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arXiv Machine Learning By Jiyi Wang, Chenxiao Yang, Yujia Zhao, Jingzhao Zhang, Lu Mi

Leveraging Dale's Principle as an Inductive Bias in Recurrent Neural Networks

Read the original on arXiv Machine Learning →

The paper introduces EISep RNN, a recurrent neural network that enforces Dale's principle—each neuron is either exclusively excitatory or inhibitory—through a learnable excitatory‑inhibitory separation. Experiments across multi‑task learning, continual learning, and reinforcement learning show that EISep yields more stable training, competitive or improved performance, and sparser, more modular connectivity compared to vanilla RNNs and prior Dale‑constrained models. The architecture also produces stable and informative latent dynamics, demonstrating that Dale's principle can serve as a biologically grounded inductive bias for RNNs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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