Hybrid-Neuromorphic Approach for Underwater Robotics Applications: A Conceptual Framework

Fakhreddine Zayer; Giulia De Masi; Hamad Karki; Jorge Dias; Sajid Javed; Vidya Sudevan

REVIEW 4 major objections 4 minor 40 references

Hybrid-Neuromorphic Approach for Underwater Robotics Applications: A Conceptual Framework

T0 review · 4 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper proposes a unified neuromorphic framework that would let underwater robots perceive, locate themselves, and respond to haptic guidance using spiking neural networks instead of conventional deep learning, cutting power and…

desk verdict A novel conceptual framework for neuromorphic underwater robotics that is honest about its gaps but undercut by a dimensionally invalid shared-control law and untested pose estimator. read the letter →

arxiv 2411.13962 v1 pith:AI447VYK submitted 2024-11-21 cs.RO

Vidya Sudevan , Fakhreddine Zayer , Sajid Javed , Hamad Karki , Giulia De Masi , Jorge Dias This is my paper · ORCID

classification cs.RO

keywords neuromorphiccomputingspikingneuralnetworksunderwaterroboticsposeestimationhapticfeedbackvisualservoingenergyefficiencyautonomousvehicles

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper argues that underwater robots could run on brain-inspired spiking neural networks instead of conventional deep learning, lowering power and computation while keeping comparable performance. It proposes a unified framework that chains neuromorphic modules for visibility enhancement, target detection, and vehicle pose estimation, feeding a shared-control law that combines visual servoing with haptic feedback. The framework is conceptual: the authors assemble existing spiking algorithms and theoretically formulate a new spike-based CNN-LSTM for pose regression. If the framework can be realized, underwater exploration, monitoring, and maintenance tasks would become accessible to longer-endurance, more autonomous vehicles.

What carries the argument

The load-bearing mechanism is the hybrid spike-based CNN-LSTM for vehicle pose estimation, whose output-layer spiking neurons retain their membrane potential $V_m$ as a continuous regression value instead of firing a spike; this makes regression possible in a spiking network and feeds the Position Based Visual Servoing controller with a translation vector and an axis-angle orientation. Around this, the framework organizes a conditional shared-control law: when the lateral tracking error $e_l(t)$ exceeds the threshold, the final command $v_{final} = v_{pbvs} + \tau_{final}$ combines the PBVS command with a haptic torque $\tau^j_x = -A(\theta_x(t)-\theta^d_x(t)) - B\dot{\theta}_x(t)$; otherwise only the PBVS command acts. The perception front end uses spiking dehazing and Spiking-YOLO for target detection.

What would settle it

A controller-in-the-loop test with a real or simulated ROV in turbid water would settle the claim: run the PBVS-plus-haptic law using the spike-based CNN-LSTM pose estimate, and if the lateral tracking error diverges or the vehicle loses the target, the framework's central feasibility claim fails.

Watch

Extended reading notes

Core claim

The paper's central claim is that a task-oriented underwater robot can be assembled from a neuromorphic perception-to-control pipeline in which every learned module is a spiking neural network. Building blocks include spiking-based dehazing for visibility enhancement, Spiking-YOLO for target detection, a global-to-local-style pose network translated to the spiking domain for target pose, and a newly formulated hybrid spike-based CNN-LSTM whose output neurons report continuous membrane potentials instead of spikes, enabling regression for vehicle pose. These feed a conditional control law that applies Position Based Visual Servoing and adds lateral haptic torque only when the target leaves the operator's allowed visual range. The authors claim this design can deliver comparable performance to conventional learning-based frameworks while reducing energy consumption and computational demand.

Load-bearing premise

The load-bearing premise is that spiking neural networks can perform underwater visibility enhancement, target detection, and vehicle pose estimation accurately enough to keep an underwater vehicle on target, which no experiment in the paper demonstrates.

