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From distributed resources to limited slots in multiple-item working memory: a spiking network model with

Ziqiang Wei¹, Xiao-Jing Wang, Da-Hui Wang

¹Department of Systems Science and National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience

|August 17, 2012

Summary

This study introduces a continuous network model for working memory, revealing how neural dynamics explain capacity limits. It reconciles discrete-slot and shared-resource models by showing how neural activity patterns can merge or fade.

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Area of Science:

Cognitive Neuroscience
Computational Neuroscience

Background:

Two models explain limited working memory: discrete-slot and shared-resource.
Neural processes underlying these models remain unclear.

Purpose of the Study:

Investigate working memory neural mechanisms using a continuous network model.
Reconcile discrete-slot and shared-resource models.

Main Methods:

Developed a continuous network model for analog feature working memory.
Analyzed recurrent circuit dynamics and persistent activity patterns (bump attractors).

Main Results:

Model exhibits shared-resource mechanisms with distributed neural encoding.
Network dynamics align with discrete-slot model via localized persistent activity.
Identified bump fading and merging as key capacity-limiting phenomena.
Demonstrated dependence on synaptic excitation and network normalization.

Conclusions:

The model integrates both discrete-slot and shared-resource perspectives.
Neural resource allocation is dynamic and invariant to set size/delay.
Proposed testable predictions for the merging phenomenon.