Working Memory Facilitates Reward-Modulated Hebbian Learning in Recurrent Neural Networks
Abstract
Reservoir computing is a powerful tool to explain how the brain learns temporal sequences, such as movements, but existing learning schemes are either biologically implausible or too inefficient to explain animal performance. We show that a network can learn complicated sequences with a reward-modulated Hebbian learning rule if the network of reservoir neurons is combined with a second network that serves as a dynamic working memory and provides a spatio-temporal backbone signal to the reservoir. In combination with the working memory, reward-modulated Hebbian learning of the readout neurons performs as well as FORCE learning, but with the advantage of a biologically plausible interpretation of both the learning rule and the learning paradigm.
Cite
Text
Pogodin et al. "Working Memory Facilitates Reward-Modulated Hebbian Learning in Recurrent Neural Networks." NeurIPS 2019 Workshops: Neuro_AI, 2019.Markdown
[Pogodin et al. "Working Memory Facilitates Reward-Modulated Hebbian Learning in Recurrent Neural Networks." NeurIPS 2019 Workshops: Neuro_AI, 2019.](https://mlanthology.org/neuripsw/2019/pogodin2019neuripsw-working/)BibTeX
@inproceedings{pogodin2019neuripsw-working,
title = {{Working Memory Facilitates Reward-Modulated Hebbian Learning in Recurrent Neural Networks}},
author = {Pogodin, Roman and Corneil, Dane and Seeholzer, Alexander and Heng, Joseph and Gerstner, Wulfram},
booktitle = {NeurIPS 2019 Workshops: Neuro_AI},
year = {2019},
url = {https://mlanthology.org/neuripsw/2019/pogodin2019neuripsw-working/}
}