An Indexing Theory for Working Memory Based on Fast Hebbian Plasticity.

ENeuro
Florian FiebigAnders Lansner

Abstract

Working memory (WM) is a key component of human memory and cognition. Computational models have been used to study the underlying neural mechanisms, but neglected the important role of short-term memory (STM) and long-term memory (LTM) interactions for WM. Here, we investigate these using a novel multiarea spiking neural network model of prefrontal cortex (PFC) and two parietotemporal cortical areas based on macaque data. We propose a WM indexing theory that explains how PFC could associate, maintain, and update multimodal LTM representations. Our simulations demonstrate how simultaneous, brief multimodal memory cues could build a temporary joint memory representation as an "index" in PFC by means of fast Hebbian synaptic plasticity. This index can then reactivate spontaneously and thereby also the associated LTM representations. Cueing one LTM item rapidly pattern completes the associated uncued item via PFC. The PFC-STM network updates flexibly as new stimuli arrive, thereby gradually overwriting older representations.

Citations

Sep 29, 2020·Frontiers in Neuroscience·Yu YangAnders Lansner
Jan 6, 2021·PloS One·Nareg BerberianSylvain Chartier
Feb 9, 2021·Trends in Cognitive Sciences·Andre O BeukersKenneth A Norman

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Software Mentioned

BCPNN
NEST Simulator
NEST
Git
LTMb
LTMa
STM
gnu
MPI
PrgEnv

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