Spiking Neural Networks

Also known as SNN

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30 papers in the last four weeks, up 650% on the four weeks before. 0.3% of all new papers.

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SpikeProphecy: A Large-Scale Benchmark for Autoregressive Neural Population Forecasting
May 13, 2026John R. Minnick, Jinghui Geng, Kamran Hussain +6Neural Processes Time Series Forecasting
Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA, USA · UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA · Department of Computer Science and Engineering, University of California, Santa Cruz, CA, USA +2
Heterogeneous SoC Integrating an Open-Source Recurrent SNN Accelerator for Neuromorphic Edge Computing on FPGA
May 12, 2026Michelangelo Barocci, Vittorio Fra, Enrico Macii +1Edge Computing Heterogeneous Computing
Department of Computer and Control Engineering, Politecnico di Torino, Turin, Italy · Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, Turin, Italy
STARS: Spike Tail-Aware Relational Synthesis for ANN-to-SNN Data-Free Knowledge Distillation
May 12, 2026Shuhan Ye, Yi Yu, Qixin Zhang +5Synthetic Data Generation Spiking Neural Networks
Nanyang Technological University · Jilin University · Wuhan University +1
Multi-Timescale Conductance Spiking Networks: A Sparse, Gradient-Trainable Framework with Rich Firing Dynamics for Enhanced Temporal Processing
May 12, 2026Alex Fulleda-Garcia, Saray Soldado-Magraner, Josep Maria Margarit-TauléSpiking Neural Networks Neuromorphic Computing
Instituto de Microelectrónica de Barcelona (IMB-CNM), Consejo Superior de Investigaciones Cientíﬁcas (CSIC) Cerdanyola del Vallès, Spain · Department of Neurobiology, University of California Los Angeles (UCLA) Los Angeles, USA · Joint Senior authors
Breaking Global Self-Attention Bottlenecks in Transformer-based Spiking Neural Networks with Local Structure-Aware Self-Attention
May 12, 2026Lingdong Li, Hangming Zhang, Qiang YuSpiking Transformers Efficient ViTs
School of Future Technology, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin 300354, China · School of Artificial Intelligence, Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin 300354, China
Leveraging Non-Equilibrium ECRAM Dynamics for Short-Term Plasticity in Neuromorphic Circuits
May 11, 2026Alex Currie, Sean Borkholder, Nithil Harris Manimaran +4Spiking Neural Networks Neuromorphic Computing
RAMLab, Department of Electrical and Microelectronic Engineering, Rochester Institute of Technology, Rochester NY, USA · Iontronic and Nanoelectronics Lab, School of Physics and Astronomy, Rochester Institute of Technology, Rochester NY, USA · Brain Lab, Department of Computer Engineering, Rochester Institute of Technology, Rochester NY, USA
Energy-Efficient Implementation of Spiking Recurrent Cells on FPGA
May 11, 2026Pascal Harmeling, Florent De Geeter, Guillaume DrionEfficient Neural Network Inference Spiking Neural Networks
Frequency Matching in Spiking Neural Networks for mmWave Sensing
May 11, 2026Di Yu, Zhenyu Liao, Changze Lv +7Energy-Efficient ML Frequency-Domain Feature Learning
College of Computer Science and Technology, Zhejiang University, Hangzhou, China · School of Computer Science, Fudan University, Shanghai, China · Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China +1
Encoding and Decoding Temporal Signals with Spiking Bandpass Wavelets
May 10, 2026Jens Egholm Pedersen, Tony Lindeberg, Peter GerstoftWavelet Methods Spiking Neural Networks
Department of Electrical and Photonics Engineering Technical University of Denmark Denmark · Department of Computational Science and Technology KTH Royal Institute of Technology Sweden
Uncertainty-Aware Token Importance Estimation in Spiking Transformers
May 10, 2026Wenxuan Liu, Zecheng Hao, Tong Bu +2Spiking Transformers Uncertainty Quantification
School of Computer Science, Peking University · School of Computer Science, Peking University. · Institute for Artificial Intelligence, Peking University +2
UIESNN: A Scale-Aware Spiking Network for Underwater Image Enhancement
May 8, 2026Shuang Chen, Ruochen Li, Zihan Zhu +3Underwater Image Enhancement Spiking Neural Networks
Computer Science Department, Durham University, UK · University of Cambridge · Institute of Biology, University of Graz, Graz, Austria.
Globally Optimal Training of Spiking Neural Networks via Parameter Reconstruction
May 8, 2026Himanshu Udupi, Xiaocong Yang, ChengXiang ZhaiNeural Network Optimization Recurrent Neural Networks
University of Illinois Urbana-Champaign
Direct-to-Event Spiking Neural Network Transfer
May 8, 2026Nhan Trong Luu, Duong Trung Luu, Pham Ngoc Nam +1Efficient Neural Network Inference Spiking Neural Networks
College of ICT, Can Tho University, Vietnam · CECS, VinUniversity, Hanoi, Vietnam · Center of Digital Transformation and Communication, Can Tho University, Vietnam +1
SAFformer:Improving Spiking Transformer via Active Predictive Filtering
May 8, 2026Zequan Xie, Weiming Zeng, Yunhua Chen +3Spiking Transformers Efficient ViTs
