Working memory facilitates reward-modulated Hebbian learning in recurrent neural networks
This addresses the inefficiency and biological implausibility of existing learning schemes in reservoir computing, potentially benefiting neuroscience and AI research on sequence learning.
The paper tackled the problem of learning temporal sequences in recurrent neural networks by proposing a biologically plausible approach, showing that combining a reservoir network with a dynamic working memory enables reward-modulated Hebbian learning to perform as well as FORCE learning.
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.