arXiv Machine Learning

Common-Mode Errors Limit Low-Timestep Deep Spiking Q-Networks

The paper identifies that low‑timestep deep spiking Q‑networks (DSQNs) suffer mainly from common‑mode errors that degrade Q‑value estimation. To address this, the authors introduce Common‑Mode Compensation DSQN (CMC‑DSQN), which employs an auxiliary ANN during training to correct these errors while keeping inference energy‑efficient. Experiments on Atari and MiniAtar show that CMC‑DSQN improves performance by nearly 20 % at two timesteps and outperforms both DSQN baselines and the ANN baseline at four timesteps.

arXiv Machine Learning

5d ago

Local Timescale Gates for Timescale-Robust Continual Spiking Neural Networks

Local Timescale Gating (LT‑Gate) is a neuron model that tracks fast and slow information in parallel and uses a learned gate to adjust their influence, enabling spiking neurons to preserve long‑term context while responding quickly. The approach includes a variance‑tracking regularization inspired by biological homeostasis and operates with local updates, making it compatible with neuromorphic hardware such as Intel’s Loihi chip. Empirical results show LT‑Gate improves accuracy and retention on sequential learning tasks, achieving about 51 % final accuracy versus 46 % for a recent Hebbian baseline and outperforming prior SNN methods.

By Devansh Deep, Ansh Tiwari, Ayush Chauhan

benchmarks

arXiv AI

Jul 29

Representation Capacity-Matched QNN-SNN Twin Construction for Rate-Encoded SNNs

arXiv:2409. 08290v5 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) promise higher energy efficiency over conventional Quantized Artificial Neural Networks (QNNs) due to their event-driven, spike-based computation.

By Zhanglu Yan, Zhenyu Bai, Kaiwen Tang, Weng-Fai Wong

efficiency

arXiv Computation and Language

6d ago

SpikingVLA: Asynchronous Spiking Vision-Language-Action Models

arXiv:2610.09710v1 Announce Type: new Abstract: ANN-to-SNN conversion offers a practical route toward energy-efficient spiking Vision-Language-Action (VLA) models by bypassing the substantial cost of...

By Jingya Wang, Dehao Zhang, Shuai Wang, Malu Zhang, Yang Yang, Haizhou Li

efficiency multimodal

arXiv Machine Learning

Jul 24

Error Amplification Limits ANN-to-SNN Conversion in Continuous Control

arXiv:2601. 21778v3 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) can achieve competitive performance by converting already existing well-trained Artificial Neural Networks (ANNs), avoiding further costly training.

By Zijie Xu, Zihan Huang, Yiting Dong, Kang Chen, Wenxuan Liu, Zhaofei Yu

reinforcement-learning benchmarks

arXiv Machine Learning

Aug 10

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons

arXiv:2506. 03392v2 Announce Type: replace Abstract: We propose a new ternary spiking neuron model to improve the representation capacity of binary spiking neurons in deep Q-learning.

By Aref Ghoreishee, Abhishek Mishra, John Walsh, Anup Das, Nagarajan Kandasamy

agents reinforcement-learning safety

arXiv AI

Jul 15

Enabling Energy-Efficient Simultaneous Multi-Task Reinforcement Learning through Spiking Neural Networks with Active Dendrites for Bio-inspired Generalist Agents

arXiv:2412. 04847v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has demonstrated remarkable capabilities in training agents to solve complex tasks autonomously, such as mobile robots, UAVs/UGVs, and game-playing agents).

By Rachmad Vidya Wicaksana Putra, Avaneesh Devkota, Muhammad Shafique

agents reinforcement-learning robotics benchmarks

arXiv Machine Learning

Aug 5

Revisiting TD Target Aggregation under Uncertainty in Q-Learning

arXiv:2608. 03069v1 Announce Type: new Abstract: Deep Q-Networks (DQNs) learn value functions through bootstrapped temporal-difference updates, where future returns are approximated using a greedy maximization over next-state action values.

By Lipeng Zu, Xiaonan Zhang

reinforcement-learning benchmarks

arXiv AI

Oct 2

SpikeMoE: Brain-Inspired Competitive Routing for Flexible Spiking Mixture-of-Experts

SpikeMoE introduces a spike-based k‑WTA router that uses lateral inhibition and refractory periods to select the top‑K experts based on discrete spike counts, inspired by hippocampal CA1 competition. The framework combines spiking neural network dynamics with mixture‑of‑experts conditional computation and adds a two‑stage missing‑modality module for robust multimodal processing. Experiments on vision, language, and multimodal tasks show that SpikeMoE matches or surpasses ANN baselines while offering energy‑efficient performance.

By Xiaoli Liu, Yujie Liang, Jialin Li, Malu Zhang

rag multimodal benchmarks

arXiv AI

Jun 12

Otters++: A Time-to-first-spike Based Energy Efficient Optical Spiking Transformer

arXiv:2606. 13016v1 Announce Type: new Abstract: Spiking neural networks (SNNs) are promising for energy-efficient inference, and time-to-first-spike (TTFS) coding is especially attractive because each neuron fires at most once.

By Zhanglu Yan, Jiayi Mao, Kaiwen Tang, Fanfan Li, Gang Pan, Tao Luo, Bowen Zhu, Qianhui Liu, Weng-Fai Wong

llms efficiency

arXiv AI

Aug 18

Understanding and Stabilizing Deep Q-Learning via Controlled Bootstrapping and Regulated Value Dynamics

arXiv:2608. 16182v1 Announce Type: cross Abstract: Deep Q-learning (DQL) has achieved remarkable empirical success in reinforcement learning, yet its training process remains notoriously unstable.

By Bozhou Chen, Yongyi Wang, Hanyu Liu, Xionghui Yang, Wenxin Li

reinforcement-learning safety

Hugging Face Trending Papers

Jul 29

The Sparsity Ceiling: Where Spiking Networks Can and Cannot Trade Activity for Energy

Spiking neural networks (SNNs) are promoted as an energy-efficient substrate because sparse, event-driven activity replaces dense multiply-accumulates with cheap accumulates. We argue the energy dividend of sparsity is not a property of SNNs but of the task.

llms efficiency

arXiv Machine Learning

Sep 10

A Gradient-based yet Spike-Timing-Dependent Solution to the Feedback Learning Problem in Neural Microcircuits

arXiv:2609.08070v1 Announce Type: cross Abstract: The brain uses discrete spikes for dynamic computation, yet, how neural microcircuits (NMCs) solve temporal credit assignment using local spike timin...

By Xiangnan Zhang, Jingxin Liu, Ranqi Lu, Jingyu Liu, Qunxi Dong, Fuze Tian, Lixian Zhu, Bin Hu, Bj\"orn W. Schuller

benchmarks

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