School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China · Faculty of Science, Hong Kong Baptist University, China · School of Electronic Information, Wuhan University, China
Benchmarking Fairness in Spiking Neural Networks: Data Bias, Spurious Features, and Hardware Effects
May 8, 2026Hudi He, Fukun Wang, Zhe Wang +7Benchmark Design Algorithmic Fairness
Jilin University · Nanyang Technological University · RMIT University
Neuromorphic visual attention for Sign-language recognition on SpiNNaker
May 7, 2026Sarka Liskova, Olha Vedmedenko, Mazdak Fatahi +3Event-Based Vision Visual Attention
Dept. of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic · Faculty of Information Technology, Czech Technical University in Prague, Czech Republic
Detecting AI-Generated Videos with Spiking Neural Networks
May 7, 2026Minsuk Jang, Yujin Yang, Heeseon Kim +3Deepfake Detection Spiking Neural Networks
Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
Bio-plausible Neuromorphic Disturbance Observer Based on Emulation Theory: Extended Version
May 5, 2026Hongfu Xu, Xiaoyu Guo, Shengbo Wang +1Robust Control Spiking Neural Networks
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China · Department of Mechanical Engineering, City University of Hong Kong, Hong Kong · Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong +1
Elastic Spiking Transformers for Efficient Gesture Understanding
May 4, 2026Alberto Ancilotto, Gianluca Amprimo, Stefano Di Carlo +1Efficient Neural Network Inference Spiking Transformers
Fondazione Bruno Kessler · Politecnico di Torino
SNNF: An SNN-based Near-Sensor Noise Filter for Dynamic Vision Sensors
May 3, 2026Yahan Yang, Pradeep Kumar Gopalakrishnan, Chang Chip Hong +1Event-Based Vision Efficient Neural Network Inference
ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization
May 3, 2026Kaiwen Tang, Di Yu, Jiaqi Zheng +4Spiking Neural Networks Neural Network Quantization
Zhejiang University · Shandong University
From Cortical Synchronous Rhythm to Brain Inspired Learning Mechanism: An Oscillatory Spiking Neural Network with Time-Delayed Coordination
May 3, 2026Tingting Dan, Guorong WuOscillatory Neural Networks Spiking Neural Networks
Advanced Computational Medicine Laboratory (ACMLab) · Department of Psychiatry & Department of Computer Science
Congestion-Aware Dynamic Axonal Delay for Spiking Neural Networks
May 2, 2026Dewei Bai, Hongxiang Peng, Yunyun Zeng +2Dynamic Neural Networks Spiking Neural Networks
University of Electronic Science and Technology of China
Spiking Sequence Machines and Transformers
May 1, 2026Joy BoseTransformer Attention Spiking Neural Networks
Independent Researcher Bengaluru, India
Scalable Learning in Structured Recurrent Spiking Neural Networks without Backpropagation
May 1, 2026Bo Tang, Weiwei XieRecurrent Neural Networks Spiking Neural Networks
Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester, USA
Real-Time Frame- and Event-based Object Detection with Spiking Neural Networks on Edge Neuromorphic Hardware: Design, Deployment and Benchmark
Apr 30, 2026Udayanga G. W. K. N. Gamage, Yan Zeng, Cesar Cadena +2Edge Inference Spiking Neural Networks
Department of Electrical and Photonics Engineering, Technical University of Denmark, Anker Engelunds Vej 1, 2800 Kongens Lyngby, Denmark · Faculty of Engineering, University of Ruhuna, Hapugala,Galle 80000, Sri Lanka · Autonomous Systems Lab, ETH zürich, Leonhardstrasse 21, 8092 Zürich, Switzerland
NeuroRing: Scaling Spiking Neural Networks via Multi-FPGA Bidirectional Ring Topologies and Stream-Dataflow Architectures
Apr 30, 2026Muhammad Ihsan Al Hafiz, Artur PodobasSpiking Neural Networks Neuromorphic Computing
KTH Royal Institute of Technology, Stockholm, Sweden
Evolutionary feature selection for spiking neural network pattern classifiers
Apr 29, 2026Michal Valko, Nuno C. Marques, Marco CastelaniSpiking Neural Networks Feature Selection
Department of Applied Informatics, Comenius University, Mlynská Dolina, 842 48 Bratislava, Slovakia · CENTRIA,Departmento de Informatica, Faculdade de Ciencias e Tecnologia, University Nova de Lisboa, Quinta da Torre, 2829-516 Caparica, Portugal · CENTRIA, Faculdade de Ciencias e Tecnologia, University Nova de Lisboa, Quinta da Torre, 2829-516 Caparica, Portugal
Binary Spiking Neural Networks as Causal Models
Apr 29, 2026Aditya Kar, Emiliano Lorini, Timothée MasquelierStructural Causal Models Binary Neural Networks
CNRS, IRIT · Institut de Recherche en Informatique de Toulouse (IRIT), Centre de Recherche Cerveau et Cognition (CerCo), CNRS, France · Institut de Recherche en Informatique de Toulouse (IRIT), CNRS, France +2
EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures
Apr 29, 2026Gustav Olaf Yunus Laitinen-Fredriksson Lundstrom-Imanov, Taner YilmazEnergy-Efficient ML Edge Inference
Department of Economics, Stockholm University, Universitetsvägen 10 A, SE-106 91 Stockholm, Sweden · Department of Computer Engineering, Afyon Kocatepe University, 03200 Afyonkarahisar, Türkiye