Editorial extensions

If this is right

An underwater vehicle running this framework could operate longer on battery power because perception and pose estimation would run on event-driven spiking networks instead of dense deep-learning models.
The conditional haptic control lets a human operator stay in the loop only when the target drifts outside the allowed visual range, reducing operator fatigue during turbid-water inspections.
The spike-based regression design points a way for spiking networks to move beyond classification and handle continuous-output tasks such as pose estimation and control.
If realized, the framework would make underwater infrastructure inspection, environmental monitoring, and exploration more energy-efficient and more robust to turbid conditions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A practical first test of the framework is to convert existing underwater dehazing and detection networks into spiking form through ANN-to-SNN conversion, since the paper itself notes the lack of spiking dehazing and pose-regression literature.
The lateral-error haptic law with the experimentally tuned gains $A$ and $B$ is generic enough to be reused in other shared teleoperation settings where an operator must keep a target in view.
The viability of membrane-potential regression is the hinge of the whole proposal: if it fails to match conventional pose accuracy on benchmark underwater datasets, the pose module would need a fundamentally different spiking architecture.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

You should know this is not a results paper. It is a conceptual framework for combining spiking neural network modules (dehazing, detection, pose estimation, LSTM) with PBVS and haptic feedback for underwater vehicles. The integration is genuinely new—I have not seen this specific pipeline proposed elsewhere. The paper is also honest: it explicitly admits there is no spiking multi-modal pose estimation literature, that membrane-potential regression is untested, and that training data for underwater SNNs are scarce. The standard PBVS equations are correctly stated and cited.

The soft spots are real. The most concrete one is Eq. (7). The final control law adds τ_final, a scalar haptic torque (N·m from Eq. (2)), directly to v_pbvs, a 6-DOF velocity twist (m/s and rad/s from Eq. (6)). That is dimensionally inconsistent and not implementable as written. Haptic torque is generated at the operator's joystick; it should guide the human, not be added to the vehicle command. This is load-bearing because “haptic-guided conditional control” is a headline feature. It might be fixable by defining a mapping from haptic input to a velocity or force command in the vehicle frame, but as written it is wrong.

Second, the pose estimator that supplies cpa to Eq. (5) is only “theoretically formulated.” No spiking pose estimator exists, and the membrane-potential regression approach has no accuracy evidence. That makes the whole chain from perception to control unverified. The paper's central claim—comparable performance with significantly reduced power—has no measurements, simulations, or even benchmarked surrogate results. The discussion and conclusion repeat the promise without adding evidence.

I do not see circularity or fabricated entities. The free parameters (haptic gains A, B; PD gains; threshold) are simply unspecified, typical for a conceptual paper. The literature citations look reasonable.

Bottom line: this paper is a well-written roadmap, not a demonstration. A serious referee could help the authors fix the dimensional error and temper the claims. I would send it to review, but I would expect major revision. For your own work, it is not something you would cite for results, but it might be worth a reading group if you are interested in neuromorphic underwater robotics.

Referee Report

4 major / 4 minor

Summary. The manuscript presents a conceptual framework for integrating neuromorphic components—spiking dehazing, spiking target detection, a hybrid spiking CNN-LSTM pose estimator, and haptic feedback—into a position-based visual servoing (PBVS) controller for underwater vehicles. It provides equations for a lateral haptic torque, a PBVS control law, and a conditional final control law, and it reviews relevant SNN techniques. The paper is explicitly conceptual: it identifies the lack of underwater spiking pose-estimation literature, acknowledges limited training data, and proposes membrane-potential regression as a possible solution. No experimental results or simulations are reported.

Significance. The framework addresses a real gap: conventional deep learning pipelines for underwater perception are computationally heavy, and neuromorphic methods have seen little use in marine robotics. The paper's main strength is a clear assembly of existing SNN building blocks into an end-to-end control pipeline, together with an honest statement of the missing ingredients. However, the central efficiency claim is not demonstrated by any measurement or simulation, and the proposed haptic-shared control law is not well-posed as written. If revised to fix the control-law issues and to include at least a proof-of-concept validation of the spiking pose estimator, the paper could be a useful blueprint for future neuromorphic underwater systems.

major comments (4)

