惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

The Cloudflare Blog
U
Unit 42
F
Fortinet All Blogs
雷峰网
雷峰网
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
月光博客
月光博客
Y
Y Combinator Blog
罗磊的独立博客
V
Visual Studio Blog
大猫的无限游戏
大猫的无限游戏
J
Java Code Geeks
量子位
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
爱范儿
爱范儿
B
Blog RSS Feed
aimingoo的专栏
aimingoo的专栏
有赞技术团队
有赞技术团队
T
Tailwind CSS Blog
Microsoft Security Blog
Microsoft Security Blog
L
LangChain Blog
I
InfoQ
博客园 - 叶小钗
博客园 - 聂微东
Last Week in AI
Last Week in AI

cs.NE updates on arXiv.org

MPCS: Neuroplastic Continual Learning via Multi-Component Plasticity and Topology-Aware EWC Combining Trained Models in Reinforcement Learning Training Non-Differentiable Networks via Optimal Transport ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization Probe-Geometry Alignment: Erasing the Cross-Sequence Memorization Signature Below Chance Benchmarking local Hebbian learning rules for memory storage and prototype extraction Robust volatility updates for Hierarchical Gaussian Filtering Spiking Sequence Machines and Transformers Affinity Is Not Enough: Recovering the Free Energy Principle in Mixture-of-Experts Scalable Learning in Structured Recurrent Spiking Neural Networks without Backpropagation Geometric and dynamical analysis of attractor boundaries and storage limits in kernel Hopfield networks Attractor FCM Physical Foundation Models: Fixed hardware implementations of large-scale neural networks When Does Structure Matter in Continual Learning? Dimensionality Controls When Modularity Shapes Representational Geometry Learning to Forget: Continual Learning with Adaptive Weight Decay Causal Learning with Neural Assemblies NORACL: Neurogenesis for Oracle-free Resource-Adaptive Continual Learning Text-Utilization for Encoder-dominated Speech Recognition Models EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures EvoTSC: Evolving Feature Learning Models for Time Series Classification via Genetic Programming Analysis and Explainability of LLMs Via Evolutionary Methods Deployment-Aligned Low-Precision Neural Architecture Search for Spaceborne Edge AI SeaEvo: Advancing Algorithm Discovery with Strategy Space Evolution Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs MAEO: Multiobjective Animorphic Ensemble Optimization for Scalable Large-scale Engineering Applications Necessary and sufficient conditions for universality of Kolmogorov-Arnold networks Learn&Drop: Fast Learning of CNNs based on Layer Dropping Architecture-Induced Recoverability Bias in Differentiable Symbolic Regression Collocation-based Robust Physics Informed Neural Networks for time-dependent simulations of pollution propagation under thermal inversion conditions on Spitsbergen Structure-Guided Diffusion Model for EEG-Based Visual Cognition Reconstruction
Self-Organized Optical Pathways in Optofluidic Photonic C...
Steven Motta · 2026-03-27 · via cs.NE updates on arXiv.org

This paper reports FDTD simulations of optofluidic reconfiguration in two-dimensional silicon photonic crystal waveguides, treating structural plasticity (the creation and destruction of optical pathways) via selective fluid infiltration. Using MPB eigenmode analysis, we decouple bandgap narrowing from defect-mode weakening, showing that defect weakening dominates (2.4 times faster transmission decay than bandgap narrowing at CS_2 indices). Infiltration topology controls signal routing (L-bend selectivity S = 0.98), though modulation depth is weak (Delta varepsilon/ varepsilon_ textSi = 11 %). A phenomenological optothermal feedback model produces self-organized pathways that achieve 63 % of a hand-designed waveguide's bandgap transmission (7.6 times the heavily suppressed empty-crystal baseline). Amplitude competition between counter-propagating sources produces strong, monotonic pathway steering (DeltaCOM_x from +0.03 to +4.92 ;a), while pulsed spike-timing-dependent plasticity yields a predictable null result: the timing-sensitive cross-term is suppressed by >10^2 when pulse delays exceed the temporal pulse width. The results provide benchmarks and identify physical limits for bio-inspired reconfigurable optofluidic photonics.