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SpikeProphecy: A Large-Scale Benchmark for Autoregressive Neural Population Forecasting

John R. Minnick, Jinghui Geng, Kamran Hussain, Jesus Gonzalez-Ferrer+5

Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA, USA · UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA · Department of Computer Science and Engineering, University of California, Santa Cruz, CA, USA+2

Neural Processes Time Series Forecasting Forecasting Benchmarks Spiking Neural Networks Spatiotemporal Forecasting

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Neural population models, which predict the joint firing of many simultaneously recorded neurons forward in time, are typically evaluated by a single aggregate Pearson correlation

r

between predicted and actual spike counts, a number that masks critical structure. We argue that how we evaluate spike forecasting matters as much as what we build, and introduce SpikeProphecy, the first large-scale benchmark for causal, autoregressive spike-count forecasting on real electrophysiology recordings. Our core contribution is a population metric decomposition that separates aggregate performance into temporal fidelity, spatial pattern accuracy, and magnitude-invariant alignment. The decomposition surfaces aspects of the underlying data that an aggregate scalar collapses together. We apply the protocol to 105 Neuropixels sessions (Steinmetz 2019 + IBL Repeated Site; ~89,800 neurons) with seven architecture baselines spanning four structural families: four SSMs (three diagonal and one non-diagonal), a Transformer, an LSTM, and a spiking network. The decomposition surfaces a brain-region predictability ranking that reproduces across all seven baselines and survives ANCOVA correction for firing-statistics constraints (region

ΔR^2 = 0.018

above the firing-statistics covariates). It also exposes a sub-Poisson evaluation floor where rigorous metrics combine with genuine biophysical constraints on regular spike trains, and yields a negative result on KL-on-output-rates distillation for ANN-to-SNN transfer in this Poisson count domain.

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