Research map / Probabilistic and scientific modeling / Dynamical systems
Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add intrinsic plasticity (learned per-class excitability lambda) to the network (ablation) | PPG Hebbian network · MNIST digits 0-3, 0.5% and 5% labels | classification success rate | Helped |
| vary number of labels (0.5% vs 5%) and iterations over the dataset | network with IP · MNIST digits 0-3 | classification success rate | No clear effect |
| learn class-specific intensities instead of ad hoc intensity normalization | PPG network · Shiny vs dull spheres | class separation by intensity: 620 -> 720 | Helped |
| use full MNIST instead of digits 0-3 | PPG network · MNIST | qualitative results | No clear effect |
| run neural dynamics instead of exact EM | PPG model · Artificial rectangles data | parameter recovery | No clear effect |
Closest papers by meaning
- Learning the Plasticity: Plasticity-Driven Learning Framework in Spiking Neural Networks (NeurIPS 2025 · similarity 0.89)
- Impression learning: Online representation learning with synaptic plasticity (NeurIPS 2021 · similarity 0.89)
- Learning efficient task-dependent representations with synaptic plasticity (NeurIPS 2020 · similarity 0.88)
- Model Based Inference of Synaptic Plasticity Rules (NeurIPS 2024 · similarity 0.87)
- Discovering plasticity rules that organize and maintain neural circuits (NeurIPS 2024 · similarity 0.86)
- Neuron Platonic Intrinsic Representation From Dynamics Using Contrastive Learning (ICLR 2025 · similarity 0.85)
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