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- 100% We study networks with 8 hidden units and between 104 and 442 parameters (depending on the pRNN architecture). So our networks are very small! But not quite as tiny as @marcelomattar.bsky.social - whose RNNs have < 100 parameters.
- Though, training all 128 pRNN architectures on tasks with experimental data, as in the tiny RNN paper, would be really interesting! Then, we could ask questions like: ⭐ Which architectures can best learn those tasks? ⭐ What pathways do they have? ⭐ Which best match experimental data? EtcAug 7, 2025 08:18