[Section II.C, Eq. (7)] The final control law v_final = v_pbvs + tau_final adds the scalar haptic torque tau_final (N·m, from Eq. (2)) to the 6-DOF velocity twist v_pbvs (m/s, rad/s, from Eq. (6)). This is dimensionally inconsistent, and the addition of quantities with different units and different vector dimensions is not implementable. More importantly, tau_final is defined as the torque generated by a DC motor on the operator's joystick, so it should affect the human operator, not be injected directly into the vehicle's velocity command. Please replace this with an operator-in-the-loop shared-control formulation, for example by mapping the haptic torque through an admittance model into a velocity correction, or by defining tau_final as a wrench applied to the vehicle.
[Section II.B, Eqs. (1), (4), (7)] The threshold condition el(t) >= eth(t) uses the signed lateral error el(t) = xc(t) - xl(t). If the target is on the opposite side of the image center from the one where el(t) is positive, the condition is false even when |el(t)| exceeds the allowable visual range, so no haptic feedback or conditional control would be activated for that side. The condition should be based on |el(t)| (or el(t)^2) if the haptic guidance is intended to work in both lateral directions.
[Section III.E, Vehicle Pose Estimation] The pose estimate cpa used in Eq. (5) comes from a "theoretically formulated" hybrid spike-based CNN-LSTM framework, with no training regime, hyperparameters, dataset, or accuracy evaluation. The paper itself notes the lack of spiking multi-modal pose-estimation literature and the absence of suitable underwater training data. Because the entire PBVS loop depends on this module, the feasibility of the framework cannot be assessed as presented. Please add at least a small-scale proof-of-concept experiment or simulation, or provide an explicit quantitative analysis that does not rely on the pose estimator's accuracy.
[Abstract and Section IV] The abstract claims that the framework promises "significantly reduced computational requirements and power consumption," and the Discussion says there is a "prospect of reduced power consumption while maintaining comparable performance." No measurements, energy models, or spike-count/FLOP comparisons are provided for the proposed modules. Cited energy advantages of SNNs in other domains do not automatically transfer to an underwater perception and control pipeline. Please add a complexity or power estimate for the full pipeline, or explicitly reframe the claim as a hypothesis to be tested in future work.

minor comments (4)

[Section III.E.2, Eq. (9)] The text says "where J represents the Hadamard product," but the equations use a symbol that renders as K in the manuscript; J is not actually defined in the equations. Use a standard symbol such as ⊙ and define it explicitly.
[Section III.C] The sentence "Spiking-YOLO architecture, trained for underwater scenarios, facilitates target detection in the spiking domain [36]" cites reference [36], which is a general object-detection paper and is not specific to underwater scenarios; please correct the citation or rephrase the claim.
[Section II.C, Eq. (5)] The target pose st is set to (cpt, 0), which assumes the target orientation is identity. This should be stated explicitly as an assumption, since the rotational component θu then encodes the absolute vehicle orientation rather than a relative error with respect to the target.
[Section II.C] The text states that the forward velocity is held constant, but vpbvs is a general 6-DOF velocity command that includes translational components; please clarify how a constant forward velocity is compatible with the PBVS velocity command.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is a conceptual framework with no fitted predictions or self-citation chain reducing the central claim to its inputs.

full rationale

This is a conceptual framework paper, not an empirical derivation, and I find no circular step that reduces a claimed result to its own inputs. The control law in Eqs. (5)-(7) is the standard Position Based Visual Servoing formulation cited to external prior work (Chaumette et al. [12]); the haptic force in Eqs. (1)-(4) is defined from image-centroid error and joystick displacement, with gains A, B and PD gains kp, kv left as experimental or design parameters rather than fitted to a dataset and then renamed as predictions. The neuromorphic perception modules are proposed by analogy to external, non-self-authored results: Spiking-YOLO [37], G2L-Net [38], spiking denoising [31,32], and NAS-based dehazing [33-35]. The one load-bearing component that is not externally supported, the hybrid spike-based CNN-LSTM pose estimator, is explicitly admitted in Section III.E to be only theoretically formulated because no spiking multi-modal pose estimation literature exists, and the Discussion openly lists the open limitations: effectiveness of membrane-potential regression, selection of neuron models, lack of spiking regression frameworks, and absence of underwater SNN pose-estimation training data. These honest limitations confirm that the paper is not presenting an internally validated prediction derived from its own equations. The claimed benefits of reduced computational demand and power consumption are literature-based expectations, not outputs of the paper's own derivation. The dimensional inconsistency in Eq. (7), where a scalar haptic torque is added to a 6-DOF velocity twist, is a correctness or implementability issue rather than a circularity issue, since the control law is not derived from the quantity it is said to predict. No self-citations are used as load-bearing evidence, and no equation reduces to another equation by construction. The appropriate finding is therefore no significant circularity.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new physical entities or forces. Its free parameters are standard control gains that are left unspecified. The main assumptions are domain assumptions about the transferability and accuracy of spiking neural networks for underwater tasks, which are neither derived nor empirically supported.

free parameters (4)

