis indicates time, and the vertical axis represents input channel indices.
Table 2: Accuracy (%) of the proposed DGN under different noise conditions and adversarial attacks on TIDIGITS and SHD. Bold entries indicate the best performance. HeterLIF denotes the hetero- geneous LIF model proposed by Perez-Nieves al. (2021)
Noise Subtractive
Attacks PGD
Model
Clean
Additive
Mixed
FGSM
BIM
TIDIGITS
LIF
97.02
46.83
93.70
44.20
39.53
15.39
15.95
HeterLIF Perex-Niewss X01]
96.52
77.49
89.37
72.78
52.48
43.94
43.68
ALIF (Reliceetll 2018)
96.99
63.29
93.17
60.58
42.50
19.80
19.42
DGN(Ours)
98.59
95.34
93.70
78.12
90.35
86.76
86.88
RNN
97.09
23.64
86.76
21.66
9.89
0.00
0.00
LSTM
97.88
65.12
79.25
64.77
64.97
60.66
61.01
Rec
LIF
97.80
73.23
89.60
67.68
26.55
61.79
60.70
HeterLIF Perez-Nievss
96.29
78.97
82.59
73.05
8.76
36.62
35.74
ALIF (Belkce 2018)
97.54
84.01
86.19
79.25
25.04
62.82
63.18
DGN((Urr)
99.10
94.84
96.70
93.86
89.40
87.52
87.68
SHD
LIF
77.30
29.93
56.32
31.44
51.55
47.87
47.92
HeterLIF (PreesNNeees al X01
77.77
25.49
54.91
25.58
52.23
50.78
50.89
ALIF (Ralkee al 2018
78.02
40.25
55.08
39.50
53.31
51.51
51.57
DGN(Ours)
85.18
59.46
64.05
58.87
63.81
61.59
61.44
RNN
78.24
27.47
$2.29
28.06
17.35
11.93
13.94
LSTM
86.89
41.61
64.58
39.23
39.27
32,01
33.37
Rec
LIF
75.77
9.24
$7.44
9.25
17.78
30.59
31.45
HeterLIF
(Peres-Neeves et al. 2001
79.85
39.57
58.19
38.87
44.76
49.12
49.10
ALIF
(Belkee et al 2018
82.08
46.59
63.32
47.28
52.2
58.01
58.31
DGN(Ours)
87.78
78.97
61.91
79.35
69.45
66.13
66.34
4.2 OVERALL PERFORMANCE FOR VARIOUS PERTURBATION
To evaluate the robustness of the proposed DGN model, we implement a rigorous framework where all models are trained on pristine datasets without artificial corruption or noise. Traditional robust- ness evaluations typically use hybrid datasets containing noisy samples in both training and testing sets, which can lead to noise pattern memorization (Bishop) [995 Simard et al. 2003). Traditional method introduces significant dataset construction overheads and may yield misleading assessments of noise immunity due to varying pattern learning capacities among models (Tsipras et al. 2018
Thams al. 2022 Zhang et al. 2021). In contrast, adopt more challenging approach that evaluates models using previously unseen noise patterns, thereby providing a more realistic assess- ment of their performance under suboptimal conditions.
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We considered three types of noise commonly encountered in SNNs: additive noise, subtractive noise, and mixed noise. We also evaluated model robustness under three gradient-based adversarial attacks: FGSM (Goodfellow et al. 2014), PGD (Madry et al. 2018), and BIM (Kurakin et al. 2018). We conducted anti-noise experiments on the TIDIGITS dataset and the SHI) dataset to compare other models with our DGN model. Examples of how different noise types affect the input signals are shown in Fig. 3 The experimental setup is in Appendix A.2.4 and the extended experimental data is in Appendix A.5 In Tab. we select sampling points of different strengths for different perturbation. Noise generation probability 0.006 for additive noise, and p 0.3 for subtractive noise. The perturbation c 0.003 for all attacks, and iterative step & 4, step size a 0.01 for PGD, BIM. All results were reproduced using publicly available code to ensure fair comparison under identical conditions. Accuracies under the clean condition can be found in Appendix |A.2.3
As shown in Tab. 2 the DGN-based feedforward network keeps 95.34% accuracy under additive noise on the TIDIGITS dataset, surpassing the conventional LIF model by 48.51%, %, demonstrating that its adaptive dynamic conductance learning mechanism effectively isolates noise from salient features. The robustness of gated architectures is further evidenced by comparative analyses: under PGD attacks SHD, LSTM outperforms vanilla RNN by 20.08%, while the recurrent DGN sur- passes recurrent LIF by 35.54%, validating the robustness from gated conditions and adversarial attacks, the DGN model exhibits superior resistance compared to clas- sical SNN neurons and ANNs (RNN and LSTM), maintaining the highest baseline accuracy and minimal performance degradation. These results underscore that the biologically inspired gating structure, driven by dynamic conductance modulation, fundamentally enhances robustness.
crriuniims.Acooos all noise
4.3 ABLATION STUDY
Efficiency under Parameter Reduction. Just as LSTM/GRU enhance MLPs by introducing com- plex, task-dependent structures, our DGN neuron endows SNNs with more expressive computational capabilities than traditional LIF units through the incorporation of dynamic conductance mecha- nisms. The observed performance improvements stem not from an increase in the number of pa- rameters, but rather from the inherent efficiency of the neuron design. To clarify this distinction, we introduce a simplified variant, S-DGN, which reduces the parameter count while preserving the core mechanisms.
In S-DGN, all synapses connecting to the same neuron share single equilibrium potential E, in contrast to the original DGN where each synapse maintains its own. This modification substantially reduces the number of learnable parameters, thereby constraining expressivity while preserving the essential computational mechanism. The resulting dynamics of S-DGN are formulated as follows:
N = -V 91 Σ C,D; + +EV _ C,D,
N
dV dt
(15)
iml
We evaluate its effectiveness on the SHD dataset. The evaluation considers three critical factors: classification accuracy, parameter count, and overall robustness against noise. As demonstrated in Tab. 3 S-DGN achieves competitive accuracy and noise robustness while maintaining LIF-leveel pa- rameter efficiency. We demonstrate that stable performance improvements persist without increasing parameter counts, confirming that the observed benefits do not stem solely from parameter scaling but rather from the intrinsic advantages of the proposed neuronal mechanism.
Performance under Different Perturbation Strength. We systematically assess the robustness of spiking neuron models by measuring their classification accuracy under escalating perturbation intensities (p or E). As shown in Fig. 4 the proposed gated neuron model maintains higher classifi- cation accuracy with only marginal degradation when subjected intensified noise disturbances and diverse adversarial attacks. This performance advantage is attributed to the gating mechanism' 's abil- ity to dynamically adjust the neuron information transmission mode. thereby improving the model's s adaptability to perturbations. Extended experiments based on other datasets (Appendix (A..2) fur- ther and consisently demonstrate the robustness of DGN.

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Published as a conference paper at ICLR 2026
Table 3: Performance comparison of different neuronal models on the SHD dataset. We report the parameter count (in K), clean accuracy (%), and overall robustness (%). Highlighted rows corre- spond to our proposed models, s-DGN and DGN, which consistently achieve superior accuracy and robustness while demonstrating the impact of parameter reduction.
FF Models
Parameters (K)
Clean Accuracy (%)
Overall Robustness (%)
LIF
92.16
77.30
44.17 + 9.96
HeterL.F
92.31
77.77
43.31 = 12.64
ALIF
92.16
78,02
48.53 = 6.24
DLIF (250 step)
Ding 2024a
92.66
79.96
49.93 + 5,91
s-DGN (ours)
92.31
84.30
58.28 + 2.33
DGN (ours)
184.32
85.18
61.54 + 1.95
Rec Models
Parameters (K)
Clean Accuracy (%)
Overall Robustness
LIF
108.54
75.77
25.96 = 16.67
HeterL.I
108.69
79.85
46.60 + 6.58
ALIF
108.54
82.08
54.28 = 6.11
DLIF (250 step)
Ding 2024a
109.04
82.70
58.87 = 4.64
s-DGN (ours)
108.82
85.65
64.48 = 4.82
DGN (ours)
217.09
87.78
70.36 + 6.60
Additive
Subtractive
Mixed
Accuracy (9)
Accuracy (9))
Accuracy
ALIF UF
25
25
HeterLIF Ours
0
0.0
0.009
0.018
0.03
0.0
0.09
0.18
0.3
0.0
0.009
0.018
0.03
P
P
FGSM
PGD
BIM
ALIF
Accuracy (9))
75
Accuracy (%)
75
LIF
HeterLIF
Accuracy
50
50
25
25
25
0
0.0
0.009
0.018
0.03
0.0
0.009
0.018
0.03
0.0
0.009
0.018
0.03
Figure 4: Performance of the model on TIDIGITS using feedforward network under perturbations of different distribution probabilities p or attack strengths e.
CONCLUSION
In this work, address the lack of bio-inspired gating mechanisms in traditional spiking neural networks (SNNs) by revisiting the biophysical principle of dynamic conductance. Inspired by the temporal behavior of biological ion channels, we propose novel neuron model that implements biologically inspired gating structure. This mechanism significantly enhances the spatiotemporal expressiveness and information selectivity the neuron, leading to substantial performance im- provements in speech recognition. Moreover, our model demonstrates superior robustness under various noise perturbations and adversarial attacks. This work introduces a new modeling paradigm for SNNs, offering insights into both robust computation and biologically grounded design. Fu- ture directions include integrating our approach with other advances in the SNNs community, and exploring richer conductance-batt based gating models with enhanced spatiotemporal properties.
