Research map / Architectures and efficiency / Spiking networks
Biologically Plausible Sequence Learning with Spiking Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use an MPN rather than a GLM to model inter-spike intervals | Cat V1 spike-train data · Cat V1 spike-train test data | KL divergence (ISI): 0.21 ± 0.11 > 0.04 ± 0.003 | Helped |
| Increase intrinsic noise (temperature τ) (ablation) | MPN storing multiple cycles · Stored spatiotemporal cycles | memory switching frequency | Mixed |
| Train at larger temperature τ without increasing the learning rate (ablation) | MPN on neural spike-train data · Cat V1 spike-train data | test log-likelihood | Hurt |
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