A and B (haptic torque gains) = Not specified (experimental)
Eq. (2) defines the joystick torque as -A(theta(t) - theta_d(t)) - B*theta_dot(t), and the text says the gains A and B are 'determined through experimental methods.' Their values directly affect the haptic feedback behavior.
a, kp, kv (PD controller gains in theta_d) = Not specified
Eq. (3) computes the desired joystick displacement using -1/a * (kp*e_l + kv*e_dot_l). The parameters a, kp, kv are not defined or assigned values anywhere in the paper.
e_th (allowable visual range threshold) = Not specified
Eq. (4) activates haptic feedback only when e_l(t) >= e_th(t). The threshold e_th is introduced without a definition or suggested value.
lambda (PBVS gain) = Not specified
Eq. (6) uses the standard exponential decrease control law v = -lambda * L_hat^+ * e_p. lambda is a control gain that must be chosen by the designer, but no value or tuning method is given.

assumptions (3)

standard math Standard position-based visual servoing equations from Chaumette et al. (reference [12]) are valid for the underwater vehicle.
The control law in Eq. (5) and Eq. (6) relies on the classical PBVS framework. The paper does not derive these equations and assumes their correctness.
domain assumption Spiking neural networks can achieve accuracy comparable to conventional deep networks for perception and pose estimation tasks.
The entire motivation rests on this premise, supported only by cited literature from other domains. No underwater experiments or benchmarks are provided.
domain assumption Directly trained spiking networks or ANN-to-SNN conversion methods can be applied to underwater dehazing, detection, and regression without significant performance loss.
Section III discusses existing spiking architectures but does not show that they transfer to the underwater setting, which has unique turbidity and data scarcity.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Hybrid-Neuromorphic Approach for Underwater Robotics Applications: A Conceptual Framework." pith.science (2026). https://pith.science/paper/AI447VYK

@misc{pith2026241113962,
  author       = {Pith},
  title        = {Pith review of: Hybrid-Neuromorphic Approach for Underwater Robotics Applications: A Conceptual Framework},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AI447VYK}},
  note         = {Machine review of arXiv:2411.13962}
}

read the original abstract

This paper introduces the concept of employing neuromorphic methodologies for task-oriented underwater robotics applications. In contrast to the increasing computational demands of conventional deep learning algorithms, neuromorphic technology, leveraging spiking neural network architectures, promises sophisticated artificial intelligence with significantly reduced computational requirements and power consumption, emulating human brain operational principles. Despite documented neuromorphic technology applications in various robotic domains, its utilization in marine robotics remains largely unexplored. Thus, this article proposes a unified framework for integrating neuromorphic technologies for perception, pose estimation, and haptic-guided conditional control of underwater vehicles, customized to specific user-defined objectives. This conceptual framework stands to revolutionize underwater robotics, enhancing efficiency and autonomy while reducing energy consumption. By enabling greater adaptability and robustness, this advancement could facilitate applications such as underwater exploration, environmental monitoring, and infrastructure maintenance, thereby contributing to significant progress in marine science and technology.

Figures

Figures reproduced from arXiv: 2411.13962 by the authors.

**Figure 2.** Schematic representation of the task-driven control framework for underwater vehicles. The highlighted blocks in ’red’ denote the incorporation [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Schematic representation of spike-based CNN-LSTM hybrid framework [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

40 extracted references · 34 canonical work pages

[1]

Ultra low power bioelectronics: Fundamentals, biomedical applications, and bio-inspired system,

R. Sharpeshkar, “Ultra low power bioelectronics: Fundamentals, biomedical applications, and bio-inspired system,” 2010

work page 2010
[2]

Monitoring marine environments with autonomous underwater vehicles: a bibliometric analysis,

F. Di Ciaccio and S. Troisi, “Monitoring marine environments with autonomous underwater vehicles: a bibliometric analysis,” Results in Engineering, vol. 9, p. 100205, 2021

work page 2021
[3]

Ex- ploring the

B. T. Phillips, M. Dunbabin, B. Henning, C. Howell, A. DeCiccio, A. Flinders, K. A. Kelley, J. J. Scott, S. Albert, S. Carey, et al., “Ex- ploring the” sharkcano” biogeochemical observations of the kavachi submarine volcano (solomon islands),” Oceanography, vol. 29, no. 4, pp. 160–169, 2016

work page 2016
[4]

Treatment of offshore oily produced water: Research and application of a novel fibrous coalescence technique,