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6 ACKNOWLEDGEMENT
This work was in part supported by the Brain Science and Brain-like Intelligence Technology National Science and Technology Major Project (Grant No. 20252D0215600), the National Natural Science Foundation of China (Grant No. 92370103), and in part by the Xiaomi Foundation.
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A APPENDIX
A.1 METHODOLOGY
A.1.1 DYNAMIC GATED NEURON
For reading convenience, Eq. 2 is repeated here:
dg. dt
C Σ 8(t-th (()-
(16)
To
t/<
where i! indicating the arrival time of the jth presynaptic spike of the ith afferent neuron before time ((x) represents Dirac delta function (Dirac 1981), so Eie<o 8(t - f is equivalent expression of (t). Then solve Eq. [6 the general solution method for first-order linear nonhomogeneous differential equations:
(' elo 1k C. Σ 5(1 E0(1- 12)
9:(T) = - -1o + d (ct (c+
6/
(17)
e + (ct Ci
1" J0 fest
8 - k2)dd)
where c is an arbitrary constant determined by the initial condition. We f(t,t)) e 8(t
12), At = T/n, d
A' (ceiling function). Then:
e d(l 1.)dd J/¹ Σ SU,
Σ
1/
lim
f(k t)
n 0= P/ck-A
lim
Σ
f(k At,
(18)
n=000
1/<Tk=d
Σ
f(k At,
n
k=d
{" f(t,t))tt
Σ M<T
According to the properties of the Dirac delta function, we can get F f((, = 6 When T the conductance 9;(T) of the i-th synapse Ó. Substituting this into Eq. 17 yields 0. So, we finally get:
(T) = C,
Σ
(19)
= C
T4
<<T
Then, we set D = Ex
yielding Eq. 3
Considering the discrete case in Eq. 5 we get:
g.(t) C.
Σe
CiD
(20)
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Substituting the above formula into Eq. I we can get:
dV di
(gr + ECDD.) + EEC.D. E,C.D.
(21)
In neurobiological computational modeling, classical theoretical frameworks typically posit synap- tic equilibrium potential E; as a binary-state parameter (Gütig & Sompolinsky 2009). However, our network construction process transcend this limitation by permitting heterogeneous equilib- rium potential parameters across individual synaptic units. So we set Eg as learnable parameter. The mathematical formalization method establishes the synaptic connection weight C; E; as learnable parameter W; through a multiplicative relationship, because C₁ and E, are trainable pa- rameters. This parameterization methodology preserves biophysical interpretability while enabling multidimensional regulatory mechanisms for synaptic efficacy. Crucially, such an approach not only transcends the theoretical constraints of conventional bistable equilibrium potentials but also sub- stantially augments the modeling capacity for network dynamics characteristics through the incorpo- ration of continuous-spectrrum E, values. So by slightly rearranging Eq. 21 get Eq. Eq.
A.1.2 RECURRENT DGN
Our recurrent DGN follows a standard RSNN design in which each neuron receives an additional recurrent pathway driven by spike activity from the previous time step. Practically, this is imple- mented through an extra decaying synaptic current to the soma, without explicit celf-connections. This approach widely adopted in recurrent SNNs and extends naturally from our feedforward formulation.
The resulting update equations are:
Dj = At DD
-
(22)
D = al Di +2¹
(23)
N
N
0² s 9 AL
C
Al
Cr Di,000)
(24)
1=1
N
V 11=p'v--1 At W.D! + At Σ
Winee D sree - 0z1-1
(25)
O(
(26)
The recurrent model is also trained using standard backpropagation through time (BPTT). In prac- tice, we observe that the introduced recurrent pathway further enhances the computational capability and robustness of the neurons, which demonstrates that our dynamic gating mechanism generalizes well beyond purely feedforward architectures and consistently improves performance on challeng-
ing temporal tasks (as also reflected in Tab And the overall robustness is also better, shown in follow:
Table 4: Overall Robustness on the SHD dataset from Tab
Datasets
Architecture
Overall Robustness(%) (%)
FF Rec
88.56 +: 5.64 91.67 3.62
TIDIGITS
FF
61.54 1.95
SHD
Rec
70.36 + 6.60
We note that the recurrent DGN does not fundamentally change the underlying neuron dynamics; rather, it provides the network with additional temporal context through recurrent synaptic currents. This recurrent integration might be of the key reasons that lead to better performance.
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In terms of training behavior, both feedforward and recurrent DGNs converge reliably under the same optimization settings. The recurrent architecture is not inherently easier to train, but its recur- rent pathway allows DGN neurons to leverage their dynamic conductance mechanism more effec- tively, resulting in stronger temporal credit assignment and improved performance.
A.1.3 DERIVATION OF SDE VARIANCE
The perturbation satisfies:
B(E(t)) = 0, E(t)dt dldW =
(27)
where W, is the Brownian motion used to describe random behavior.
We have linear SDE of DGN:
dV
dt = GGV + Σ Wips + Σ os (W; C2 CiVec)y E(t)
(28)
We take the expectation both sides of dynamic equation Eq. 28 Then get:
BV] -v + Σ Wip+ Σ o. (W; C.Vssenyy B(g(t))
(29)
VV + Σ WiH
(30)
In steady state:
W.p:
E[V]
(31)
Go
Applying Itô calculus (1t 1944) to 2
get the calculation formula:
d(V 2V + (dV)²
(32)
Substituting Eq. 28 into the above equation, we get
- C, C.Visody)) )] dt
d(v3) = 2t + Σ 2W VV + Eo,(W,
(33)
+2
o.(W, C. Viscady)) VE(t)dt
(34)
Taking the expectation on both sides, get:
Zo,(We C.V. VVsendy) )²
dt =
2Go +
Σ 2V+ +
(35)
Σ oo,W, ViVen)y))) VE(g(()
(36)
Substituting Eq.
31 into the above equation, can get the following when taking steady state:
ΣN N Ti
(W,
C. E Wyps
W
Go
(V2) = E
(37)
2Go
Similarly, the steady-state variance LIF neurons can be obtained.
1.1.4 TRAINING DGN-SNNS WITH BPTT
The network outputs at each timestep are given by 0, Wz Classification is based the average these outputs across all timesteps, computed as Upred = 11111110000 T. Σ Or. The loss function L defined over averaged outputs and is typically formulated E ((pppldd. y). where y represents the true labels and & could be the cross-ent cross-entropy entropy function, as noted various studies (Zheng et al.
2021 Meng et al. 2023
Fang al. 2023 Wang al. 2023)
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loss
z
zi
/+1
V
Vi
[-1
1
Laveehl
D
D
D
zi-1
2
[+1
Time
Figure 5: DGN unfolds over three time steps
BPTT unfolds the iterations described in Eq. 7 and propagates gradients back along the computa- tional graphs across both temporal and spatial dimensions, as illustrated in Fig. 5 Subsequently, the weight update for single layer is determined among all timesteps T::
dF dW,
T
dE dz det dW,
Σ
(38)
T dE dzt
dE
Σ dz dC;
(39)
dC,
dE
0E
OE Ozk
Σ (-O0A)
(40)
del
0z
kmt+1 f=H+1
dz
t-1
D'
Σ
II ( - 10--511111 oリ
(41)
dW;
A=
1=
dza
v I' -'V---
D
Σ
II (0) -000
1- D
(42)
dC,
where:
V surrogate gradient
Fx: derivative of the truncated function ó in Eq 6
f:: the value of F' at 1 (gi + ΣN C.D.), i.e. ='= (gt + ΣN C.D!))
In the process of gradient propagation using BPTT, it is also necessary to manually set surrogate function to calculate surrogate gradient v', which are used as dz' /dVV,, that is:
dz
(43)
dV
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dE dz dC,, Published as a conference paper at ICLR 2026
The detailed derivation process of Eq. E with respect to the trainable weights C2 and W of synapse is:
38
Eq.
42 as follows. The gradient of the loss function
dE
dE dz dz dW,
(44)
dW,
dE
(45)
dC;
dz
Combining the calculation graph, we can obtain
dE
0E
dz dE
(46)
dz
Oz
dzt+1
dz
Then:
d
d
OV
(47)
det
dV++1
Oz
According to Eq. 8
and Eq.
43
we obtain respectively:
Ov++1
(48)
Oz
dzt+1
(49)
dV(+1
By combining the above formula and substituting Eq. 47 into Eq.
46
we obtain:
dE
OE
dE
00
(50)
dzt
Oz
dzt+1
To carry out the analysis, for any time 1 S t < T, we expand the recursion:
T
dE
0E
OE (-ONE")
+
(51)
dz'
dz
Ozk
k=t+1
j=t-1
Combined with the calculation graph, we get:
d2g dz dVt
(52)
dW.
dVi
dW.
dzg dzh dV
(53)
dC,
dV' dC.
According to the calculation diagram DGN over time, combined with Eq, 7
we can get:
dv = ov ov dvi-1 o d= +
dW, ow, av dW, + 02 dW,
(54)
dV. ov dp' ov dv 1V d=t-1
(55)
dC, Op dC, avi-1 dC, dz--1 dC,,
Substitute Eq. 52 into Eq.
