Self-Organized Optical Pathways in Optofluidic Photonic Crystals

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.

Federated Few-Shot Learning on Neuromorphic Hardware: An Empirical Study Across Physical Edge Nodes

Federated learning on neuromorphic hardware remains unexplored because on-chip spike-timing-dependent plasticity (STDP) produces binary weight updates rather than the floating-point gradients assumed by standard algorithms. We build a two-node federated system with BrainChip Akida AKD1000 processors and run approximately 1,580 experimental trials across seven analysis phases. Of four weight-exchange strategies tested, neuron-level concatenation (FedUnion) consistently preserves accuracy while element-wise weight averaging (FedAvg) destroys it (p = 0.002). Domain-adaptive fine-tuning of the upstream feature extractor accounts for most of the accuracy gains, confirming feature quality as the dominant factor. Scaling feature dimensionality from 64 to 256 yields 77.0% best-strategy federated accuracy (n=30, p < 0.001). Two independent asymmetries (wider features help federation more than individual learning, while binarization hurts federation more) point to a shared prototype complementarity mechanism: cross-node transfer scales with the distinctiveness of neuron prototypes.