Neural Encoding

Follow this topic Ask about this topic

Momentum

8 papers in the last four weeks, up 167% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 62

All topics

The Value of Spike Timing: A Leakage-Resistant Benchmark of SNN Design Choices for Network Intrusion Detection
May 31, 2026Raj Patel, Shaswata Mitra, David Amebley +3Data Leakage Network Intrusion Detection
Department of Computer Science, The University of Alabama
MIRAGE: Adaptive Multimodal Gating for Whole-Brain fMRI Encoding
May 28, 2026Abdulkadir Gokce, Badr AlKhamissi, Martin SchrimpfNeuroimaging Neural Encoding
NeuroAI Lab, EPFL
Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images
May 27, 2026Joséphine Raugel, Maximilian Seitzer, Marc Szafraniec +6Backpropagation Visual Representation Learning
Meta AI · Ecole Normale Supérieure, PSL University
AffectCodec: Emotion-Preserving Neural Speech Codec with Block-Diagonal Residual FSQ
May 22, 2026Zhaoyang Meng, Zhengyao Ma, Kecan Mao +2Neural Audio Codecs Neural Encoding
Beijing University of Posts and Telecommunications
Sparse Autoencoders Map Brain-LLM Alignment onto Cortical Semantic Topography
May 21, 2026Dongxin Guo, Jikun Wu, Siu Ming YiuNeural Processes LLM Interpretability
The University of Hong Kong · Hong Kong, China · Stellaris AI Limited
Rare Events, Real Signals: Functional Ensembles as Units of Computation in Deep Spiking Networks
May 21, 2026Aditi Aravind, Konstantinos Ladakis, Mario Alexios Savaglio +2Functional Connectivity Neural Network Interpretability
University of Crete, Greece · 2Foundation of Research & Technology - Hellas (FORTH), Greece · 4Harvard Medical School, USA +2
Temporal Coding as a Substrate for Sensorimotor Object Inference: A Spiking Reinterpretation of Thousand Brains Architecture
May 21, 2026Joy BoseNeural Processes Active Sensing
Independent Researcher, Bengaluru, India
Stimulus symmetries can confound representational similarity analyses
May 20, 2026Farhad Pashakhanloo, Jacob A. Zavatone-VethRepresentational Similarity Analysis Representation Learning
Center for Brain Science and 2Society of Fellows Harvard University, Cambridge, MA, USA
Nonlocal operator learning for fMRI encoding and decoding tasks
May 19, 2026Andreas Kramer, Saugat Acharya, Alice Giola +1Neural Decoding Neuroimaging
Department of Computer Science, Idaho State University 921 S. 8th Ave Mail Stop 8060, Pocatello, ID 83209-8023 · Department of Mathematics and Statistics, Idaho State University Physical Science Complex, 921 S. 8th Ave., Stop 8085, Pocatello, ID 83209
Optimising Neural Speech Codecs for 300bps Communication using Reinforcement Learning
May 19, 2026Junyi Wang, Chi Zhang, Jing Qian +4Neural Audio Codecs Neural Encoding
Tsinghua University, China · Huawei Technologies Co., Ltd, China
Brain alignment of reasoning and action representations from vision-language and action models during naturalistic gameplay
May 19, 2026Subba Reddy Oota, Anant Khandelwal, Khushbu Pahwa +4Vision-Language Models Neural Encoding
Independent · Microsoft Research, Bangalore, India · AWS AI Labs, Amazon +4
Fine-tuning language encoding models on slow fMRI improves prediction for fast ECoG
May 19, 2026Aditya R. Vaidya, Richard J. Antonello, Alexander G. HuthCross-Modal Learning Transfer Learning
Department of Computer Science The University of Texas at Austin, USA · Zuckerman Mind Brain Behavior Institute Columbia University, USA · Departments of Neuroscience and Statistics University of California, Berkeley, USA
Efficient coding along the visual hierarchy
May 18, 2026Ananya Passi, Brian S. Robinson, Michael F. BonnerUnsupervised Learning Neural Processes
Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, USA · Applied Physics Laboratory, Johns Hopkins University, Baltimore, MD, USA
Mechanistically Interpretable Neural Encoding Reveals Fine-Grained Functional Selectivity in Human Visual Cortex
May 15, 2026Idan Daniel Grosbard, Mor Geva, Galit YovelMechanistic Interpretability Visual Neuroscience
1Sagol School of Neuroscience · 2Blavatnik School of Computer Science and AI · School of Psychological Sciences
Polar probe linearly decodes semantic structures from LLMs
May 13, 2026Pablo J. Diego-Simón, Pierre Orhan, Emmanuel Chemla +2Representation Learning Linear Probing
LSCP, ENS, PSL, EHESS, CNRS, Paris, France · Paris Brain Institute, Paris, France · Earth Species Project, Berkeley, California, USA +1
Embodied Neurocomputation: A Framework for Interfacing Biological Neural Cultures with Scaled Task-Driven Validation
May 13, 2026Johnson Zhou, Daniel Tanneberg, Forough Habibollahi +12Neuromorphic Computing Neural Encoding
Cortical Labs, Australia · Honda Research Institute Europe, Germany
Feature Visualization Recovers Known Cortical Selectivity from TRIBE v2
May 13, 2026Stuart Bladon, Brinnae BentNeural Network Interpretability Visual Neuroscience
Duke University, Durham, NC, USA.
AffectCodec: Emotion-Preserving Neural Speech Codec for Expressive Speech Modeling
May 11, 2026Jiacheng Shi, Hongfei Du, Xinyuan Song +3Audio Representation Learning Neural Audio Codecs
College of William & Mary · Emory University · George Mason University
Kelvin v1.0: A Neural Pre-Encoder for H.264: A standards-compliant learned preprocessor with -27.62% BD-VMAF on UVG
May 10, 2026Marco GrazianoLearned Image Compression Video Compression
Graziano Labs Corp., Palo Alto, CA, USA.
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
Reason to Play: Behavioral and Brain Alignment Between Frontier LRMs and Human Game Learners
May 8, 2026Botos Csaba, Sreejan Kumar, Austin Tudor David Andrews +6Game-Playing Agents Human Behavior Simulation
University of Oxford · Columbia University · New York University +2
LiVeAction: a Lightweight, Versatile, and Asymmetric Neural Codec Design for Real-time Operation
May 7, 2026Dan Jacobellis, Neeraja J. YadwadkarLearned Image Compression Rate-Distortion Optimization
University of Texas at Austin
Concepts Whisper While Syntax Shouts: Spectral Anti-Concentration and the Dual Geometry of Transformer Representations
May 2, 2026Pratyush Acharya, Nuraj Rimal, Habish DhakalTransformer Interpretability Representation Learning
Hypencoder Revisited: Reproducibility and Analysis of Non-Linear Scoring for First-Stage Retrieval
Apr 29, 2026Arne Eichholtz, Yongkang Li, Jutte Vijverberg +2Learning to Rank Hypernetworks
University of Amsterdam Amsterdam, The Netherlands
Independent-Component-Based Encoding Models of Brain Activity During Story Comprehension
Apr 27, 2026Kamya Hari, Taha Binhuraib, Jin Li +2Independent Component Analysis Neuroimaging
School of Electrical and Computer Engineering, Georgia Institute of Technology · School of Psychological and Brain Sciences, Georgia Institute of Technology · Department of Linguistics, Stanford University
PEPS: Positional Encoding Projected Sampling -- Extended
Apr 27, 2026Guillaume Perez, Janarbek Matai, Takahiro HaradaImplicit Neural Representations Positional Encoding
Advanded Micro Devices
MoDAl: Self-Supervised Neural Modality Discovery via Decorrelation for Speech Neuroprosthesis
Apr 22, 2026Yuanhao Chen, Peter ChinBrain-Computer Interfaces Neural Decoding
Dartmouth College Hanover, NH, USA
ClariCodec: Optimising Neural Speech Codes for 200bps Communication using Reinforcement Learning
Apr 16, 2026Junyi Wang, Chi Zhang, Jing Qian +4Neural Audio Codecs Speech Processing
Tsinghua University, China · Huawei Technologies Co., Ltd, China
Similarity of Neural Network Representations in Superposition
Mar 31, 2026Sunny Liu, Habon Issa, André Longon +4Feature Superposition Representational Similarity Analysis
Cold Spring Harbor Laboratory · UC San Diego · Anthropic +2
Towards Interpretable Framework for Neural Audio Codecs via Sparse Autoencoders: A Case Study on Accent Information
Mar 18, 2026Shih-Heng Wang, Tiantian Feng, Aditya Kommineni +4Neural Audio Codecs Sparse Autoencoders
University of Southern California, USA