54 Eq. 53 into Eq, 55 and arrange them to get:
dVe 0v Ov dvt-1 ov dz'-1 dV
dW.
OW,
0. dW; dz,1 dV--I dW,
0V
ov d dvt-1
(56)
av,
δz. dvi-i dW:
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dv ov dØ
ov dV-- OV d='-1 dV
dC; =
Op dc; + OV--1 dC;
dz dV--1 dC;
ov dpt ov
+ 0. + o2 1=:1 dV
(57)
Opt dCi
0zt-1 dV dC;
According to the Eq. 5
8
wC get:
3/ 444 20
(58)
2/6 14
d 1 1.16
(59)
a I-1 0
(60)
7 211
(61)
1-11 1A11
(62)
448
1pt V--1
(63)
dp'
F" (1 Σ
C.DDY
(64)
dC,
Substitute the above formula into Eq. 56 Eq. 57 and sort out to get:
dv
= (p' - -0v'-1 dv
+ D
(65)
dW,
dW: l'V"-1
dV' (p' -マーー1) dv
(66)
IC;
dC,
Expand the recursive calculation of equations Eq. 65 and Eq.
66
and we get Eq.
38 Eq. 42
A.1.5 PSEUDOCODE FOR DGN COMPUTATION
We have now included pseudocode detailing the core computational steps of membraee update, dynamic conductance computation, and spiking-whick aligns with Eq Eq and is com- patible with mainstream deep-learning frameworks.
Algorithm Hidden Layer States Update
Input: Input spike sequence: 2t, Hidden layer states: his buy ltz Output: Updated hidden layer states: u,
I: pre_i + P: h4 + 24
b Calculate presynaptic current Multiply by weight matrix get dynamic conductance b Multiply by weight matrix to get somatic input current
2: g + pre 100 C
3: post i 6- pre i & W
4: e+ (1 dt 9t
dt g) h2 + post 9 h2 Neuronal potential decay, update membrane
potential
5: θ (v !
Calculate spike emission
6: return pre.i, 0,
A.2 EXPERIMENTS
A.2.1 DATASETS
T146Alpha: T146Alpha is the full alphabets subset the TI46 Speech corpus (Mark Liberman al. 1993) and contains spoken English alphabets from 16 speakers. There 4,142 and 6,628
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spoken English examples in 26 classes for training and testing, respectively. The threshold encoding mechanism (Gütig& & Sompolinsky 2009) is used to encode the audio information into spike pattern. First, a spectrogram is computed with consecutive Fourier transforms (legacy function) from the original sound wave. Then the spectrogram is filtered by a filter bank of 16 triangular filters to obtain a mel-scale spectrogram. Next, for each mel-scale spectrogram bin corresponding
a filter, 30 neurons are used to encode its energy changes as spikes. Thus, a total of 480 neurons are used encode an audio sample (more details, see (Gütig & Sompolinsky 2009)). In order increase the generalization ability of the model, we added 20 empty channels, each original audio has been converted into spike trains over 500 input channels.
TIDIGITS: TIDIGITS is widely used speech recogmition dataset that contains the utterances of 11 words from the digits "zero" "nine" and "oh." It contains a training set of 2464 samples and a test set of 2486 samples. The same preprocessing used for T146A/pha is adopted.
spike based sequence classification benchmark, consisting of spoken digits from 0 to 9 in both English and German (20 classes). The dataset con- tains recordings from twelve different speakers, with two of them only appearing in the test set. Each original waveform has been converted into spike trains over 700 input channels. The train set contains 8,332 examples, and the test set consists of 2,088 examples (no validation set). In our experiments, reduce the time resolution to speed up the simulation. Therefore, the preprocessed samples only have about 250 time steps. We determine that a channel has a spike at a certain time step of the preprocessed sample if there's least one spike among the corresponding several time steps of the original sample.
SHD: The Spiking Heidelberg Digits dataset
The Spiking Speech Command dataset, another spike-based sequence classification bench- mark, is derived from the Google Speech Commands version 2 dataset and contains 35 classes from a large number of speakers. The original waveforms have been converted to spike trains over 700 input channels. The dataset is divided into train, validation, and test splits, with 75,466, 9,98 I and 20,382 examples, respectively. The same preprocessing used for SHD is adopted.
SSC:
A.2.2 TRAINING SETUP
Table 5: Network parameters for different datasets.
Dataset
Network
Tm
Ts
(c. w)
feedforward
10.00
2.0
1.00
(0.01 # 0.005, 0.01 +.001+0000 0.005)
Ti46Alpha
recurrent
15.00
1.50
1.00
(0.01 # 0.005, 0.01 0.005)
feedforward
100.00
1.0
1.00
(0.01 # 0.005, 0.001 0.0005)
TIDIGITS
recurrent
10.00
2.50
1.00
(0.01 # 0.005, 0.01 0.005)
feedforward
1.00
0.02
1.00
(0.01 # 0.005, 0.01 + 0.005)
SHD
recurrent
1.00
0.02
1.00
(0.001 = 0.0005, 0.001 = 0.0005)
feedforward
1.00
0.02
1.00
(0.01 # 0.005, 0.01 # 0.005)
SSC
recurrent
1.00
0.02
1.00
(0.01 + 0.005, 0.01 0.005)
We train the Ti46Alpha and TIDIGITS datasets for 64 epochs utilizing the Adam optimizer. Their learning rate are set to 0.001 for both feedforward and recurrent networks. For SHD and SSC datasets, we train the models for 128 epochs using the Adam optimizer. Their learning rate are set to 0.001 as well. Unlike standard binary spike trains, the SHD dataset have been temporally preprocessed to aggregate spikes within 4ms-windows (Zhang et al. [2024a] Yin et al. 2021 Cramer et al. 2020), resulting in integer spike counts per time step. We train all of the datasets on Nvidia GeForce RTX 4060 GPUs with 8GB memory for feedforward network and Nvidia GeForce RTX 4090 GPUs with 24GB memory for recurrent network.
We summarize the specific hyperparameter settings for all neuron models (including our proposed DGN and the reproduced baselines) in Tab. 5 These settings include the membrane time constant (Tm), synaptic time constant (r.), and spike threshold (9). In addition, (c, w) denote the initial values of the trainable parameters (C', W), where is used only in the DGN model.
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To eliminate confounding factors and clearly attribute performance improvements to the dynamic conductance gating mechanism itself, we evaluated models using relatively simple feedforward and recurrent architectures, and correspondingly selected baseline SNN models with comparable struc- tural complexity. Additionally, to avoid the confounding effects of complex camponents-advvnaee spiking Transformer architectures introduce structural elements such multi-head attention and normalization modules, which may overshadow or obscure the independent contribution of dynamic conductance gating and reduce the interpretability mechanistic analysis- did not include such architectures in this study. Meanwhile, we fully acknowledge that integrating dynamic conductance with spiking Transformers constitutes promising research direction.
A.2.3 ACCURACIES UNDER THE CLEAN CONDITION
We reproduce the results on the datasets following the referenced paper, as shown in Tab. To ensure fairness in the subsequent experiments involving noise and adversarial attacks, we adopt the same hyperparameter settings across all runs to obtain the base models. The discrepancy between our reproduced accuracies and those reported in the original paper may partly stem from differ- ences experimental platforms. Nevertheless, conducting all evaluations under identical conditions makes our comparisons more consistent and meaningful.
For the T146Alpha and TIDIGITS datasets, we use a single hidden layer with 100 neurons, while for the SHD and SSC datasets, we use single hidden layer with 128 neurons.
We conducted three runs of the DGN model using the same initialization but different random seeds. As shown in Tab. 66] the first row reports the best result among the three runs, while the second row presents the mean and standard deviation. The best-performing model was then used for the noise robustness experiments.
Table 6: Accuracy of each method we reproduced on different datasets without noise or attacks.
Method
Ti46alpha
Tidigits
SHD
SSC
LIF
94.00
97.02
77.30
47.72
HeterLIF
93.50
96.52
76.76
55.59
FF
ALIF
93.85
96.99
78.02
49.17
95.69
98.91
85.18
58.77
DGN(Ours)
(95.60 # 0.08)
(98.24 + 0.34)
(84.6 0.42)
(58.34 0.06)
RNN
91.89
97.09
78.24
72.91
LSTM
96.05
97.88
86.89
75.95
LIF
90.89
97.80
75.77
53.16
HeterLIF
91.31
96.29
79.85
57.47
Rec
ALIF
90.28
97.54
82.08
55.96
96.31
99.10
87.78
66.18
DGN(Ourr)
(95.74 0.35)
(98.67 # 0.26)
(86.33 0.58)
(65.72 0.27)
A.2.4
NOISE SETUP
Additive
Each element of the input tensor is independently perturbed by adding a random binary value sam- pled from Bernoulli distribution with probability p. This operation mathematically equivalent to injecting Poisson noise with rate of p/ At (Hz) into the dataset. The resulting noise tensor has the same shape as the input tensor, and the perturbed values are truncated to ensure non-negativity.
Subtractive
Each con-zero element in the input tensor is independently perturbed by subtracting a random binary value sampled from Bernoulli distribution with probability p. The perturbation only occurs where the original data is greater than zero. After the subtraction, the resulting values are clamped from below ensure negative values remain.