H. Lu, Y .-q. Liu, J.-b. Cai, X. Xu, L.-s. Xie, Q. Yang, Y .-x. Li, and K. Zhu, “Treatment of offshore oily produced water: Research and application of a novel fibrous coalescence technique,” Journal of Petroleum Science and Engineering , vol. 178, pp. 602–608, 2019

work page 2019
[5]

Deep learning for un- derwater visual odometry estimation,

B. Teixeira, H. Silva, A. Matos, and E. Silva, “Deep learning for un- derwater visual odometry estimation,” Ieee Access, vol. 8, pp. 44687– 44701, 2020

work page 2020
[6]

Real-time underwater onboard vision sensing system for robotic gripping,

Y . Wang, C. Tang, M. Cai, J. Yin, S. Wang, L. Cheng, R. Wang, and M. Tan, “Real-time underwater onboard vision sensing system for robotic gripping,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–11, 2020

work page 2020
[7]

Underwater acoustic research trends with machine learning: general background,

H. Yang, K. Lee, Y . Choo, and K. Kim, “Underwater acoustic research trends with machine learning: general background,” Journal of Ocean Engineering and Technology, vol. 34, no. 2, pp. 147–154, 2020

work page 2020
[8]

Underwater hyperspectral imaging technology and its applications for detecting and mapping the seafloor: A review,

B. Liu, Z. Liu, S. Men, Y . Li, Z. Ding, J. He, and Z. Zhao, “Underwater hyperspectral imaging technology and its applications for detecting and mapping the seafloor: A review,” Sensors, vol. 20, no. 17, p. 4962, 2020

work page 2020

Show all 40 references

[9]

A survey of model driven engineering in robotics,

E. de Ara ´ujo Silva, E. Valentin, J. R. H. Carvalho, and R. da Silva Bar- reto, “A survey of model driven engineering in robotics,” Journal of Computer Languages, vol. 62, p. 101021, 2021

2021
[10]

Assessing feasibility of secure quantum communications involving underwater assets,

M. Lanzagorta and J. Uhlmann, “Assessing feasibility of secure quantum communications involving underwater assets,” IEEE Journal of Oceanic Engineering , vol. 45, no. 3, pp. 1138–1147, 2019

2019
[11]

Design and applications of mems flow sensors: A review,

F. Ejeian, S. Azadi, A. Razmjou, Y . Orooji, A. Kottapalli, M. E. Warkiani, and M. Asadnia, “Design and applications of mems flow sensors: A review,” Sensors and Actuators A: Physical , vol. 295, pp. 483–502, 2019

2019
[12]

Visual servoing,

F. Chaumette, S. Hutchinson, and P. Corke, “Visual servoing,” in Springer Handbook of Robotics , pp. 841–866, Springer, 2016

2016
[13]

Image processing with spiking neuron networks,

B. Meftah, O. L ´ezoray, S. Chaturvedi, A. A. Khurshid, and A. Benyet- tou, “Image processing with spiking neuron networks,” Artificial Intel- ligence, Evolutionary Computing and Metaheuristics: In the Footsteps of Alan Turing , pp. 525–544, 2013

2013
[14]

Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications,

S. Koravuna, U. R ¨uckert, T. Jungeblut, et al. , “Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications,” Frontiers in Computational Neuroscience , vol. 17, 2023

2023
[15]

Koch and I

C. Koch and I. Segev, Methods in neuronal modeling: from ions to networks. MIT press, 1998

1998
[16]

Gerstner, W

W. Gerstner, W. M. Kistler, R. Naud, and L. Paninski, Neuronal dynamics: From single neurons to networks and models of cognition . Cambridge University Press, 2014

2014
[17]

Simple model of spiking neurons,

E. M. Izhikevich, “Simple model of spiking neurons,” IEEE Transac- tions on neural networks , vol. 14, no. 6, pp. 1569–1572, 2003

2003
[18]

Neural coding in spiking neural networks: A comparative study for robust neuromorphic systems,

W. Guo, M. E. Fouda, A. M. Eltawil, and K. N. Salama, “Neural coding in spiking neural networks: A comparative study for robust neuromorphic systems,” Frontiers in Neuroscience, vol. 15, p. 638474, 2021

2021
[19]

On the relevance of time in neural computation and learning,

W. Maass, “On the relevance of time in neural computation and learning,” Theoretical Computer Science , vol. 261, no. 1, pp. 157– 178, 2001

2001
[20]

First spikes in ensembles of human tactile afferents code complex spatial fingertip events,

