Research map / Probabilistic and scientific modeling / Dynamical systems
Learning efficient task-dependent representations with synaptic plasticity
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| train longer on classification task (t=0 to 10k time units) | plasticity-trained recurrent network · Classification, stimulus discrimination | d': 0.93 -> 1.90 | Helped |
| train longer on classification task (10k to 250k time units) | plasticity-trained recurrent network · Classification, stimulus discrimination | d': 1.90 -> 1.94 | No clear effect |
| increase intrinsic noise sigma | plasticity-trained recurrent network · Estimation task | learning speed and asymptotic MSE | Hurt |
| increase intrinsic noise sigma | plasticity-trained recurrent network · Estimation task | recurrent-to-input current ratio after learning | Helped |
| tighten the input prior | plasticity-trained recurrent network · Estimation task | error for improbable stimuli | Hurt |
| shift input prior relative to the classification boundary | plasticity-trained recurrent network · Classification task | error for less probable class | Hurt |
| stimulus probability high vs low | plasticity-trained recurrent network · Estimation task | decoder bias | Mixed |
| increase intrinsic noise sigma | plasticity-trained recurrent network · Estimation task | mean energy of p(r|s) | Helped |
Closest papers by meaning
- Impression learning: Online representation learning with synaptic plasticity (NeurIPS 2021 · similarity 0.91)
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- Model Based Inference of Synaptic Plasticity Rules (NeurIPS 2024 · similarity 0.88)
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