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Algorithm 2 Additive Noise
Input: Data tensor data; Probability
Output: Perturbed data with Bernoulli noise
1: noise <- Bernoull(y)) samples with same shape as dala
2: perturbed.date + data + noise
3: perturbed max boxtprtrurbed. 0)
4: return perturbed data
Algorithm Subtractive Noise
Input: Data tensor data; Probability p Output: Perturbed data with Bernoulli noise
1: noise < Bernouli(p)) samples with same shape as data
2: mask <- (data 0)
3: perturbed data &- data noise x mask
4: perturbed_dee data max(perturbed 0)
5: return perturbed,date
Mixed
This approach combines both additive and subtractive Bernoulli noise. For non-zero elements, noise subtracted with a higher probability scaled by factor (default 10x). For zero elements, noise is added with the original probability p. All perturbations are performed independently, and the result clamped ensure no negative values remain. Since the input non-zero valid data is very sparse, only when the probability of subtractive noise is high can the interference effect be equal to (or even lower than) that of additive noise. Therefore, when constructing mixed noise, the probability of subtractive noise is magnified by 10 times.
Algorithm Mixed Noise
Input: Data tensor data; Probability p; Deletion scale factor ry (default: 10) Output: Perturbed data with mixed Bernoulli noise
1: delete_mask < (data > 0)
2: delele min(p 7,
3: delele_noise <- (ernoulli(deeeee.p)))
4: add.mask (dota ==
5: add_noise + Bernoull(pp)
6: perturbed data data delete. mask delete.noise noise add mask noise
7: perturbed data +- max(prrurrbed data,
8: return perturbed
Given classification model ff with dataset (z. Muue), where is the clean image and Virue is the corresponding correct label. The formulations the attacks we used in this study are described as follows:
FGSM
FGSM aims to perturb the original data x along the sign direction of the gradient on the loss function with one step to increase the perturbed linear output, thereby misleading the network, and it can be formalized follows:
f = x + e sign V V.(((()) Mane));
(67)
where sign(.) is an odd mathematical function that extracts the sign real number.
PGD
PGD attack is the iterative variant of FGSM. first starts from a random perturbation in the L norm constraint around the original sample x, then takes gradient iteration step in the sign direction to achieve the greatest loss output, it can formalized as follows:
== u(-c,+t))
(68)
h+1 =Clip { + a sign V (+((+*), ()mee))
(69)
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where & is the iterative step, is step size for each attack iteration, controls the perturbation level. (-) is uniform function, Clipz,c }-} is the function which performs per-pixel clipping of the image E, so the result will be in Lx -nom e-neighborhood of the original image
BIM
Both BIM and PGD attacks iterative attacks. Different from PGD attacks, BIM updates the adversarial samples starting from the original image.
A.3 ABLATION STUDY
A.3.1 PRACTICAL SCENARIOS FOR S-DGN VS. FULL DGN
Specifically, s-DGN's reduced parameter count facilitates deployment on memory--cnstraiiee de- vices, rendering it well-suited for edge computing scenarios -waaurios-innuuinnng IoT terminals (e.g.. smart sensors, edge gateways), portable AI devices (e-g., wearable health monitors, small UAV flight con- trol systems), and low-power embedded systems with limited computational resources and video memory. In contrast, the full DGN implements the complete dynamic conductance mechanism, fully leveraging its gating effect to exhibit superior performance in processing dynamic temporal in- formation. It is thus better tailored to scenarios demanding high performance and abundant hardware resources, such as real-time high-resolution video analysis (e.g.. intelligent security early warning, autonomous driving environmental perception), complex physiological signal decoding (e.g., high- precision brain-computer interface control, real-time multi-parameter monitoring of critically ill patients), and large-scale dynamic data prediction (e.g., financial high-frequency trading analysis, smart grid dynamic load dispatching).
The full DGN model proposes general neuronal model implement this dynamic conductance gating mechanism. Like S-DGN, indicators such as parameter count, energy efficiency, and inference latency can be mitigated through hyperparameter adjustment or other optimization methods (e.g., model compression, hardware-aware design).
Additive
Subtractive
Mixed
100
100
100
Accuracy (%)
Accuracy (%)
Accuracy (%)
75
75
75
50
50
ALIF
50
LIF
25
HeterLIF
0.018
p
p
p
FGSM
PGD
BIM
100
100
100
ALIF
Accuracy (%)
Accuracy (%)
Accuracy (%)
75
HeterLIF Ours
0.0
0.009
0.018
0.03
0.0
0.009
0.018
0.03
0.009
0.018
0.03
8
Figure 6: Performance of the model on TIDIGITS using recurrent network under perturbations of different distribution probabilities p or attack strengths e.
A.3.2 PERFORMANCE UNDER DIFFERENT PERTURBATION STRENGTH
We systematically evaluate the robustness of multiple spiking neuron models TIDIGITS and SHD datasets using both feedforward and recurrent networks by quantifying the performance loss under gradually increasing parameter perturbations(p e).
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Additive
Subtractive
Mixed
Accuracy (96
80
ALIF
Accuracy (96
80
Accuracy (96)
80
LIF
60
HeterLIF
60
60
Ours
40
40
40
20
20
20
0.0
0.009
0.018
0.03
0.0
0.09
0.18
0.3
0.0
0.009
0.018
0.03
FGSM
PGD
BIM
Accuracy (%)
LIF
Accuracy (%6
Accuracy (%)
60
HeterLIF
60
60
Ours
20
20
20
Figure 7: Performance of the model on SHD using a feedforward network under perturbations of
different distribution probabilities p or attack strengths
Additive
Subtractive
Mixed
Accuracy (%)
80
LIF
ALIF
Accuracy (%)
80
Accuracy (%)
80
60
HeterLIF Ours
60
60
20
20
20
0.0
0.009
0.018
0.03
0.0
0.09
0.18
0.3
0.0
0.009
0.018
0.03
FGSM
PGD
BIM
80
ALIF
Accuracy (%)
80
80
Accuracy
60
HeterLIF
60
Accuracy
60
40
40
40
20
0.0
0.009
0.018
0.03
0.0
0.009
0.018
0.03
0.0
0.009
0.018
0.03
Figure 8: Performance of the model on SHD using recurrent network under perturbations of dif- ferent distribution probabilities p or attack strengths e.
As illustrated Fig. Fig. 3 our neuron model outperforms others in terms of accuracy under increasing perturbation intensities. It consistently maintains the highest accuracy and exhibits the lowest degradation across within a reasonable perturbation range. For example, under an additive noise perturbation of 0.004 (Poisson rate of 1 Hz), the performance loss the SHD dataset remains within 15% for all network architectures. These results demonstrate that our neuron model is able to filter out interfering information while maintaining excellent effective information transfer efficiency, highlighting the effectiveness the proposed bio-inspired gating mechanism in enhanc- ing the model's robustness variety of perturbation patterns.
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A.4 EFFICIENCY ANALYSIS
A.4.1 EVALUATION OF ENERGY EFFICIENCY
Energy efficiency and hardware deployment are indeed important future directions. We provide a complete analysis for transparency and practical reference as Tab. and Tab. 8 For the empiri- cal estimate, we evaluate two-layer network (128-128 hidden units, i.c., m = n 128) on the SHD dataset. We measure the laser-wise firing rates of the LIF and DGN neurons over 32 epochs, obtaining: LIF: [4.43%, 0.54%), DGN: [7.07%, 1.19%]. s-DGN: (6.38%, 1.41%].
To obtain the total energy cost, we plug these firing rates into the above formulas and adopt the 45nm CMOS process estimates from Horowitz 2014), where the energy cost of an AC and MAC operation are EAC = 0. and EMAC 4.6 pl. respectively. This yields the empirical energy val- ues reported in the last column of Table 8 Although DGN and s-DGN incur slightly higher energy than LIF due to the additional gating-related operations as expected, their energy consumption re- mains orders of magnitude lower than that of an LSTM, thus preserving the core energy-efficieny advantage of spiking neural networks.
Table 7: Comparison of neuron dynamics and per-step energy cost of LIF, LSTM, DGN and s-DGN models. The and denote the numbers of input and output neurons. Frin and Frou represent the firing rate of input and output neurons. EAC and EMAC are the energy cost of AC and MAC operations, respectively.
Neuron Model
Dynamies
Step Cost
Vi pm pt- + w.m.n.t
+EmAC+
LIF
Whe s/-1 - 127
(mnFra Frn + Fruu + Frow)) Eac
pi 4(1 9 C.Ti0 Om (111
mnF Exc + FAF
DGN
V pi V/-11 W-m.a 2|-1
nEsscc
+ Whet
0z--1
(mnFrin + FnoFr FF EsC
pi p(1 0 C. 2/ Ch. =f
mnFr FAc + FraFroF FAC
S-DGN
V/¹ = ri V,-1 + (FACTT""
nEMAC
+ (E nr (7:0)) 1-1 --'-1
(mnFrn + Frar + nF FFFrou Esc
fe 0r (W + U,ly-1 by)
re(m + 12) EMAC
4 0, (W,:; + U,h--1 + b.)
n(m + nt) EMAC
00 0g (Woz: + Uphe-1 + bo)
n(m + n) Exac
LSTM
& (WW,,, , + Uch + be)
m + n.) FMAA
Q Je ① 0-1 + 0
2n 2nEMAC
h. 0r @ (c.)
n.EAC
Table Total theoretical energy cost and empirical energy measurements for LIF, LSTM, DGN and s s-DGN models.