R. S. Johansson and I. Birznieks, “First spikes in ensembles of human tactile afferents code complex spatial fingertip events,” Nature neuroscience, vol. 7, no. 2, pp. 170–177, 2004

2004
[21]

Training spiking neural networks using lessons from deep learning,

J. K. Eshraghian, M. Ward, E. O. Neftci, X. Wang, G. Lenz, G. Dwivedi, M. Bennamoun, D. S. Jeong, and W. D. Lu, “Training spiking neural networks using lessons from deep learning,” Proceed- ings of the IEEE , 2023

2023
[22]

Advancements in algorithms and neuromorphic hardware for spiking neural networks,

A. Javanshir, T. T. Nguyen, M. P. Mahmud, and A. Z. Kouzani, “Advancements in algorithms and neuromorphic hardware for spiking neural networks,” Neural Computation, vol. 34, no. 6, pp. 1289–1328, 2022

2022
[23]

Spike timing–dependent plasticity: a hebbian learning rule,

N. Caporale and Y . Dan, “Spike timing–dependent plasticity: a hebbian learning rule,” Annu. Rev. Neurosci., vol. 31, pp. 25–46, 2008

2008
[24]

Backpropagation-based learning techniques for deep spiking neural networks: A survey,

M. Dampfhoffer, T. Mesquida, A. Valentian, and L. Anghel, “Backpropagation-based learning techniques for deep spiking neural networks: A survey,” IEEE Transactions on Neural Networks and Learning Systems, 2023

2023
[25]

Error-backpropagation in temporally encoded networks of spiking neurons,

S. M. Bohte, J. N. Kok, and H. La Poutre, “Error-backpropagation in temporally encoded networks of spiking neurons,” Neurocomputing, vol. 48, no. 1-4, pp. 17–37, 2002

2002
[26]

The tempotron: a neuron that learns spike timing–based decisions,

R. G ¨utig and H. Sompolinsky, “The tempotron: a neuron that learns spike timing–based decisions,” Nature neuroscience , vol. 9, no. 3, pp. 420–428, 2006

2006
[27]

Mapping from frame-driven to frame-free event-driven vision systems by low-rate rate coding and coincidence processing–application to feedforward convnets,

J. A. P ´erez-Carrasco, B. Zhao, C. Serrano, B. Acha, T. Serrano- Gotarredona, S. Chen, and B. Linares-Barranco, “Mapping from frame-driven to frame-free event-driven vision systems by low-rate rate coding and coincidence processing–application to feedforward convnets,” IEEE t...

2013
[28]

Spiking deep convolutional neural networks for energy-efficient object recognition,

Y . Cao, Y . Chen, and D. Khosla, “Spiking deep convolutional neural networks for energy-efficient object recognition,” International Jour- nal of Computer Vision , vol. 113, pp. 54–66, 2015

2015
[29]

Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing,

P. U. Diehl, D. Neil, J. Binas, M. Cook, S.-C. Liu, and M. Pfeiffer, “Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing,” in 2015 International joint conference on neural networks (IJCNN) , pp. 1–8, ieee, 2015

2015
[30]

Batch normalization: Accelerating deep network training by reducing internal covariate shift,

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International conference on machine learning , pp. 448–456, pmlr, 2015

2015
[31]

Spiden: deep spiking neural networks for efficient image denoising,

A. Castagnetti, A. Pegatoquet, and B. Miramond, “Spiden: deep spiking neural networks for efficient image denoising,” Frontiers in Neuroscience, vol. 17, p. 1224457, 2023

2023
[32]

Deep spiking-unet for image processing,

H. Li, Y . Zhang, Z. Xiong, Z.-j. Zha, and X. Sun, “Deep spiking-unet for image processing,” arXiv preprint arXiv:2307.10974 , 2023

2023 arXiv
[33]

Neural architecture search for image dehazing,

M. Mandal, Y . R. Meedimale, M. S. K. Reddy, and S. K. Vipparthi, “Neural architecture search for image dehazing,” IEEE Transactions on Artificial Intelligence , 2022

2022
[34]

Neural archi- tecture search for spiking neural networks,

Y . Kim, Y . Li, H. Park, Y . Venkatesha, and P. Panda, “Neural archi- tecture search for spiking neural networks,” in European Conference on Computer Vision , pp. 36–56, Springer, 2022

2022
[35]

