Library · 07-brain-inspired-spiking-llms
Spiking LLMs
| Title | Peer | Link |
|---|---|---|
| SpikingBrain Technical Report: Spiking Brain-Inspired Large Models | ◦ preprint | arxiv.org/abs/2509.05276 |
| BriLLM: Brain-Inspired Large Language Model | ◦ preprint | arxiv.org/abs/2503.11299 |
| SpikePropamine: Differentiable Plasticity in Spiking Neural Networks | — unref | arxiv.org/abs/2106.02681 |
| Meta-SpikePropamine: Learning to Learn with Synaptic Plasticity in SNNs | ✓ peer | frontiersin.org/journals/neuroscience/artic… |
| Neuromorphic Principles in Self-Attention Hardware for Efficient Transformers | ✓ peer | nature.com/articles/s43588-025-00868-9 |
| Bridging Brains and Machines: A Unified Frontier in Neuroscience, AI, and Neuromorphic Systems | ◦ preprint | arxiv.org/abs/2507.10722 |
| SpikingBrain 2.0: Brain-Inspired Foundation Models for Efficient Long-Context / Cross-Platform Inference | ◦ preprint | arxiv.org/html/2604.22575 |
| A Brain-Inspired Gating Mechanism Unlocks Robust Computation in SNNs | ◦ preprint | arxiv.org/html/2509.03281 |
| Adaptive Spiking Neurons for Vision and Language Modeling | ◦ preprint | arxiv.org/pdf/2604.12365 |
| NEXUS: Bit-Exact ANN→SNN Equivalence via Neuromorphic Gate Circuits | ◦ preprint | arxiv.org/pdf/2601.21279 |
| Plug-and-Play Spiking Operators: Breaking the Nonlinearity Bottleneck in Spiking Transformers | ◦ preprint | arxiv.org/pdf/2605.20289 |
| Reconsidering the energy efficiency of spiking neural networks | ◦ preprint | arxiv.org/abs/2409.08290 |
| The NeuroBench framework for benchmarking neuromorphic computing | ✓ peer | nature.com/articles/s41467-025-56739-4 |
| High-performance deep SNNs with 0.3 spikes per neuron | ✓ peer | nature.com/articles/s41467-024-51110-5 |
| Temporal dendritic heterogeneity incorporated with SNNs | ✓ peer | nature.com/articles/s41467-023-44614-z |
| Spike-based dynamic computing: async sensing-computing chip ("Speck") | ✓ peer | nature.com/articles/s41467-024-47811-6 |
| Optimizing the Energy Consumption of SNNs (ANN→SNN conversion) | pmc.ncbi.nlm.nih.gov/articles/PMC7339957/ | |
| Spike-driven Transformer v3 | ◦ preprint | arxiv.org/abs/2411.16061 |
| End-to-end SNN control of a 6-DOF arm (Huebotter et al.) | ◦ preprint | arxiv.org/abs/2509.05356 |
| Astrocyte-regulated SNN CPG for legged locomotion (Han & Sengupta) | ◦ preprint | arxiv.org/abs/2312.15805 |
| Hardware-aware vs -agnostic SNN energy for space | ◦ preprint | arxiv.org/abs/2508.19654 |
| Rhythm-SNN — neural-oscillations-modulated spiking nets | — | |
| Advancing spatio-temporal processing through adaptation in SNNs | — | |
| Neural heterogeneity promotes robust learning | — | |
| LightSpikformer — tensor-network compression of spiking transformers | sciencedirect.com/science/article/abs/pii/S… | |
| Prosperity: Accelerating Spiking Neural Networks via Product Sparsity (Wei et al.) | ◦ preprint | arxiv.org/abs/2503.03379 |
| Eventprop training for efficient neuromorphic applications | ◦ preprint | arxiv.org/abs/2503.04341 |
| Neuromorphic Principles for Efficient LLMs on Intel Loihi 2 | ◦ preprint | arxiv.org/abs/2503.18002 |
| Online Continual Learning on Intel Loihi 2 via a Co-designed SNN | ◦ preprint | arxiv.org/abs/2511.01553 |
| AR-LIF: Adaptive reset leaky integrate-and-fire neuron | ◦ preprint | arxiv.org/abs/2507.20746 |
| Adaptive Surrogate Gradients for Sequential RL in SNNs | ◦ preprint | arxiv.org/abs/2510.24461 |
| Networks of spiking neurons: the third generation of neural network models | ✓ peer | doi.org/10.1016/S0893-6080(97)00011-7 |
| Optimizing the energy consumption of spiking neural networks for neuromorphic applications | ✓ peer | doi.org/10.3389/fnins.2020.00662 |
| The Sparsity Ceiling: Where Spiking Networks Can and Cannot Trade Activity for Energy | ◦ preprint | arxiv.org/abs/2607.26648 |
| Neuromodulation enhances the capability and efficiency of spiking neural networks | doi.org/10.1101/2025.07.25.666748 |