Neuron Model
Total Cost
Empirical Cost (n.J)
(mnFrr + FrrFron + TFFr EAC.
LIF
1.32 (2mmFrin + 2nmFFrom + TFFruu) FrCc
EEMAC
DGN
3.03
+ nEMA:
(2mmnPri + 2wnsFrom + WFFrou) EAC
S-DGN
2.89
+ nĖMAE
LSTM
(1mn 4nm + 3m) FMAC
604.7
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A.4.2 TRAINING AND INFERENCE TIME
We also analyzed training and inference time in Tab. 9 The results, averaged over 250 training steps on the SHD dataset, show that runtime differences between models are minimal, while our approach provides superior robustness and computational capability in SNNs our core contributions that are demonstrated in the main-text results.
Table Training and Inference Time on the SHD dataset
Arcitecture
Neuron
Train Time(s)
Test Time(s)
LIF
15.61 # 1.49
5.17 : 0.69
HeterLIF
18.99 = 1.80
5.45 = 0.68
FF
ALIF
21.56 2.01
5.80 0.36
DGN(Ours)
22.58 # 1.48
5.93
LIF
16.88 3.39
5.25 + 0.95
HeterL.I
18.75
5.29 + 0.41
Rec
ALIF
22.45 4.36
5.58 = 0.98
DGN(Ours)
28.59 + 5.00
6.58 2 0.84
A.4.3 DISCUSSION ON NEUROMORPHIC IMAGE DATASETS
To further evaluate the generalization ability of our proposed neuron, we extend our experiments to the neuromorphic DVS-Gesture dataset. This dataset features rich temporal dynamics, making it suitable benchmark validate whether the advantages of our design also hold beyond speech tasks.
Following prior work (Fang et al., 2021), adopt the same network architecture to ensure fair comparison and avoid confounding factors from architectural modifications. Since our focus lies in analyzing the behavior of neurons themselves, deliberately keep the network simple and consis- tent with the main text. We report preliminary reproduction results, with identical noise settings as defined in the main experiments.
Table 10: Results the DVS-Gesture dataset. We report clean accuracy (%) and robustness under various noise and adversarial perturbations. Our proposed DGN achieves the highest accuracy and robustness across most settings.
Neuron
Clean Acc.
Additive
Subtractive
Mixed
FGSM
PGD
BIM
LIF
93.06
89.58
92.01
89.24
89.24
88.54
88.89
HeterLIF
94.79
91.67
92.36
90.97
86.81
86.46
85.42
ALIF
93,40
90.97
89.58
91.32
87.85
88.89
88.19
DGN (ours)
95.14
91.67
92.36
92.36
92.01
89.93
89.24
As shown Table I0, our neuron consistently achieves superior accuracy and robustness, demon- strating that the proposed mechanism generalizes well beyond speech datasets and retains its advan- tage on image-based neuromorphic tasks.
A.5
MORE EXPERIMENTAL RESULTS
Tab. 16 present the experimental results of reproduced models (with clean accuracies shown in Tab. under three types of noise and three types of adversarial attacks. In each table, the first row indicates the strength of the noise or attack. For additive and mixed noise, we vary the strength from to 0.03; for subtractive noise, the range is 0 to 0.3. For all adversarial attack methods, the perturbation strength ranges from to 0.003.
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Table 11: Additive Noise full experiment data through different p.
Net
Method
0.003
0.006
0.009
0.012
0.015
0.018
0.021
0.024
0.027
0.03
Datasets:
Ti46alpha
LIF
88.72
65.02
33.59
14.83
8.74
6.43
5.34
4.72
4.45
4.19
HeterLII
78.7
51.22
28.94
17.49
12.2
9,65
9
7.99
7.41
7.28
ALIF
88.35
61.62
25.67
12.49
8.34
6.73
5.85
5.29
5.15
4.81
DGN
92.7
87.21
77.07
63.77
52.02
40.65
31.11
24.62
19.06
17.02
RNN
18.8
7.53
5.2
4.4
4.24
4.54
4.56
4.49
4.84
4.28
LSTM
88.65
64.84
43.16
30.07
21.63
16.08
12.46
10.79
10.44
9.52
LIF
80.63
62.34
38.98
22.46
14.65
11.21
8.8
7.69
7.19
7.88
Rec
HeterLIF
77.87
61.49
44.88
30.53
21.62
17.07
14.16
12.58
11.19
11.22
ALIF
80.51
64.8
39.98
19.54
9.97
6.83
5.13
4.39
4.24
4.07
DGN
91.86
81.29
64.58
44.13
27.68
17.36
10.3
7.62
5.81
4.95
Datasets:
TIDIGITS
LIF
84.9
46.83
31.73
28.49
25.07
22.83
21.81
19.91
18.52
17.22
HeterI.I
91.43
77.49
63.19
52.38
43.27
35.64
30.1
26.15
23.36
21.06
ALIF
88.81
63.29
48.04
40.67
34.33
30.59
27.84
25
22.65
21.84
DGN
97.47
95.34
91.36
86.65
78.67
71.77
67.12
62.77
56.8
52.46
RNN
35.5
23.64
19.37
17.56
16.33
16.43
16.97
19
17.74
15.82
LSTM
89.49
65.12
47.8
29.47
19.83
16.48
15.16
13.35
11.95
12.65
Rec
LIF
95.06
73.23
32.6
19.46
16.42
13.73
12.77
11.6
10.58
9.88
HeterLIF
90.71
78.97
64.95
52.78
44.2
36.82
30.73
26.51
23.4
20.9
ALIF
93.99
84.01
54.44
20.96
10.76
9.48
9.43
9.33
9.3
9.26
DGN
97.56
94.84
89.12
81.06
70.38
58.59
48.28
37.74
30.34
24.71
Datasets:
SHD
LIF
60.39
29.93
15.42
10.93
7.77
5.91
4.89
4.85
4.85
4.85
HeterLIF
49.87
25.49
14.63
8.55
5.8
4.85
4.85
4.85
4.85
4.85
ALIF
65.57
40.25
26.51
17.97
14.74
13.72
12.57
11.27
10.32
9.26
DGN
77.6
59.46
39.18
25.86
19.55
15.43
12.46
11.29
10.43
9.56
RNN
39.26
27.47
25.27
24.48
24.15
22.17
19.92
16.3
11.38
7.75
LSTM
56.09
41.61
32.2
29.56
28.82
27.63
26.7
25.23
23.36
21.24
LIF
46.8
9.24
4.99
4.85
4.85
4.85
4.85
4.85
4.85
4.85
Rec
HeterLIF
66.8
39.57
26.77
22.17
19.13
17.43
15.17
13.16
10.63
9.35
ALIF
73.48
46.59
37.18
30.24
25.64
20.87
18.03
14.98
11.6
9.69
DGN
86.49
78.97
56.41
35.46
29.67
27.92
24.44
20.22
18.04
16.14
Datasets: SSC
LIF
34.37
21.64
15.66
12.76
11
9.94
8.91
8.58
7.58
7.18
HeterLIF
41.72
26.96
19.45
15.43
12.95
10.77
9.1
7.89
7.11
6.34
FF
ALIF
37.51
25.65
19.72
15.84
13.71
11.92
11.14
10.37
10.3
9.84
DGN
49.62
31.74
19.47
13.73
11.15
9.73
8.67
7.87
7.26
6.68
RNN
57.13
29.15
19.29
15.68
14.39
13.74
13.18
12.34
11.69
11.03
LSTM
60.01
36.53
23.2
18.26
16.56
15.65
14.71
14.23
13.62
13.15
LIF
39.38
26.27
20.09
17.45
15.9
14.46
13.38
12.13
11.67
10.81
Rec
HeterLIF
45.27
25.91
18.75
16.31
14.59
12.50
10.36
8.33
7.92
7.15
ALIF
41.36
26.23
17.98
14.08
12.09
11.06
10.36
9.73
9.27
9.08
DGN
45.58
27.62
18.41
16.55
15.83
14.96
14.92
14.34
13.59
12.19
28

---

Published as a conference paper at ICLR 2026
Table 12: Subtractive Noise full experiment data through different p.
