  Working memory is a cognitive process that is responsible for temporarily
holding and manipulating information. Most of the empirical neuroscience
research on working memory has focused on measuring sustained activity in
prefrontal cortex (PFC) and/or parietal cortex during simple delayed-response
tasks, and most of the models of working memory have been based on neural
integrators. But working memory means much more than just holding a piece of
information online. We describe a new theory of working memory, based on a
recurrent neural circuit that we call ORGaNICs (Oscillatory Recurrent GAted
Neural Integrator Circuits). ORGaNICs are a variety of Long Short Term Memory
units (LSTMs), imported from machine learning and artificial intelligence.
ORGaNICs can be used to explain the complex dynamics of delay-period activity
in prefrontal cortex (PFC) during a working memory task. The theory is
analytically tractable so that we can characterize the dynamics, and the theory
provides a means for reading out information from the dynamically varying
responses at any point in time, in spite of the complex dynamics. ORGaNICs can
be implemented with a biophysical (electrical circuit) model of pyramidal
cells, combined with shunting inhibition via a thalamocortical loop. Although
introduced as a computational theory of working memory, ORGaNICs are also
applicable to models of sensory processing, motor preparation and motor
control. ORGaNICs offer computational advantages compared to other varieties of
LSTMs that are commonly used in AI applications. Consequently, ORGaNICs are a
framework for canonical computation in brains and machines.
