A Simple Reservoir Model of Working Memory with Real Values
Neurons and Cognition
2018-06-19 v1 Machine Learning
Neural and Evolutionary Computing
Abstract
The prefrontal cortex is known to be involved in many high-level cognitive functions, in particular, working memory. Here, we study to what extent a group of randomly connected units (namely an Echo State Network, ESN) can store and maintain (as output) an arbitrary real value from a streamed input, i.e. can act as a sustained working memory unit. Furthermore, we explore to what extent such an architecture can take advantage of the stored value in order to produce non-linear computations. Comparison between different architectures (with and without feedback, with and without a working memory unit) shows that an explicit memory improves the performances.
Cite
@article{arxiv.1806.06545,
title = {A Simple Reservoir Model of Working Memory with Real Values},
author = {Anthony Strock and Nicolas Rougier and Xavier Hinaut},
journal= {arXiv preprint arXiv:1806.06545},
year = {2018}
}