Net
Method
0.03
0.06
0.09
0.12
0.15
0.18
0.21
0.24
0.27
0.3
Datasets:
Ti46AIpha
LIF
93.4
92.73
92.11
91.69
90.94
90.27
89.06
88.13
87.23
86.07
HeterL.I
93
92.16
91.73
91.36
90.42
90.13
88.98
88.81
87.41
85.91
ALIF
93.42
92.69
92.31
91.87
90.71
90.19
89.12
88.28
87.17
85.95
DGN
95.23
94.81
94.59
94.08
93.1
92.81
91.26
90.45
88.5
87.18
RNN
90.94
90.41
89.13
88.28
87.01
85.59
83.32
80.91
78.01
74.01
LSTM
95.5
95.04
94.29
93.8
92.51
90.92
88.14
84.94
80.6
75.09
LIF
90.4
89.19
87.6
87.37
84.91
83.64
81.09
78.26
74.84
71.25
Rec
HeterLIF
90.21
89.44
87.94
86.83
84.49
82.21
79.51
76.48
72.93
68.6
ALIF
88.71
87.34
86.03
84.99
82.81
80.36
76.5
74.41
69.89
65.78
DGN
95.74
95.31
94.48
94.72
93.79
93.38
92.43
91.67
90.72
89.18
Datasets:
TIDIGITS
LIF
97.07
96.47
96.31
96.29
96.02
95.61
95.39
95.12
95.1
93.7
HIterI.I
96.37
95.78
95.39
95.23
94.48
93.83
92.93
92.58
90.56
89.37
ALIF
96.46
96.25
96.22
96.41
95.61
95.1
94.74
95
94.91
93.17
DGN
98.27
98.38
98.31
98.03
97.72
96.98
96.98
95.74
94.63
93.7
RNN
96.95
96.72
96.17
95.66
95.16
93.87
92.81
92.12
90.14
86.76
LSTM
97.97
97.77
97.19
96.42
95.74
94.57
91.73
89.07
84.82
79.25
Rec
LIF
97.65
97.34
97.11
96.67
96.12
94.93
94.57
93.21
91.83
89.6
HeterLIF
95.69
94.8
94.17
93.33
92.5
91.12
89.71
87.22
85.37
82.59
ALIF
96.89
96.43
95.69
95.34
94.11
94.07
92.67
91.14
89.19
86.19
DGN
98.98
98.93
98.77
98.63
98.42
98.48
97.58
97.6
97.62
96.7
Datasets:
SHD
LIF
77.42
75.6
74.12
72.34
70.34
68.13
64.75
62.71
59.84
56.32
HeterLIF
76.64
75.11
74.21
71.89
69.1
66.78
65.37
61.25
59.11
54.91
ALIF
77.7
75.56
74.94
72.75
70.85
67.64
64.67
62.57
58.43
55.08
DGN
84.3
83.65
82.17
81.02
78.35
76.03
73.69
70.57
67.83
64.05
RNN
75,88
74.41
72.5
70.83
67.87
66.71
62.92
58.96
55.77
52.29
LSTM
86.04
85.34
84.08
81.98
80.68
77.49
75.37
72.45
69.13
64.58
LIF
74.88
73.64
72.66
71.17
68.35
67.92
65.24
62.45
60.41
57.44
Rec
HeterLIF
79.53
77.59
75.61
73.3
71.55
69.34
66.79
63.61
60.7
58.19
ALIF
81.36
80.52
79.08
77.52
74.91
72.94
70.44
68.67
65.4
63.32
DGN
86.9
86.61
85.04
82.67
79.7
76.84
73.45
69.53
65.88
61.91
Datasets: SSC
LIF
46.98
46.68
45,94
45.39
44.3
42.94
41.83
40.16
38.57
36.46
HeterLIF
55.3
54.59
53.81
53.3
52.15
50.5
49.45
47.85
46.02
43.76
ALIF
48.52
47.91
47.12
46.33
44.74
43.86
42.72
40.91
39.21
37.03
DGN
58.51
58.32
57.87
57.28
56.21
54.81
53.37
51.15
49.27
47.06
RNN
72.6
72.25
71.63
71.21
70.31
69.24
68.22
66.75
65.03
63.29
LSTM
75.61
75.47
74.7
74.12
73.4
72.25
71.53
70.06
68.76
67.5
Rec
LIF
52.73
52.2
51.96
51.42
50.98
50.38
49.71
48.24
46.93
45.72
HeterLIF
57.35
56.74
56.38
55.76
54.88
53.81
53.05
52.04
50.69
49.32
ALIF
55.1
54.91
54.58
53.54
52.64
51.77
49.95
48.7
47
45.55
DGN
66.32
66.25
65.47
64.33
64.14
62.98
61.71
60.15
58.99
57.12
29

---

Published as a conference paper at ICLR 2026
Table 13: Mixed Noise full experiment data through different p.
Net
Method
0.003
0.006
0.009
0.012
0.015
0.018
0.021
0.024
0.026
0.03
Datasets:
Ti46Alpha
LIF
88.17
60.72
25.61
10.46
6.78
5.38
4.9
4.25
4.12
HeterL.I
76.49
45.91
22.95
13.8
9.58
8.11
7.39
6.96
6.11
6.08
ALIF
87.62
56.23
19.87
9.65
7.22
5.82
5.28
4.73
4.57
4.52
DGN
92.69
85.66
73.33
55.9
40.52
28.24
18.92
12.75
9.81
7.46
RNN
17.8
6.93
4.71
4.42
4.18
4.09
4.14
4.14
4.14
4.14
LSTM
87.97
62.97
40.86
26.71
16.95
11.29
9.16
7.54
6.57
6.19
LIF
79.03
57.59
32.31
18.7
12.19
8.87
7.32
6.86
6.51
6.31
Rec
HeterLIF
75.95
55.96
36.95
25.09
16.94
13.34
11.31
10.64
9.05
8.06
ALIF
79.51
60.85
34.29
18.02
8.64
6.38
4.86
4.39
4.49
4.08
DGN
90.89
78.12
56.54
33.76
17.88
9.62
6.6
5
4.32
4.17
Datasets:
TIDIGITS
LIF
82.54
44.2
30.1
25.91
22.58
20.23
18.96
17.09
15.56
14.34
HIterI.IF
89.66
72.78
57.39
44.48
34.91
30.04
26.35
23.49
21.06
19.67
ALIF
88.34
60.58
44.77
34.33
28.67
26.16
23.13
19.52
18.02
16.37
DGN
97.64
95.43
91.74
82.88
74.95
66.05
57.66
52.86
45.13
40.3
RNN
34.54
21.66
17.32
15.18
15.03
15.93
15.05
14.93
14.48
13.73
LSTM
90.07
64.77
45.03
26.72
17.92
15.5
13.65
12.42
11.84
11.45
Rec
LIF
93.88
67.68
30.1
16.31
13.04
12.04
11.72
10.48
9.97
9.73
HeterLIF
88.5
73.05
58.73
44.38
33.68
26.08
21.93
18.95
16.69
14.93
ALIF
91.64
79.25
48.57
15.86
10.33
9.45
9.49
9.26
9.35
9.22
DGN
97.92
93.86
87.12
75.09
59.28
44.41
30.8
24.58
19.04
17.27
Datasets:
SHD
LIF
60.18
31.44
15.67
11.4
7.77
6.19
5,09
4.85
4.85
4.85
HeterLIF
50.02
25.58
14.41
8.44
6.1
5.02
4.85
4.85
4.85
4.85
ALIF
65.59
39.4
25.52
17.84
15.18
13.08
10.63
9.63
9.46
9.41
DGN
78.01
58.87
37.77
24.73
19.35
15.13
13.48
11.83
10.49
9.15
RNN
37.98
28.06
24.86
23.49
21.56
19.63
17.38
15.86
14.05
11.01
LSTM
56.03
39.23
30.56
26.44
25.57
24.62
23.05
20.5
18.33
17.08
LIF
48.04
9.25
4.85
4.85
4.85
4.85
4.85
4.85
4.85
4.85
Rec
HeterLIF
67.89
38.87
25.4
21.47
19.89
18.11
14.28
11.77
9.8
8.48
ALIF
73.68
47.28
36.81
30.72
26.93
23.18
19.69
15.51
10.02
7.63
DGN
86.81
79.35
56.42
32.83
27.13
25.41
23.26
21.2
18.65
15.37
Datasets:
SSC
LIF
34.67
21.25
15
11.69
10.13
8.97
8.17
7.33
6.41
5.8
HeterLIF
42.05
26.65
18.36
14.3
11.66
9.51
7.88
6.66
5.73
5.09
ALIF
37.74
25.56
19.03
14.52
12.6
10.61
9.53
8.91
8.35
7.93
DGN
50
31.3
18.1
12.45
10.09
8.72
7.88
6.86