May 31, 2026, cs.CRJ/K move · Enter open · S save

The Value of Spike Timing: A Leakage-Resistant Benchmark of SNN Design Choices for Network Intrusion Detection

Raj Patel, Shaswata Mitra, David Amebley, Taye Akinrele+2

Department of Computer Science, The University of Alabama

Data Leakage Network Intrusion Detection Spiking Neural Networks Neural Encoding

Open paper AskPDF arXiv

Spiking neural networks (SNNs) are increasingly studied for network intrusion detection, but comparative evidence on how neuron models and spike encodings affect performance remains limited. Evaluation choices can influence results when preprocessing, capture structure, or scenario information crosses the train--test boundary. We evaluate nine snnTorch neuron families with three spike encodings, yielding 27 SNN configurations across four intrusion-detection benchmarks. We screen the design space and then repeat the evaluation using train-only transforms, seed-independent partitions, and capture- or scenario-aware separation. In our study, LeakyParallel/latency ranked first in both 27-configuration evaluations. The leading configurations identified during screening also remained largely consistent under confirmation, indicating that screening preserved the ordering useful for design selection even when the measured performance changed under the stricter protocol. We then examine what latency coding contributes by holding the active spike set fixed and changing only its temporal organization. At the main T=25 operating point, mapping feature magnitude to spike time improved macro-F1 on all five confirmation protocols, while spreading the same spikes through time without that mapping could either improve or reduce performance. This shows that the effect of latency coding comes from both the information carried by spike time and how those spikes interact with the temporal dynamics of the network. Finally, sparse input encoding does not directly translate into sparse internal activity, although latency coding requires fewer fanout-weighted operations than rate coding in the evaluated models. Overall, the results show that SNN design choice, evaluation protocol, spike-timing representation, and computational activity should be examined separately when applying SNNs to static network-flow data.

Momentum

Related topics

Latest papers 62