# Spiking Neural Network

Event-driven learning turns continuous signals into sparse events, but the triggering threshold has long been chosen empirically. This work maps signed threshold encoding onto discretized learning systems, uses the ratio of normal-condition increment standard deviation to threshold to define an information-sufficiency boundary, then picks the sparsest threshold retaining a prescribed fraction of peak discriminative information. On power-system fault classification, retaining 75% of peak information cut event density about 7.6-fold relative to that boundary while overall accuracy was 94.14%.

Event-driven learning turns continuous signals into sparse events, but the triggering threshold has long been chosen empirically. This work maps signed threshold encoding onto discretized learning systems, uses the ratio of normal-condition increment standard deviation to threshold to define an information-sufficiency boundary, then picks the sparsest threshold retaining a prescribed fraction of peak discriminative information. On power-system fault classification, retaining 75% of peak information cut event density about 7.6-fold relative to that boundary while overall accuracy was 94.14%.

HPE Labs proposed and validated SEPhIA, an optoelectronic spiking neural network architecture built on microring modulators and multi-wavelength lasers that lets many spiking neurons share one multi-wavelength light source, pushing the laser-per-neuron ratio below one. Prior photonic spiking work focused mostly on individual spiking devices or all-optical laser networks and rarely addressed optical power, crosstalk and footprint together at the architecture level. Using experimentally derived device parameters, a trainable optoelectronic spiking model reached 91.35% test accuracy on four-class spike-encoded image classification, about one percentage point behind a same-size fully connected spiking baseline at 93.65%.

HPE Labs proposed and validated SEPhIA, an optoelectronic spiking neural network architecture built on microring modulators and multi-wavelength lasers that lets many spiking neurons share one multi-wavelength light source, pushing the laser-per-neuron ratio below one. Prior photonic spiking work focused mostly on individual spiking devices or all-optical laser networks and rarely addressed optical power, crosstalk and footprint together at the architecture level. Using experimentally derived device parameters, a trainable optoelectronic spiking model reached 91.35% test accuracy on four-class spike-encoded image classification, about one percentage point behind a same-size fully connected spiking baseline at 93.65%.

Spiking networks that fuse audio and video have lacked a way to regulate information flow inside cross-modal attention according to the input. GSCANet adds a gate to a dual-stream Spikingformer backbone, placing it on the attention output just before spike firing and driving it with concatenated query and value features. On CREMA-D and RAVDESS this configuration beats the no-gate baseline by 2.15 and 1.05 percentage points; both gate position and driving signal change the outcome.

Spiking networks that fuse audio and video have lacked a way to regulate information flow inside cross-modal attention according to the input. GSCANet adds a gate to a dual-stream Spikingformer backbone, placing it on the attention output just before spike firing and driving it with concatenated query and value features. On CREMA-D and RAVDESS this configuration beats the no-gate baseline by 2.15 and 1.05 percentage points; both gate position and driving signal change the outcome.

Parkinson's disease diagnosis still rests on symptoms and expert assessment, while multimodal clinical data are heterogeneous, labels are scarce, and deep models rarely report calibrated confidence. NeuroQ-MGST encodes voice, gait, neuroimaging and clinical variables with spiking neural networks, aligns them through self-supervised contrastive and masked reconstruction objectives, reasons over a dynamically rebuilt heterogeneous graph with a hierarchical graph transformer, and adds quantum-inspired variational refinement plus Monte Carlo dropout uncertainty. On the PMMD 2.0 test set it reaches 98.7% accuracy and 0.989 AUC-ROC, and 95.8% zero-shot accuracy on an external European cohort without fine-tuning.

Parkinson's disease diagnosis still rests on symptoms and expert assessment, while multimodal clinical data are heterogeneous, labels are scarce, and deep models rarely report calibrated confidence. NeuroQ-MGST encodes voice, gait, neuroimaging and clinical variables with spiking neural networks, aligns them through self-supervised contrastive and masked reconstruction objectives, reasons over a dynamically rebuilt heterogeneous graph with a hierarchical graph transformer, and adds quantum-inspired variational refinement plus Monte Carlo dropout uncertainty. On the PMMD 2.0 test set it reaches 98.7% accuracy and 0.989 AUC-ROC, and 95.8% zero-shot accuracy on an external European cohort without fine-tuning.

A multi-scale biophysical model couples sleep homeostasis, circadian drive, a hypothalamic switch and a cortical spiking network to simulate five sleep disorders under one 72-hour photoperiod; paradoxical insomnia reaches the same total sleep time as controls yet scores lowest on restoration.

A multi-scale biophysical model couples sleep homeostasis, circadian drive, a hypothalamic switch and a cortical spiking network to simulate five sleep disorders under one 72-hour photoperiod; paradoxical insomnia reaches the same total sleep time as controls yet scores lowest on restoration.

A multi-agent large language model framework confines neural network tuning to designated functional blocks while the overall network structure stays fixed. Such key components were previously chosen by hand, and existing agent systems either optimize the entire machine learning script or build a network from a text description. With analyst, coder, and recorder roles plus a sandboxed environment and an experiment log, the framework beats its baselines on five tasks: MNIST classification accuracy rises 13 percentage points, digits 6 points, the relative error on the single-soliton generalized nonlinear Schrödinger equation drops about 2491-fold, the conservation error drops about 325-fold in the selected run, and mass drift on the Kuramoto–Sivashinsky problem drops 14-fold.

A multi-agent large language model framework confines neural network tuning to designated functional blocks while the overall network structure stays fixed. Such key components were previously chosen by hand, and existing agent systems either optimize the entire machine learning script or build a network from a text description. With analyst, coder, and recorder roles plus a sandboxed environment and an experiment log, the framework beats its baselines on five tasks: MNIST classification accuracy rises 13 percentage points, digits 6 points, the relative error on the single-soliton generalized nonlinear Schrödinger equation drops about 2491-fold, the conservation error drops about 325-fold in the selected run, and mass drift on the Kuramoto–Sivashinsky problem drops 14-fold.