6.06
5.38
RNN
57.69
29.66
18.81
14.85
13.33
12.64
11.61
10.38
9.67
8.79
LSTM
60.16
35.65
21.35
15.11
12.82
11.6
10.46
9.29
8.78
8.43
Rec
LIF
39.33
25.98
19.23
16.5
14.78
13.32
12.03
10.66
9.74
8.73
HeterLIF
45.13
25.09
17.54
14.53
12.31
9.95
7.31
5.66
5.23
5.17
ALIF
41.87
26.09
17.71
13.69
11.59
10.39
9.55
8.58
8.26
7.66
DGN
46.55
27.27
17.20
14.94
13.61
12.54
11.57
11.28
9.84
7.79
30

---

Published as a conference paper at ICLR 2026
Table 14: FGSM attack full experiment data through different
Net
Method
0.003
0.006
0.009
0.012
0.015
0.018
0.021
0.024
0.027
0.03
Datasets:
Ti46Alpha
LIF
58.31
19.33
2.27
0.17
0.08
0.11
0.14
0.17
0.18
0.2
HeterLII
33.45
4.92
0.38
0.14
0.06
0.05
0.05
0.02
0.02
0.03
ALIF
58.73
19.34
3.84
0.39
0.11
0.09
0.09
0.09
0.09
0.08
DGN
74.74
50.81
37.85
30.4
24.96
21.07
18.29
16.34
14.66
13.56
RNN
0.59
0.41
0.39
0.38
0.35
0.33
0.33
0.33
0.33
0.33
LSTM
50.52
12.16
6.84
4.74
3.44
2.93
2.67
2.44
2.16
2.1
LIF
29.88
3.85
0.61
0.39
0.21
0.17
0.12
0.17
0.17
0.09
Rec
HeterLIF
25.25
2.63
0.36
0.12
0.08
0.08
0.08
0.11
0.11
0.11
ALIF
24.35
2.42
0.44
0.18
0.14
0.12
0.14
0.11
0.14
0.15
DGN
52.57
21.17
10.11
6.41
5.22
4.61
4.2
4.15
3.89
3.52
Datasets:
TIDIGITS
LIF
39.53
3.48
0.54
0.27
0.08
0.08
0.08
0.04
0.04
0.04
HIterI.IF
52.48
12.97
3.17
1,46
0.86
0.59
0.35
0.23
0.16
0.12
ALIF
42.5
3.22
0.31
0.08
0.04
0.04
0.04
0.04
0.04
0.04
DGN
90.35
71.52
52.25
40.84
35.49
31.92
29.31
27.39
24.71
23.05
RNN
9.89
2.59
0.59
0.16
0.04
0.04
0.04
0,04
0.04
0.04
LSTM
64.97
22.48
9.57
5.3
4.48
4.25
3.66
2.32
2.51
2.32
Rec
LIF
26.55
2.45
1.33
1.33
1.25
1.08
1.01
0.93
0.97
0.97
HeterLIF
8.76
1.62
2.67
2.91
2.08
1.54
1.34
1.19
1.03
0.95
ALIF
25.04
2.04
0.23
0.04
0.04
0.04
0.04
0.08
0.08
0.08
DGN
89.4
61.91
29.62
11.48
4.18
3.08
3.28
3.55
4.2
4.51
Datasets:
SHD
LIF
51.55
35.57
24.22
17.51
12.09
8.14
5.67
3.92
2.49
1.65
HeterLIF
52.23
36.2
25.35
18.55
12.76
8.94
6.98
5.2
3.9
3.25
ALIF
53.31
38
26.89
20.06
14.32
9.42
6.86
5.07
3.92
2.86
DGN
63.81
49.29
37.15
27.12
20.08
15.55
12.56
10.55
9.27
7.73
RNN
17.35
6.61
4.03
3.38
2.84
2.53
2.1
1.99
1.91
1.86
LSTM
39.27
19.19
8.38
3.81
2.04
1.56
1.52
1.52
1.34
1.12
LIF
17.78
4.45
1.35
0.8
0.49
0.39
0.3
0.26
0.22
0.26
Rec
HeterLIF
44.76
24.15
13.45
7.53
5.03
3.37
2.27
1.48
1.27
1.14
ALIF
52.2
28.12
17.55
11.84
9.89
8.65
7.54
6.93
6.45
6.15
DGN
69.45
57.82
45.46
36.65
30.25
25.9
22.45
19.47
17.03
14.82
Datasets: SSC
LIF
14.77
4.37
1.44
0.54
0.18
0.05
0.03
0.01
0
0
HeterLIF
7.87
0.54
0.03
0.01
0.01
0.01
0
0
ALIF
12.61
2.6
0.54
0.1
0.01
0
0
0
0
0
DGN
29.44
12.5
5.58
2.77
1.58
0.93
0.59
0.42
0.33
0.24
RNN
20.59
4.96
2.18
1.31
0.73
0.46
0.26
0.19
0.17
0.14
LSTM
29.41
11.2
7.25
5.9
4.9
4.19
3.75
3.24
2.83
2.55
Rec
LIF
12.83
2.87
0.86
0.26
0.07
0.04
0.02
0.02
0.01
0.01
HeterLIF
15.53
2.14
0.20
0.04
0
0
0
0
0
ALIF
17.1
4.64
1.39
0.49
0.21
0.13
0.05
0.02
0.03
0.01
DGN
30.99
12.63
6.49
3.99
2.77
2.05
1.60
1.26
0.94
0.69
31

---

Published as a conference paper at ICLR 2026
Table 15: PGD attack full experiment data through different
Net
Method
0.003
0.006
0.009
0.012
0.015
0.018
0.021
0.024
0.027
0.03
Datasets:
Ti46Alpha
LIF
53.51
10.04
0.11
0
0
0
HeterLII
20.62
1.38
0.05
0.02
0
0
0
0
0
ALIF
11.73
0.53
0.03
0
0
0
0
0
0
DGN
68.54
31.97
20.58
15.52
11.44
9.48
8.36
7.82
6.95
6.64
RNN
0.02
0
0
0
0
0
LSTM
41.94
3.09
0.95
1.05
1.28
0.91
0.51
0.26
0.12
0.03
Rec
LIF
18.75
1.09
0.56
0.29
0.09
0.03
0.02
0.03
0.06
HeterLIF
20.07
4.48
1.78
0.87
0.39
0.3
0.51
0.77
0.96
1.17
ALIF
16.97
0.42
0.11
0.03
0.08
0.02
0
0.03
0.06
DGN
39.55
10.06
3.02
1.21
0.43
0.54
0.41
0.3
0.3
0.3
Datasets: TIDIGITS
LIF 15.39
0.54
0
0
0
HeterL.I
43.94
5.64
0.7
0.08
0
0
0
0
0
ALIF
19.8
0.33
0
0
DGN
86.76
45.81
22.49
14.75
12.72
11.76
11.15
10.58
10.47
9.41
RNN
0
0
0
0
0
0
0
LSTM
60.66
3.71
0.9
0.64
0.12
1.12
0.37
0.16
0
Rec
LIF
61.79
10.74
1.07
0.16
0
0
0
0
0
HeterLIF
36.62
9.06
2.57
0.44
0.16
0.04
0.08
0
0.12
ALIF
62.82
10.88
1.07
0.12
0.35
0.04
0
0
0
DGN
87.52
44.74
6.84
0.98
0.27
0.08
0.04
0.04
0.04
Datasets:
SHD
LIF
47.87
29.53
17.78
9.51
5.74
3.18
1.5
0.45
0.17
HeterLIF
50.78
30.91
16.92
11.83
9.25
5.65
3.14
2.12
1.15
0.78
ALIF
51.51
32.58
20.64
13.58
6.79
3.42
2.27
1.47
0.97
0.84
DGN
61.59
43.86
25.3
14.22
8.81
5.72
3.79
2.4
1.14
0.63
RNN
11.93
3.52
2.73
2.08
2.17
1.38
0.3
0.22
0.04
LSTM
32.01
9.54
2.64
1.36
0.61
0.22
0.26
0.17
0.04
LIF
30.59
7.29
1.12
0.22
0.09
0.22
0.43
0.79
1.78
2.45
Rec
HeterLIF
49.12
28.76
14.37
7.83
5.03
3.67
2.49
1.6
1.07
1.17
ALIF
58.01
34.6
19.72
10.85
6.1
3.44
2.1
1.63
1.5
1.46
DGN
66.13
51.59
31.34
18.56
11.81
7.97
6.32
6.04
4.37
4.05
Datasets: SSC
LIF
10.67
1.35
0.13
0
0
0
0
HeterLIF
3.45
0.03
0
0
FF
ALIF
8.58
0.7
0.04
0
0
0
0
0
0
DGN
23.64
5.23
1.6
0.45
0.05
0.01
0
0
RNN
16.87
4.19
1.68
0.38
0.38
0.07
0
0
LSTM
29.41
11.2
7.25
5.9
4.9
4.19
3.75
3.24
2.83
2.55
Rec
LIF
8.35
0.88
0.14
0.02
0
0
0
0
HeterLIF
10.26
0.53
0.12
0.02
0
0
0
0
ALIF
12.89
2.14
0.6
0.08
0.03
0
0
0
0
DGN
20.55
5.65
2.22
0.74
0.21
0.03
0.02
0.01
0.01
0.01
32

---

Published as a conference paper at ICLR 2026
Table 16: BIM attack full experiment data through different E.
