Mechanisms of distributed working memory in a large-scale network of macaque neocortex.

Jorge F Mejías,Xiao-Jing Wang

doi:10.7554/elife.72136

Abstract

Neural activity underlying working memory is not a local phenomenon but distributed across multiple brain regions. To elucidate the circuit mechanism of such distributed activity, we developed an anatomically constrained computational model of large-scale macaque cortex. We found that mnemonic internal states may emerge from inter-areal reverberation, even in a regime where none of the isolated areas is capable of generating self-sustained activity. The mnemonic activity pattern along the cortical hierarchy indicates a transition in space, separating areas engaged in working memory and those which do not. A host of spatially distinct attractor states is found, potentially subserving various internal processes. The model yields testable predictions, including the idea of counterstream inhibitory bias, the role of prefrontal areas in controlling distributed attractors, and the resilience of distributed activity to lesions or inactivation. This work provides a theoretical framework for identifying large-scale brain mechanisms and computational principles of distributed cognitive processes.

Highlights

With the advances of brain connectomics and physiological recording technologies like Neuropixels (Jun et al, 2017; Stringer et al, 2019), an increasingly important challenge in Neuroscience is to investigate biological mechanisms and computational principles of cognitive functions that engage many interacting brain regions
We shall refer to the gradual preferential targeting onto inhibitory neurons by top-d own projections as the ‘counterstream inhibitory bias’ hypothesis
The investigation of cognitive functions has been traditionally restricted to operations in local brain circuits –mostly due to the limitations on available precision recording techniques to local brain regions, a problem that recent developments are starting to overcome (Jun et al, 2017; Panichello and Buschman, 2021; Siegel et al, 2015; Stringer et al, 2019)

Summary

Introduction

With the advances of brain connectomics and physiological recording technologies like Neuropixels (Jun et al, 2017; Stringer et al, 2019), an increasingly important challenge in Neuroscience is to investigate biological mechanisms and computational principles of cognitive functions that engage many interacting brain regions. A basic cognitive function recently shown to involve multiple brain areas is working memory, the brain’s ability to retain and manipulate information in the absence of external inputs. Working memory has been traditionally associated with mnemonic delay neural firing in localized brain areas, such as those in the frontal cortex (Funahashi et al, 1989; Fuster, 1973; Goldman-Rakic, 1995; Inagaki et al, 2019; Kopec et al, 2015; Romo et al, 1999) and computational models uncovered the involvement of local recurrent connections and NMDA receptors in the encoding of memory items in Mejías and Wang.

Objectives

Methods

Results

Conclusion