Research map / Architectures and efficiency / Spiking networks
Increasing Liquid State Machine Performance with Edge-of-Chaos Dynamics Organized by Astrocyte-modulated Plasticity
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add astrocyte-modulated STDP to the LSM liquid | LSM 1000 neurons · MNIST | top accuracy: 95.44% -> 96.15% | Helped |
| add astrocyte-modulated STDP to the LSM liquid | LSM 1000 neurons · N-MNIST | top accuracy: 95.35% -> 96.13% | Helped |
| add astrocyte modulation to plain STDP (ablation) | LSM+STDP · MNIST | top accuracy: 90.52% -> 96.15% | Helped |
| add astrocyte modulation to plain STDP (ablation) | LSM+STDP · N-MNIST | top accuracy: 87.71% -> 96.13% | Helped |
| replace hand-tuned AP-STDP with astrocyte-modulated STDP | LSM+STDP · MNIST | top accuracy: 95.62% -> 96.15% | Helped |
| replace hand-tuned AP-STDP with astrocyte-modulated STDP | LSM+STDP · N-MNIST | top accuracy: 95.43% -> 96.13% | Helped |
| scale liquid from 1000 to 8000 neurons (ablation) | NALSM · MNIST | top accuracy: 96.15% -> 97.61% | Helped |
| scale liquid from 1000 to 8000 neurons (ablation) | NALSM · N-MNIST | top accuracy: 96.13% -> 97.51% | Helped |
| scale liquid from 1000 to 8000 neurons (ablation) | NALSM · Fashion-MNIST | top accuracy: 83.54% -> 85.84% | Helped |
| reduce neuron-astrocyte connection density to 10% (ablation) | NALSM · MNIST | mean accuracy: -0.36% | No clear effect |
| reduce neuron-astrocyte connection density to 10% (ablation) | NALSM · N-MNIST | mean accuracy: -0.14% | No clear effect |
Closest papers by meaning
- The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks. (ICLR 2024 · similarity 0.86)
- Learning the Plasticity: Plasticity-Driven Learning Framework in Spiking Neural Networks (NeurIPS 2025 · similarity 0.86)
- MA-Net: Rethinking Neural Unit in the Light of Astrocytes (AAAI 2024 · similarity 0.86)
- Neuronal Self-Adaptation Enhances Capacity and Robustness of Representation in Spiking Neural Networks (NeurIPS 2026 · similarity 0.85)
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- SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Models (AAAI 2025 · similarity 0.85)
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