Net
Method
0.003
0.006
0.009
0.012
0.015
0.018
0.021
0.024
0.027
0.03
Datasets:
Ti46Alpha
LIF
53.16
10.18
0
0
0
HeterLII
22.03
1.52
0.12
0
0
0
0
0
0
ALIF
55.25
11.89
0.61
0.02
0
0
0
0
0
DGN
68.35
13.98
0.23
0.02
0
0
0
0
RNN
0.02
0.02
0
0
0
0
0
LSTM
42.92
3.78
0.86
0.29
0.18
0.24
0.08
0.03
0.08
0.08
LIF
17.31
1.31
0.76
0.33
0.12
0.03
0.03
0.03
0.02
0.02
Rec
HeterLIF
19.78
4.86
1.8
0.97
0.62
0.49
0.29
0.11
0.09
0.08
ALIF
16.48
1.37
0.15
0.05
0.12
0.17
0.05
0.03
DGN
39.79
10.54
3.24
1.33
0.89
0.51
0.45
0.32
0.32
0.33
Datasets:
TIDIGITS
LIF
15.95
0.41
0
0
0
0
HIterI.IF
43.68
5.64
1.84
0.16
0.04
0
0
0
0
0
ALIF
19.42
0.25
0
0
DGN
86.88
45.93
23.26
15.39
13.29
12.27
12
11.84
11.65
11.73
RNN
0
0
0
0
0
0
0
0
LSTM
61.01
6.06
1.45
1.29
0.59
0.66
0.43
0.27
0.27
0.27
Rec
LIF
60.7
11.72
1.62
0.16
0.08
0.08
0.04
0
0
0
HeterLIF
35.74
9.23
1.88
0.2
0.04
0
0.08
0.04
0
0
ALIF
63.18
12.2
0.72
0.08
0.41
0.08
0.08
0.08
0
0
DGN
87.68
46.45
9.65
2.15
0.51
0.16
0.08
0.08
0.08
0.08
Datasets:
SHD
LIF
47.92
29.75
18.59
11.15
7.34
4.57
2.88
1.65
1.04
0.86
HeterLIF
50.89
30.87
16.72
12.65
10.01
7.28
4.98
3.66
2.66
1.73
ALIF
51.57
32.67
21.03
14.62
8.68
4.98
2.88
1.88
1.1
0.8
DGN
61.44
44.05
28.66
18.23
11.93
7.65
5.09
3.77
2.54
1.8
RNN
13.94
3.93
2.34
1.27
0.99
0.86
0.6
0.45
0.22
0.17
LSTM
33.37
13.49
5.56
2.12
0.63
0.09
0
0
0
LIF
31.45
8.9
2.24
0.84
0.44
0.23
0.09
0.09
0.04
0
Rec
HeterLIF
49.1
29.85
16.03
10.46
8.06
6.43
5.32
4.14
2.99
2.03
ALIF
58.31
35.19
21.12
13.31
9.31
6.75
5.32
4.79
4.31
4.09
DGN
66.34
51.3
31.8
21.54
115.4
11
7.89
6.07
4.8
3.55
Datasets: SSC
LIF
10.71
1.48
0.18
0.01
0
0
0
0
0
HeterLIF
3.59
0
0
ALIF
8.56
0.84
0.04
0
0
0
0
0
0
DGN
23.72
5.89
1.9
0.62
0.13
0.03
0.01
0
RNN
17.23
4.65
1.81
0.51
0.15
0.02
0.01
0.01
0.01
LSTM
18.78
4.08
1.41
0.46
0.2
0.13
0.09
0.06
0.04
0.04
Rec
LIF
8.14
0.93
0.23
0.06
0.01
0
0
0
0
HeterLIF
10.31
0.59
0.09
0.01
0
0
0
0
ALIF
12.92
2.26
0.62
0.15
0.02
0
0
(
0
0
DGN
21.12
5.93
2.36
0.96
0.38
0.17
0.07
0.06
0.05
0.03
33


Reviews:
[{"review_id": "D88k7FqumO", "reviewer": "Reviewer_wod3", "summary": "This paper proposes the Dynamic Gated Neuron (DGN) model that integrates a bio-inspired dynamic conductance mechanism to enhance the robustness and performance of spiking neural networks (SNNs), outperforming traditional LIF models and other baselines in anti-noise tasks and temporal benchmarks.", "questions": "The low power consumption of SNNs lies in simplifying multiplication to addition; however, in my view, D (as denoted in the DGN model) behaves like a floating-point number, which causes SNNs to lose this characteristic.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1.  This paper introduces a biologically grounded Dynamic Gated Neuron (DGN) model with a dynamic conductance mechanism, which enables adaptive input filtering and noise suppression\n\n2. The DGN-based spiking neural networks (SNNs) demonstrate superior performance and robustness across multiple benchmarks (e.g., TIDIGITS, SHD) under various noise types and adversarial attacks.\n\n3. The comparison in the figures is very intuitive, allowing the innovative points of the authors' architecture to be clearly identified.", "weaknesses": "1.  The authors claim that the DGN is a generalized spiking neuron model, yet there is no evaluation on relevant computer vision datasets (NMNIST, CIFAR, N-CALTECH).\n\n2.  This paper lacks an evaluation of energy consumption, and it remains unaddressed whether the newly introduced unit (DGN) will significantly increase power consumption, as reflected in Equations 5-8.", "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": "5xTJSOni4w", "reviewer": "Reviewer_mPha", "summary": "This paper provides the Dynamic Gated Neuron (DGN), a novel spiking unit in which membrane conductance evolves in response to neuronal activity. DGN appears enhanced stochastic stability compared to standard LIF models.", "questions": "1. Does the improved model have a special or proprietary training mechanism that can find a solution with specific functions?", "limitations": "", "rating": 2, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "This paper proposes an improved model. Intuitively, this model introduces more computation but does not increase the number of parameters, and experiments show improved performance. These findings suggest that the research presented in this paper represents a worthwhile improvement.", "weaknesses": "1. This paper is hard to follow.\n\n2. Theoretical analysis is limited. There is a great deal of analysis work on SNNs that combats randomness and noise.\n\n3. This paper proposes only one improved model. To my understanding, this model does not appear to have a special training mechanism; it simply uses the traditional global training method for SNNs. Based on past experience, this approach makes it difficult to achieve new functionalities. The authors' claimed intrinsic adjustment mechanism is merely a claim and cannot be verified experimentally or proven theoretically.\n\n4. The experiments in this paper are insufficient; comparative experiments with the same architecture or parameter set are needed. Furthermore, I don't understand why it's necessary to compare it with artificial neural network models like LSTM that take real-number sequences as input.", "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": "DdgGMgaWAk", "reviewer": "Reviewer_UpB6", "summary": "This paper introduces the Dynamic Gated Neuron (DGN), a novel spiking neuron model that incorporates dynamic conductance mechanisms inspired by biological neurons. The authors propose that this mechanism acts as an intrinsic gating function, similar to forget gates in LSTMs, enabling adaptive control of information flow and memory retention. The model is rigorously evaluated on multiple speech and neuromorphic benchmarks, demonstrating state-of-the-art performance and superior robustness against noise and adversarial attacks. Theoretical analysis using stochastic differential equations further supports the noise resilience of DGN. The work is well-motivated, methodologically sound, and empirically thorough.", "questions": "1. Could the dynamic conductance mechanism be integrated with other advanced SNN architectures (e.g., attention-based or transformer-like SNNs)?\n\n2. Have you considered evaluating the model on neuromorphic hardware to assess its real-world efficiency and latency?\n\n3. The paper mentions a \"simplified DGN\" (s-DGN) in the appendix. Could this be discussed more in the main text to highlight trade-offs between performance and complexity?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The idea of using dynamic conductance as a gating mechanism in spiking neurons is novel and well-grounded in neurophysiology. The connection to LSTM-like gating is insightful and bridges bio-inspired models with artificial neural networks.\n\n2. The paper provides a solid theoretical analysis of the model’s stability under noise, using stochastic differential equations and variance comparisons with LIF neurons.\n\n3. Extensive experiments on multiple datasets (TIDIGITS, SHD, SSC, etc.) across both feedforward and recurrent architectures demonstrate the model’s effectiveness in accuracy and robustness.\n\n4. The paper includes a wide range of noise and adversarial attack scenarios, showing consistent superiority over existing SNN models and even LSTMs in some cases.\n\n5. The appendix includes detailed derivations, training procedures, hyperparameters, and noise generation algorithms, which facilitate reproducibility.", "weaknesses": "1. Although the model is compared with several recent SNN variants, it would be beneficial to include more baselines  (e.g., spiking-based Transformers).\n\n2. The DGN model introduces additional parameters and computational overhead compared to LIF. A more detailed analysis of energy efficiency or inference speed on neuromorphic hardware would strengthen the practical contribution.\n\n3. Lack of pseudocode for model computation.\n\n4. Why does DGN perform relatively poorly under the recurrent architecture on the SHD dataset?", "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": "dc43rIgmeB", "reviewer": "Reviewer_1AVk", "summary": "This paper introduces the Dynamic Gated Neuron (DGN), a novel spiking neuron model that incorporates dynamic membrane conductance as a biologically-motivated gating mechanism. DGN modulates membrane decay via activity-dependent conductance, enabling selective information retention and improved robustness to noise. The authors connect DGN behavior to gating in LSTMs, analyze stochastic stability, and present empirical results on many temporal datasets, showing improved accuracy and robustness under noise and adversarial attacks.", "questions": "It is not very clear how the DGN model is used to build both feedforward and recurrent networks. From Table 1, recurrent DGNs appear to outperform their feedforward counterparts. Could the authors clarify how the recurrent version is constructed and trained? Additionally, is the recurrent DGN more stable or easier to train compared to the feedforward architecture, and if so, why?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. The work is significant, as it draws clear inspiration from neuroscience, building on established literature on activity-dependent ion-channel plasticity, to introduce an input-dependent gating mechanism into spiking neuron models. Different from LSTM in conventional deep learning, the proposed DGN model is biologically grounded and novel. It may have a significant multi-disciplinary impact.\n\n2. This paper is well-written and easy to follow. It presents a well-structured bridge among conductance-based neurons in computational neurosience, LSTM in deep learning, and LIF neurons in SNNs, and clearly articulates the differences and connections among DGN, LSTM, and LIF, offering valuable multi-disciplinary insight.\n\n3. The work is supported by rigorous theoretical analysis demonstrating how dynamic conductance mechanisms enable adaptive leakage and noise suppression. \n\n4. Experiments are extensive in this work, with strong results supporting the claims and demonstrating the strong temporal processing capability and robustness of DGN. \n\n5. The proposed model is also advantageous in parameter efficiency, which is particularly valuable for SNNs operating in edge applications.", "weaknesses": "1. The authors seem not to discuss the feasibility of deploying the DGN model on neuromorphic chips. Since the computational advantages of SNNs are primarily achieved on such hardware, it would be valuable to discuss this aspect.\n\n2. The DGN model includes a numerical truncation function. However, it is unclear which specific type of truncation function is used in the experiments. What influence does the choice of truncation function have on performance? Are these functions hardware-friendly?", "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)