Autosnn: Towards energy-efficient spiking neural networks,

B. Na, J. Mok, S. Park, D. Lee, H. Choe, and S. Yoon, “Autosnn: Towards energy-efficient spiking neural networks,” in International Conference on Machine Learning , pp. 16253–16269, PMLR, 2022

2022
[36]

Deep directly-trained spiking neural networks for object detection,

Q. Su, Y . Chou, Y . Hu, J. Li, S. Mei, Z. Zhang, and G. Li, “Deep directly-trained spiking neural networks for object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6555–6565, 2023

2023
[37]

Spiking-yolo: spiking neural network for energy-efficient object detection,

S. Kim, S. Park, B. Na, and S. Yoon, “Spiking-yolo: spiking neural network for energy-efficient object detection,” in Proceedings of the AAAI conference on artificial intelligence , vol. 34, pp. 11270–11277, 2020

2020
[38]

G2l-net: Global to local network for real-time 6d pose estimation with embed- ding vector features,

W. Chen, X. Jia, H. J. Chang, J. Duan, and A. Leonardis, “G2l-net: Global to local network for real-time 6d pose estimation with embed- ding vector features,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pp. 4233–4242, 2020

2020
[39]

Hardware aware modeling of mixed- signal spiking neural network,

S. N. Chowdhury and S. Shah, “Hardware aware modeling of mixed- signal spiking neural network,” in 2022 20th IEEE Interregional NEWCAS Conference (NEWCAS) , pp. 104–108, IEEE, 2022

2022
[40]

Long short-term memory spiking networks and their applications,

A. Lotfi Rezaabad and S. Vishwanath, “Long short-term memory spiking networks and their applications,” in International Conference on Neuromorphic Systems 2020 , pp. 1–9, 2020

2020

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

Ultra low power bioelectronics: Fundamentals, biomedical applications, and bio-inspired system,

R. Sharpeshkar, “Ultra low power bioelectronics: Fundamentals, biomedical applications, and bio-inspired system,” 2010

work page 2010

[2] [2]

Monitoring marine environments with autonomous underwater vehicles: a bibliometric analysis,

F. Di Ciaccio and S. Troisi, “Monitoring marine environments with autonomous underwater vehicles: a bibliometric analysis,” Results in Engineering, vol. 9, p. 100205, 2021

work page 2021

[3] [3]

Ex- ploring the

B. T. Phillips, M. Dunbabin, B. Henning, C. Howell, A. DeCiccio, A. Flinders, K. A. Kelley, J. J. Scott, S. Albert, S. Carey, et al., “Ex- ploring the” sharkcano” biogeochemical observations of the kavachi submarine volcano (solomon islands),” Oceanography, vol. 29, no. 4, pp. 160–169, 2016

work page 2016

[4] [4]

Treatment of offshore oily produced water: Research and application of a novel fibrous coalescence technique,

H. Lu, Y .-q. Liu, J.-b. Cai, X. Xu, L.-s. Xie, Q. Yang, Y .-x. Li, and K. Zhu, “Treatment of offshore oily produced water: Research and application of a novel fibrous coalescence technique,” Journal of Petroleum Science and Engineering , vol. 178, pp. 602–608, 2019

work page 2019

[5] [5]

Deep learning for un- derwater visual odometry estimation,

B. Teixeira, H. Silva, A. Matos, and E. Silva, “Deep learning for un- derwater visual odometry estimation,” Ieee Access, vol. 8, pp. 44687– 44701, 2020

work page 2020

[6] [6]

Real-time underwater onboard vision sensing system for robotic gripping,

Y . Wang, C. Tang, M. Cai, J. Yin, S. Wang, L. Cheng, R. Wang, and M. Tan, “Real-time underwater onboard vision sensing system for robotic gripping,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–11, 2020

work page 2020

[7] [7]

Underwater acoustic research trends with machine learning: general background,

H. Yang, K. Lee, Y . Choo, and K. Kim, “Underwater acoustic research trends with machine learning: general background,” Journal of Ocean Engineering and Technology, vol. 34, no. 2, pp. 147–154, 2020

work page 2020

[8] [8]

Underwater hyperspectral imaging technology and its applications for detecting and mapping the seafloor: A review,

B. Liu, Z. Liu, S. Men, Y . Li, Z. Ding, J. He, and Z. Zhao, “Underwater hyperspectral imaging technology and its applications for detecting and mapping the seafloor: A review,” Sensors, vol. 20, no. 17, p. 4962, 2020

work page 2020